Merge branch 'master' of github.com:apache/spark into fix-drop-events

Author: andrewor14

core/pom.xml

@@ -192,8 +192,8 @@
     </dependency>
     <dependency>
       <groupId>org.tachyonproject</groupId>
-      <artifactId>tachyon</artifactId>
-      <version>0.4.1-thrift</version>
+      <artifactId>tachyon-client</artifactId>
+      <version>0.5.0</version>
       <exclusions>
         <exclusion>
           <groupId>org.apache.hadoop</groupId>

core/src/main/scala/org/apache/spark/Dependency.scala

@@ -34,8 +34,8 @@ abstract class Dependency[T] extends Serializable {
 
 /**
  * :: DeveloperApi ::
- * Base class for dependencies where each partition of the parent RDD is used by at most one
- * partition of the child RDD.  Narrow dependencies allow for pipelined execution.
+ * Base class for dependencies where each partition of the child RDD depends on a small number
+ * of partitions of the parent RDD. Narrow dependencies allow for pipelined execution.
  */
 @DeveloperApi
 abstract class NarrowDependency[T](_rdd: RDD[T]) extends Dependency[T] {

core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala

@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+import akka.actor.Actor
+import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.storage.BlockManagerId
+import org.apache.spark.scheduler.TaskScheduler
+
+/**
+ * A heartbeat from executors to the driver. This is a shared message used by several internal
+ * components to convey liveness or execution information for in-progress tasks.
+ */
+private[spark] case class Heartbeat(
+    executorId: String,
+    taskMetrics: Array[(Long, TaskMetrics)], // taskId -> TaskMetrics
+    blockManagerId: BlockManagerId)
+
+private[spark] case class HeartbeatResponse(reregisterBlockManager: Boolean)
+
+/**
+ * Lives in the driver to receive heartbeats from executors..
+ */
+private[spark] class HeartbeatReceiver(scheduler: TaskScheduler) extends Actor {
+  override def receive = {
+    case Heartbeat(executorId, taskMetrics, blockManagerId) =>
+      val response = HeartbeatResponse(
+        !scheduler.executorHeartbeatReceived(executorId, taskMetrics, blockManagerId))
+      sender ! response
+  }
+}

core/src/main/scala/org/apache/spark/Logging.scala

@@ -45,10 +45,7 @@ trait Logging {
       initializeIfNecessary()
       var className = this.getClass.getName
       // Ignore trailing $'s in the class names for Scala objects
-      if (className.endsWith("$")) {
-        className = className.substring(0, className.length - 1)
-      }
-      log_ = LoggerFactory.getLogger(className)
+      log_ = LoggerFactory.getLogger(className.stripSuffix("$"))
     }
     log_
   }
@@ -110,23 +107,27 @@ trait Logging {
   }
 
   private def initializeLogging() {
-    // If Log4j is being used, but is not initialized, load a default properties file
-    val binder = StaticLoggerBinder.getSingleton
-    val usingLog4j = binder.getLoggerFactoryClassStr.endsWith("Log4jLoggerFactory")
-    val log4jInitialized = LogManager.getRootLogger.getAllAppenders.hasMoreElements
-    if (!log4jInitialized && usingLog4j) {
+    // Don't use a logger in here, as this is itself occurring during initialization of a logger
+    // If Log4j 1.2 is being used, but is not initialized, load a default properties file
+    val binderClass = StaticLoggerBinder.getSingleton.getLoggerFactoryClassStr
+    // This distinguishes the log4j 1.2 binding, currently
+    // org.slf4j.impl.Log4jLoggerFactory, from the log4j 2.0 binding, currently
+    // org.apache.logging.slf4j.Log4jLoggerFactory
+    val usingLog4j12 = "org.slf4j.impl.Log4jLoggerFactory".equals(binderClass)
+    val log4j12Initialized = LogManager.getRootLogger.getAllAppenders.hasMoreElements
+    if (!log4j12Initialized && usingLog4j12) {
       val defaultLogProps = "org/apache/spark/log4j-defaults.properties"
       Option(Utils.getSparkClassLoader.getResource(defaultLogProps)) match {
         case Some(url) =>
           PropertyConfigurator.configure(url)
-          log.info(s"Using Spark's default log4j profile: $defaultLogProps")
+          System.err.println(s"Using Spark's default log4j profile: $defaultLogProps")
         case None =>
           System.err.println(s"Spark was unable to load $defaultLogProps")
       }
     }
     Logging.initialized = true
 
-    // Force a call into slf4j to initialize it. Avoids this happening from mutliple threads
+    // Force a call into slf4j to initialize it. Avoids this happening from multiple threads
     // and triggering this: http://mailman.qos.ch/pipermail/slf4j-dev/2010-April/002956.html
     log
   }

core/src/main/scala/org/apache/spark/SparkContext.scala

@@ -36,6 +36,7 @@ import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf, Sequence
 import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat, Job => NewHadoopJob}
 import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat}
 import org.apache.mesos.MesosNativeLibrary
+import akka.actor.Props
 
 import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.broadcast.Broadcast
@@ -307,6 +308,8 @@ class SparkContext(config: SparkConf) extends Logging {
 
   // Create and start the scheduler
   private[spark] var taskScheduler = SparkContext.createTaskScheduler(this, master)
+  private val heartbeatReceiver = env.actorSystem.actorOf(
+    Props(new HeartbeatReceiver(taskScheduler)), "HeartbeatReceiver")
   @volatile private[spark] var dagScheduler: DAGScheduler = _
   try {
     dagScheduler = new DAGScheduler(this)
@@ -455,7 +458,7 @@ class SparkContext(config: SparkConf) extends Logging {
   /** Distribute a local Scala collection to form an RDD, with one or more
     * location preferences (hostnames of Spark nodes) for each object.
     * Create a new partition for each collection item. */
-   def makeRDD[T: ClassTag](seq: Seq[(T, Seq[String])]): RDD[T] = {
+  def makeRDD[T: ClassTag](seq: Seq[(T, Seq[String])]): RDD[T] = {
     val indexToPrefs = seq.zipWithIndex.map(t => (t._2, t._1._2)).toMap
     new ParallelCollectionRDD[T](this, seq.map(_._1), seq.size, indexToPrefs)
   }
@@ -992,7 +995,9 @@ class SparkContext(config: SparkConf) extends Logging {
     val dagSchedulerCopy = dagScheduler
     dagScheduler = null
     if (dagSchedulerCopy != null) {
+      env.metricsSystem.report()
       metadataCleaner.cancel()
+      env.actorSystem.stop(heartbeatReceiver)
       cleaner.foreach(_.stop())
       dagSchedulerCopy.stop()
       taskScheduler = null
@@ -1453,9 +1458,9 @@ object SparkContext extends Logging {
   /** Creates a task scheduler based on a given master URL. Extracted for testing. */
   private def createTaskScheduler(sc: SparkContext, master: String): TaskScheduler = {
     // Regular expression used for local[N] and local[*] master formats
-    val LOCAL_N_REGEX = """local\[([0-9\*]+)\]""".r
+    val LOCAL_N_REGEX = """local\[([0-9]+|\*)\]""".r
     // Regular expression for local[N, maxRetries], used in tests with failing tasks
-    val LOCAL_N_FAILURES_REGEX = """local\[([0-9]+)\s*,\s*([0-9]+)\]""".r
+    val LOCAL_N_FAILURES_REGEX = """local\[([0-9]+|\*)\s*,\s*([0-9]+)\]""".r
     // Regular expression for simulating a Spark cluster of [N, cores, memory] locally
     val LOCAL_CLUSTER_REGEX = """local-cluster\[\s*([0-9]+)\s*,\s*([0-9]+)\s*,\s*([0-9]+)\s*]""".r
     // Regular expression for connecting to Spark deploy clusters
@@ -1485,8 +1490,12 @@ object SparkContext extends Logging {
         scheduler
 
       case LOCAL_N_FAILURES_REGEX(threads, maxFailures) =>
+        def localCpuCount = Runtime.getRuntime.availableProcessors()
+        // local[*, M] means the number of cores on the computer with M failures
+        // local[N, M] means exactly N threads with M failures
+        val threadCount = if (threads == "*") localCpuCount else threads.toInt
         val scheduler = new TaskSchedulerImpl(sc, maxFailures.toInt, isLocal = true)
-        val backend = new LocalBackend(scheduler, threads.toInt)
+        val backend = new LocalBackend(scheduler, threadCount)
         scheduler.initialize(backend)
         scheduler
 

core/src/main/scala/org/apache/spark/SparkEnv.scala

@@ -193,13 +193,7 @@ object SparkEnv extends Logging {
         logInfo("Registering " + name)
         actorSystem.actorOf(Props(newActor), name = name)
       } else {
-        val driverHost: String = conf.get("spark.driver.host", "localhost")
-        val driverPort: Int = conf.getInt("spark.driver.port", 7077)
-        Utils.checkHost(driverHost, "Expected hostname")
-        val url = s"akka.tcp://spark@$driverHost:$driverPort/user/$name"
-        val timeout = AkkaUtils.lookupTimeout(conf)
-        logInfo(s"Connecting to $name: $url")
-        Await.result(actorSystem.actorSelection(url).resolveOne(timeout), timeout)
+        AkkaUtils.makeDriverRef(name, conf, actorSystem)
       }
     }
 

core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala

@@ -783,6 +783,17 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
     sortByKey(comp, ascending)
   }
 
+  /**
+   * Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
+   * `collect` or `save` on the resulting RDD will return or output an ordered list of records
+   * (in the `save` case, they will be written to multiple `part-X` files in the filesystem, in
+   * order of the keys).
+   */
+  def sortByKey(ascending: Boolean, numPartitions: Int): JavaPairRDD[K, V] = {
+    val comp = com.google.common.collect.Ordering.natural().asInstanceOf[Comparator[K]]
+    sortByKey(comp, ascending, numPartitions)
+  }
+
   /**
    * Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
    * `collect` or `save` on the resulting RDD will return or output an ordered list of records

core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala

@@ -731,19 +731,30 @@ private class PythonAccumulatorParam(@transient serverHost: String, serverPort:
 
   val bufferSize = SparkEnv.get.conf.getInt("spark.buffer.size", 65536)
 
+  /** 
+   * We try to reuse a single Socket to transfer accumulator updates, as they are all added
+   * by the DAGScheduler's single-threaded actor anyway.
+   */ 
+  @transient var socket: Socket = _
+
+  def openSocket(): Socket = synchronized {
+    if (socket == null || socket.isClosed) {
+      socket = new Socket(serverHost, serverPort)
+    }
+    socket
+  }
+
   override def zero(value: JList[Array[Byte]]): JList[Array[Byte]] = new JArrayList
 
   override def addInPlace(val1: JList[Array[Byte]], val2: JList[Array[Byte]])
-      : JList[Array[Byte]] = {
+      : JList[Array[Byte]] = synchronized {
     if (serverHost == null) {
       // This happens on the worker node, where we just want to remember all the updates
       val1.addAll(val2)
       val1
     } else {
       // This happens on the master, where we pass the updates to Python through a socket
-      val socket = new Socket(serverHost, serverPort)
-      // SPARK-2282: Immediately reuse closed sockets because we create one per task.
-      socket.setReuseAddress(true)
+      val socket = openSocket()
       val in = socket.getInputStream
       val out = new DataOutputStream(new BufferedOutputStream(socket.getOutputStream, bufferSize))
       out.writeInt(val2.size)
@@ -757,7 +768,6 @@ private class PythonAccumulatorParam(@transient serverHost: String, serverPort:
       if (byteRead == -1) {
         throw new SparkException("EOF reached before Python server acknowledged")
       }
-      socket.close()
       null
     }
   }

core/src/main/scala/org/apache/spark/deploy/master/Master.scala

@@ -154,6 +154,8 @@ private[spark] class Master(
   }
 
   override def postStop() {
+    masterMetricsSystem.report()
+    applicationMetricsSystem.report()
     // prevent the CompleteRecovery message sending to restarted master
     if (recoveryCompletionTask != null) {
       recoveryCompletionTask.cancel()

core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala

@@ -357,6 +357,7 @@ private[spark] class Worker(
   }
 
   override def postStop() {
+    metricsSystem.report()
     registrationRetryTimer.foreach(_.cancel())
     executors.values.foreach(_.kill())
     drivers.values.foreach(_.kill())