Merge remote-tracking branch 'upstream/master' into migrate-ddl-describe

Conflicts:
	sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
	sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala

Author: viirya

core/src/main/java/org/apache/spark/shuffle/sort/BypassMergeSortShuffleWriter.java

@@ -143,7 +143,7 @@ public void write(Iterator<Product2<K, V>> records) throws IOException {
     // Creating the file to write to and creating a disk writer both involve interacting with
     // the disk, and can take a long time in aggregate when we open many files, so should be
     // included in the shuffle write time.
-    writeMetrics.incShuffleWriteTime(System.nanoTime() - openStartTime);
+    writeMetrics.incWriteTime(System.nanoTime() - openStartTime);
 
     while (records.hasNext()) {
       final Product2<K, V> record = records.next();
@@ -203,7 +203,7 @@ private long[] writePartitionedFile(File outputFile) throws IOException {
       threwException = false;
     } finally {
       Closeables.close(out, threwException);
-      writeMetrics.incShuffleWriteTime(System.nanoTime() - writeStartTime);
+      writeMetrics.incWriteTime(System.nanoTime() - writeStartTime);
     }
     partitionWriters = null;
     return lengths;

core/src/main/java/org/apache/spark/shuffle/sort/ShuffleExternalSorter.java

@@ -233,8 +233,8 @@ private void writeSortedFile(boolean isLastFile) throws IOException {
       // Note that we intentionally ignore the value of `writeMetricsToUse.shuffleWriteTime()`.
       // Consistent with ExternalSorter, we do not count this IO towards shuffle write time.
       // This means that this IO time is not accounted for anywhere; SPARK-3577 will fix this.
-      writeMetrics.incShuffleRecordsWritten(writeMetricsToUse.shuffleRecordsWritten());
-      taskContext.taskMetrics().incDiskBytesSpilled(writeMetricsToUse.shuffleBytesWritten());
+      writeMetrics.incRecordsWritten(writeMetricsToUse.recordsWritten());
+      taskContext.taskMetrics().incDiskBytesSpilled(writeMetricsToUse.bytesWritten());
     }
   }
 

core/src/main/java/org/apache/spark/shuffle/sort/UnsafeShuffleWriter.java

@@ -298,8 +298,8 @@ private long[] mergeSpills(SpillInfo[] spills, File outputFile) throws IOExcepti
         // final write as bytes spilled (instead, it's accounted as shuffle write). The merge needs
         // to be counted as shuffle write, but this will lead to double-counting of the final
         // SpillInfo's bytes.
-        writeMetrics.decShuffleBytesWritten(spills[spills.length - 1].file.length());
-        writeMetrics.incShuffleBytesWritten(outputFile.length());
+        writeMetrics.decBytesWritten(spills[spills.length - 1].file.length());
+        writeMetrics.incBytesWritten(outputFile.length());
         return partitionLengths;
       }
     } catch (IOException e) {
@@ -411,7 +411,7 @@ private long[] mergeSpillsWithTransferTo(SpillInfo[] spills, File outputFile) th
             spillInputChannelPositions[i] += actualBytesTransferred;
             bytesToTransfer -= actualBytesTransferred;
           }
-          writeMetrics.incShuffleWriteTime(System.nanoTime() - writeStartTime);
+          writeMetrics.incWriteTime(System.nanoTime() - writeStartTime);
           bytesWrittenToMergedFile += partitionLengthInSpill;
           partitionLengths[partition] += partitionLengthInSpill;
         }

core/src/main/java/org/apache/spark/storage/TimeTrackingOutputStream.java

@@ -42,34 +42,34 @@ public TimeTrackingOutputStream(ShuffleWriteMetrics writeMetrics, OutputStream o
   public void write(int b) throws IOException {
     final long startTime = System.nanoTime();
     outputStream.write(b);
-    writeMetrics.incShuffleWriteTime(System.nanoTime() - startTime);
+    writeMetrics.incWriteTime(System.nanoTime() - startTime);
   }
 
   @Override
   public void write(byte[] b) throws IOException {
     final long startTime = System.nanoTime();
     outputStream.write(b);
-    writeMetrics.incShuffleWriteTime(System.nanoTime() - startTime);
+    writeMetrics.incWriteTime(System.nanoTime() - startTime);
   }
 
   @Override
   public void write(byte[] b, int off, int len) throws IOException {
     final long startTime = System.nanoTime();
     outputStream.write(b, off, len);
-    writeMetrics.incShuffleWriteTime(System.nanoTime() - startTime);
+    writeMetrics.incWriteTime(System.nanoTime() - startTime);
   }
 
   @Override
   public void flush() throws IOException {
     final long startTime = System.nanoTime();
     outputStream.flush();
-    writeMetrics.incShuffleWriteTime(System.nanoTime() - startTime);
+    writeMetrics.incWriteTime(System.nanoTime() - startTime);
   }
 
   @Override
   public void close() throws IOException {
     final long startTime = System.nanoTime();
     outputStream.close();
-    writeMetrics.incShuffleWriteTime(System.nanoTime() - startTime);
+    writeMetrics.incWriteTime(System.nanoTime() - startTime);
   }
 }

core/src/main/scala/org/apache/spark/Accumulators.scala renamed to core/src/main/scala/org/apache/spark/Accumulable.scala

@@ -20,14 +20,12 @@ package org.apache.spark
 import java.io.{ObjectInputStream, Serializable}
 
 import scala.collection.generic.Growable
-import scala.collection.Map
-import scala.collection.mutable
-import scala.ref.WeakReference
 import scala.reflect.ClassTag
 
 import org.apache.spark.serializer.JavaSerializer
 import org.apache.spark.util.Utils
 
+
 /**
  * A data type that can be accumulated, ie has an commutative and associative "add" operation,
  * but where the result type, `R`, may be different from the element type being added, `T`.
@@ -166,6 +164,7 @@ class Accumulable[R, T] private[spark] (
   override def toString: String = if (value_ == null) "null" else value_.toString
 }
 
+
 /**
  * Helper object defining how to accumulate values of a particular type. An implicit
  * AccumulableParam needs to be available when you create [[Accumulable]]s of a specific type.
@@ -201,6 +200,7 @@ trait AccumulableParam[R, T] extends Serializable {
   def zero(initialValue: R): R
 }
 
+
 private[spark] class
 GrowableAccumulableParam[R <% Growable[T] with TraversableOnce[T] with Serializable: ClassTag, T]
   extends AccumulableParam[R, T] {
@@ -224,176 +224,3 @@ GrowableAccumulableParam[R <% Growable[T] with TraversableOnce[T] with Serializa
     copy
   }
 }
-
-/**
- * A simpler value of [[Accumulable]] where the result type being accumulated is the same
- * as the types of elements being merged, i.e. variables that are only "added" to through an
- * associative operation and can therefore be efficiently supported in parallel. They can be used
- * to implement counters (as in MapReduce) or sums. Spark natively supports accumulators of numeric
- * value types, and programmers can add support for new types.
- *
- * An accumulator is created from an initial value `v` by calling [[SparkContext#accumulator]].
- * Tasks running on the cluster can then add to it using the [[Accumulable#+=]] operator.
- * However, they cannot read its value. Only the driver program can read the accumulator's value,
- * using its value method.
- *
- * The interpreter session below shows an accumulator being used to add up the elements of an array:
- *
- * {{{
- * scala> val accum = sc.accumulator(0)
- * accum: spark.Accumulator[Int] = 0
- *
- * scala> sc.parallelize(Array(1, 2, 3, 4)).foreach(x => accum += x)
- * ...
- * 10/09/29 18:41:08 INFO SparkContext: Tasks finished in 0.317106 s
- *
- * scala> accum.value
- * res2: Int = 10
- * }}}
- *
- * @param initialValue initial value of accumulator
- * @param param helper object defining how to add elements of type `T`
- * @tparam T result type
- */
-class Accumulator[T] private[spark] (
-    @transient private[spark] val initialValue: T,
-    param: AccumulatorParam[T],
-    name: Option[String],
-    internal: Boolean)
-  extends Accumulable[T, T](initialValue, param, name, internal) {
-
-  def this(initialValue: T, param: AccumulatorParam[T], name: Option[String]) = {
-    this(initialValue, param, name, false)
-  }
-
-  def this(initialValue: T, param: AccumulatorParam[T]) = {
-    this(initialValue, param, None, false)
-  }
-}
-
-/**
- * A simpler version of [[org.apache.spark.AccumulableParam]] where the only data type you can add
- * in is the same type as the accumulated value. An implicit AccumulatorParam object needs to be
- * available when you create Accumulators of a specific type.
- *
- * @tparam T type of value to accumulate
- */
-trait AccumulatorParam[T] extends AccumulableParam[T, T] {
-  def addAccumulator(t1: T, t2: T): T = {
-    addInPlace(t1, t2)
-  }
-}
-
-object AccumulatorParam {
-
-  // The following implicit objects were in SparkContext before 1.2 and users had to
-  // `import SparkContext._` to enable them. Now we move them here to make the compiler find
-  // them automatically. However, as there are duplicate codes in SparkContext for backward
-  // compatibility, please update them accordingly if you modify the following implicit objects.
-
-  implicit object DoubleAccumulatorParam extends AccumulatorParam[Double] {
-    def addInPlace(t1: Double, t2: Double): Double = t1 + t2
-    def zero(initialValue: Double): Double = 0.0
-  }
-
-  implicit object IntAccumulatorParam extends AccumulatorParam[Int] {
-    def addInPlace(t1: Int, t2: Int): Int = t1 + t2
-    def zero(initialValue: Int): Int = 0
-  }
-
-  implicit object LongAccumulatorParam extends AccumulatorParam[Long] {
-    def addInPlace(t1: Long, t2: Long): Long = t1 + t2
-    def zero(initialValue: Long): Long = 0L
-  }
-
-  implicit object FloatAccumulatorParam extends AccumulatorParam[Float] {
-    def addInPlace(t1: Float, t2: Float): Float = t1 + t2
-    def zero(initialValue: Float): Float = 0f
-  }
-
-  // TODO: Add AccumulatorParams for other types, e.g. lists and strings
-}
-
-// TODO: The multi-thread support in accumulators is kind of lame; check
-// if there's a more intuitive way of doing it right
-private[spark] object Accumulators extends Logging {
-  /**
-   * This global map holds the original accumulator objects that are created on the driver.
-   * It keeps weak references to these objects so that accumulators can be garbage-collected
-   * once the RDDs and user-code that reference them are cleaned up.
-   */
-  val originals = mutable.Map[Long, WeakReference[Accumulable[_, _]]]()
-
-  private var lastId: Long = 0
-
-  def newId(): Long = synchronized {
-    lastId += 1
-    lastId
-  }
-
-  def register(a: Accumulable[_, _]): Unit = synchronized {
-    originals(a.id) = new WeakReference[Accumulable[_, _]](a)
-  }
-
-  def remove(accId: Long) {
-    synchronized {
-      originals.remove(accId)
-    }
-  }
-
-  // Add values to the original accumulators with some given IDs
-  def add(values: Map[Long, Any]): Unit = synchronized {
-    for ((id, value) <- values) {
-      if (originals.contains(id)) {
-        // Since we are now storing weak references, we must check whether the underlying data
-        // is valid.
-        originals(id).get match {
-          case Some(accum) => accum.asInstanceOf[Accumulable[Any, Any]] ++= value
-          case None =>
-            throw new IllegalAccessError("Attempted to access garbage collected Accumulator.")
-        }
-      } else {
-        logWarning(s"Ignoring accumulator update for unknown accumulator id $id")
-      }
-    }
-  }
-
-}
-
-private[spark] object InternalAccumulator {
-  val PEAK_EXECUTION_MEMORY = "peakExecutionMemory"
-  val TEST_ACCUMULATOR = "testAccumulator"
-
-  // For testing only.
-  // This needs to be a def since we don't want to reuse the same accumulator across stages.
-  private def maybeTestAccumulator: Option[Accumulator[Long]] = {
-    if (sys.props.contains("spark.testing")) {
-      Some(new Accumulator(
-        0L, AccumulatorParam.LongAccumulatorParam, Some(TEST_ACCUMULATOR), internal = true))
-    } else {
-      None
-    }
-  }
-
-  /**
-   * Accumulators for tracking internal metrics.
-   *
-   * These accumulators are created with the stage such that all tasks in the stage will
-   * add to the same set of accumulators. We do this to report the distribution of accumulator
-   * values across all tasks within each stage.
-   */
-  def create(sc: SparkContext): Seq[Accumulator[Long]] = {
-    val internalAccumulators = Seq(
-        // Execution memory refers to the memory used by internal data structures created
-        // during shuffles, aggregations and joins. The value of this accumulator should be
-        // approximately the sum of the peak sizes across all such data structures created
-        // in this task. For SQL jobs, this only tracks all unsafe operators and ExternalSort.
-        new Accumulator(
-          0L, AccumulatorParam.LongAccumulatorParam, Some(PEAK_EXECUTION_MEMORY), internal = true)
-      ) ++ maybeTestAccumulator.toSeq
-    internalAccumulators.foreach { accumulator =>
-      sc.cleaner.foreach(_.registerAccumulatorForCleanup(accumulator))
-    }
-    internalAccumulators
-  }
-}