More work

Author: mateiz

core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala

@@ -17,9 +17,12 @@
 
 package org.apache.spark.shuffle.sort
 
+import java.io.{DataInputStream, FileInputStream}
+
 import org.apache.spark.shuffle._
 import org.apache.spark.{TaskContext, ShuffleDependency}
 import org.apache.spark.shuffle.hash.HashShuffleReader
+import org.apache.spark.storage.{DiskBlockManager, FileSegment, ShuffleBlockId}
 
 private[spark] class SortShuffleManager extends ShuffleManager {
   /**
@@ -57,4 +60,21 @@ private[spark] class SortShuffleManager extends ShuffleManager {
 
   /** Shut down this ShuffleManager. */
   override def stop(): Unit = {}
+
+  /** Get the location of a block in a map output file. Uses the index file we create for it. */
+  def getBlockLocation(blockId: ShuffleBlockId, diskManager: DiskBlockManager): FileSegment = {
+    // The block is actually going to be a range of a single map output file for this map,
+    // so
+    val realId = ShuffleBlockId(blockId.shuffleId, blockId.mapId, 0)
+    val indexFile = diskManager.getFile(realId.name + ".index")
+    val in = new DataInputStream(new FileInputStream(indexFile))
+    try {
+      in.skip(blockId.reduceId * 8)
+      val offset = in.readLong()
+      val nextOffset = in.readLong()
+      new FileSegment(diskManager.getFile(realId), offset, nextOffset - offset)
+    } finally {
+      in.close()
+    }
+  }
 }

core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala

@@ -18,10 +18,14 @@
 package org.apache.spark.shuffle.sort
 
 import org.apache.spark.shuffle.{ShuffleWriter, BaseShuffleHandle}
-import org.apache.spark.{SparkEnv, Logging, TaskContext}
+import org.apache.spark.{MapOutputTracker, SparkEnv, Logging, TaskContext}
 import org.apache.spark.scheduler.MapStatus
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.util.collection.ExternalSorter
+import org.apache.spark.storage.ShuffleBlockId
+import java.util.concurrent.atomic.AtomicInteger
+import org.apache.spark.executor.ShuffleWriteMetrics
+import java.io.{BufferedOutputStream, FileOutputStream, DataOutputStream}
 
 private[spark] class SortShuffleWriter[K, V, C](
     handle: BaseShuffleHandle[K, V, C],
@@ -30,17 +34,24 @@ private[spark] class SortShuffleWriter[K, V, C](
   extends ShuffleWriter[K, V] with Logging {
 
   private val dep = handle.dependency
-  private val numOutputPartitions = dep.partitioner.numPartitions
+  private val numPartitions = dep.partitioner.numPartitions
   private val metrics = context.taskMetrics
 
   private val blockManager = SparkEnv.get.blockManager
+  private val shuffleBlockManager = blockManager.shuffleBlockManager
   private val diskBlockManager = blockManager.diskBlockManager
   private val ser = Serializer.getSerializer(dep.serializer.getOrElse(null))
 
+  private val conf = SparkEnv.get.conf
+  private val fileBufferSize = conf.getInt("spark.shuffle.file.buffer.kb", 100) * 1024
+
+  private var sorter: ExternalSorter[K, V, _] = null
+
+  private var stopping = false
+  private var mapStatus: MapStatus = null
+
   /** Write a bunch of records to this task's output */
   override def write(records: Iterator[_ <: Product2[K, V]]): Unit = {
-    var sorter: ExternalSorter[K, V, _] = null
-
     val partitions: Iterator[(Int, Iterator[Product2[K, _]])] = {
       if (dep.mapSideCombine) {
         if (!dep.aggregator.isDefined) {
@@ -58,13 +69,81 @@ private[spark] class SortShuffleWriter[K, V, C](
       }
     }
 
+    // Create a single shuffle file with reduce ID 0 that we'll write all results to. We'll later
+    // serve different ranges of this file using an index file that we create at the end.
+    val blockId = ShuffleBlockId(dep.shuffleId, mapId, 0)
+    val shuffleFile = blockManager.diskBlockManager.getFile(blockId)
+
+    // Track location of each range in the output file
+    val offsets = new Array[Long](numPartitions + 1)
+    val lengths = new Array[Long](numPartitions)
+
+    // Statistics
+    var totalBytes = 0L
+    var totalTime = 0L
+
     for ((id, elements) <- partitions) {
+      if (elements.hasNext) {
+        val writer = blockManager.getDiskWriter(blockId, shuffleFile, ser, fileBufferSize)
+        for (elem <- elements) {
+          writer.write(elem)
+        }
+        writer.commit()
+        writer.close()
+        val segment = writer.fileSegment()
+        offsets(id + 1) = segment.offset + segment.length
+        lengths(id) = segment.length
+        totalTime += writer.timeWriting()
+        totalBytes += segment.length
+      } else {
+        // Don't create a new writer to avoid writing any headers and things like that
+        offsets(id + 1) = offsets(id)
+      }
+    }
+
+    val shuffleMetrics = new ShuffleWriteMetrics
+    shuffleMetrics.shuffleBytesWritten = totalBytes
+    shuffleMetrics.shuffleWriteTime = totalTime
+    context.taskMetrics.shuffleWriteMetrics = Some(shuffleMetrics)
+
+    // Write an index file with the offsets of each block, plus a final offset at the end for the
+    // end of the output file. This will be used by SortShuffleManager.getBlockLocation to figure
+    // out where each block begins and ends.
 
+    val diskBlockManager = blockManager.diskBlockManager
+    val indexFile = diskBlockManager.getFile(blockId.name + ".index")
+    val out = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(indexFile)))
+    try {
+      var i = 0
+      while (i < numPartitions + 1) {
+        out.writeLong(offsets(i))
+        i += 1
+      }
+    } finally {
+      out.close()
     }
 
-    ???
+    mapStatus = new MapStatus(blockManager.blockManagerId,
+      lengths.map(MapOutputTracker.compressSize))
+
+    // TODO: keep track of our file in a way that can be cleaned up later
   }
 
   /** Close this writer, passing along whether the map completed */
-  override def stop(success: Boolean): Option[MapStatus] = ???
+  override def stop(success: Boolean): Option[MapStatus] = {
+    try {
+      if (stopping) {
+        return None
+      }
+      stopping = true
+      if (success) {
+        return Option(mapStatus)
+      } else {
+        // TODO: clean up our file
+        return None
+      }
+    } finally {
+      // TODO: sorter.stop()
+    }
+  }
 }

core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala

@@ -21,10 +21,11 @@ import java.io.File
 import java.text.SimpleDateFormat
 import java.util.{Date, Random, UUID}
 
-import org.apache.spark.Logging
+import org.apache.spark.{SparkEnv, Logging}
 import org.apache.spark.executor.ExecutorExitCode
 import org.apache.spark.network.netty.{PathResolver, ShuffleSender}
 import org.apache.spark.util.Utils
+import org.apache.spark.shuffle.sort.SortShuffleManager
 
 /**
  * Creates and maintains the logical mapping between logical blocks and physical on-disk
@@ -54,12 +55,15 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD
   addShutdownHook()
 
   /**
-   * Returns the physical file segment in which the given BlockId is located.
-   * If the BlockId has been mapped to a specific FileSegment, that will be returned.
-   * Otherwise, we assume the Block is mapped to a whole file identified by the BlockId directly.
+   * Returns the physical file segment in which the given BlockId is located. If the BlockId has
+   * been mapped to a specific FileSegment by the shuffle layer, that will be returned.
+   * Otherwise, we assume the Block is mapped to the whole file identified by the BlockId.
    */
   def getBlockLocation(blockId: BlockId): FileSegment = {
-    if (blockId.isShuffle && shuffleManager.consolidateShuffleFiles) {
+    if (blockId.isShuffle && SparkEnv.get.shuffleManager.isInstanceOf[SortShuffleManager]) {
+      val sortShuffleManager = SparkEnv.get.shuffleManager.asInstanceOf[SortShuffleManager]
+      sortShuffleManager.getBlockLocation(blockId.asInstanceOf[ShuffleBlockId], this)
+    } else if (blockId.isShuffle && shuffleManager.consolidateShuffleFiles) {
       shuffleManager.getBlockLocation(blockId.asInstanceOf[ShuffleBlockId])
     } else {
       val file = getFile(blockId.name)

core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala

@@ -18,6 +18,7 @@
 package org.apache.spark.util.collection
 
 import java.io._
+import java.util.Comparator
 
 import scala.collection.mutable.ArrayBuffer
 
@@ -88,6 +89,13 @@ private[spark] class ExternalSorter[K, V, C](
     (Runtime.getRuntime.maxMemory * memoryFraction * safetyFraction).toLong
   }
 
+  // For now, just compare them by partition; later we can compare by key as well
+  private val comparator = new Comparator[((Int, K), C)] {
+    override def compare(a: ((Int, K), C), b: ((Int, K), C)): Int = {
+      a._1._1 - b._1._1
+    }
+  }
+
   // Information about a spilled file. Includes sizes in bytes of "batches" written by the
   // serializer as we periodically reset its stream, as well as number of elements in each
   // partition, used to efficiently keep track of partitions when merging.
@@ -192,7 +200,7 @@ private[spark] class ExternalSorter[K, V, C](
     }
 
     try {
-      val it = collection.iterator // TODO: destructiveSortedIterator(comparator)
+      val it = collection.destructiveSortedIterator(comparator)
       while (it.hasNext) {
         val elem = it.next()
         val partitionId = elem._1._1
@@ -232,11 +240,22 @@ private[spark] class ExternalSorter[K, V, C](
    * inside each partition. This can be used to either write out a new file or return data to
    * the user.
    */
-  def merge(spills: Seq[SpilledFile]): Iterator[(Int, Iterator[Product2[K, C]])] = {
+  def merge(spills: Seq[SpilledFile], inMemory: Iterator[((Int, K), C)])
+      : Iterator[(Int, Iterator[Product2[K, C]])] = {
     // TODO: merge intermediate results if they are sorted by the comparator
     val readers = spills.map(new SpillReader(_))
+    val inMemBuffered = inMemory.buffered
     (0 until numPartitions).iterator.map { p =>
-      (p, readers.iterator.flatMap(_.readNextPartition()))
+      val inMemIterator = new Iterator[(K, C)] {
+        override def hasNext: Boolean = {
+          inMemBuffered.hasNext && inMemBuffered.head._1._1 == p
+        }
+        override def next(): (K, C) = {
+          val elem = inMemBuffered.next()
+          (elem._1._2, elem._2)
+        }
+      }
+      (p, readers.iterator.flatMap(_.readNextPartition()) ++ inMemIterator)
     }
   }
 
@@ -301,6 +320,11 @@ private[spark] class ExternalSorter[K, V, C](
         }
         val k = deserStream.readObject().asInstanceOf[K]
         val c = deserStream.readObject().asInstanceOf[C]
+        if (partitionId == numPartitions - 1 &&
+            indexInPartition == spill.elementsPerPartition(partitionId) - 1) {
+          finished = true
+          deserStream.close()
+        }
         (k, c)
       } catch {
         case e: EOFException =>
@@ -319,6 +343,9 @@ private[spark] class ExternalSorter[K, V, C](
       override def hasNext: Boolean = {
         if (nextItem == null) {
           nextItem = readNextItem()
+          if (nextItem == null) {
+            return false
+          }
         }
         // Check that we're still in the right partition; will be numPartitions at EOF
         partitionId == myPartition
@@ -328,7 +355,9 @@ private[spark] class ExternalSorter[K, V, C](
         if (!hasNext) {
           throw new NoSuchElementException
         }
-        nextItem
+        val item = nextItem
+        nextItem = null
+        item
       }
     }
   }
@@ -337,11 +366,16 @@ private[spark] class ExternalSorter[K, V, C](
    * Return an iterator over all the data written to this object, grouped by partition. For each
    * partition we then have an iterator over its contents, and these are expected to be accessed
    * in order (you can't "skip ahead" to one partition without reading the previous one).
+   * Guaranteed to return a key-value pair for each partition, in order of partition ID.
    *
    * For now, we just merge all the spilled files in once pass, but this can be modified to
    * support hierarchical merging.
    */
-  def partitionedIterator: Iterator[(Int, Iterator[Product2[K, C]])] = merge(spills)
+  def partitionedIterator: Iterator[(Int, Iterator[Product2[K, C]])] = {
+    val usingMap = aggregator.isDefined
+    val collection: SizeTrackingCollection[((Int, K), C)] = if (usingMap) map else buffer
+    merge(spills, collection.destructiveSortedIterator(comparator))
+  }
 
   /**
    * Return an iterator over all the data written to this object.

core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala

@@ -369,10 +369,3 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
   }
 
 }
-
-/**
- * A dummy class that always returns the same hash code, to easily test hash collisions
- */
-case class FixedHashObject(v: Int, h: Int) extends Serializable {
-  override def hashCode(): Int = h
-}

core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala

@@ -0,0 +1,80 @@
+/*
+ * 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.util.collection
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.{SparkContext, SparkConf, LocalSparkContext}
+import org.apache.spark.SparkContext._
+import scala.collection.mutable.ArrayBuffer
+
+class ExternalSorterSuite extends FunSuite with LocalSparkContext {
+
+  test("spilling in local cluster") {
+    val conf = new SparkConf(true)  // Load defaults, otherwise SPARK_HOME is not found
+    conf.set("spark.shuffle.memoryFraction", "0.001")
+    conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager")
+    sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
+
+    // reduceByKey - should spill ~8 times
+    val rddA = sc.parallelize(0 until 100000).map(i => (i/2, i))
+    val resultA = rddA.reduceByKey(math.max).collect()
+    assert(resultA.length == 50000)
+    resultA.foreach { case(k, v) =>
+      k match {
+        case 0 => assert(v == 1)
+        case 25000 => assert(v == 50001)
+        case 49999 => assert(v == 99999)
+        case _ =>
+      }
+    }
+
+    // groupByKey - should spill ~17 times
+    val rddB = sc.parallelize(0 until 100000).map(i => (i/4, i))
+    val resultB = rddB.groupByKey().collect()
+    assert(resultB.length == 25000)
+    resultB.foreach { case(i, seq) =>
+      i match {
+        case 0 => assert(seq.toSet == Set[Int](0, 1, 2, 3))
+        case 12500 => assert(seq.toSet == Set[Int](50000, 50001, 50002, 50003))
+        case 24999 => assert(seq.toSet == Set[Int](99996, 99997, 99998, 99999))
+        case _ =>
+      }
+    }
+
+    // cogroup - should spill ~7 times
+    val rddC1 = sc.parallelize(0 until 10000).map(i => (i, i))
+    val rddC2 = sc.parallelize(0 until 10000).map(i => (i%1000, i))
+    val resultC = rddC1.cogroup(rddC2).collect()
+    assert(resultC.length == 10000)
+    resultC.foreach { case(i, (seq1, seq2)) =>
+      i match {
+        case 0 =>
+          assert(seq1.toSet == Set[Int](0))
+          assert(seq2.toSet == Set[Int](0, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000))
+        case 5000 =>
+          assert(seq1.toSet == Set[Int](5000))
+          assert(seq2.toSet == Set[Int]())
+        case 9999 =>
+          assert(seq1.toSet == Set[Int](9999))
+          assert(seq2.toSet == Set[Int]())
+        case _ =>
+      }
+    }
+  }
+}