Spark 源碼分析之ShuffleMapTask內存數據Spill和合並

Spark 源碼分析之ShuffleMapTask內存數據Spill和合並

更多資源分享

• SPARK 源碼分析技術分享(視頻彙總套裝視頻): https://www.bilibili.com/video/av37442139/
• github: https://github.com/opensourceteams/spark-scala-maven
• csdn(彙總視頻在線看): https://blog.csdn.net/thinktothings/article/details/84726769

前置條件

• Hadoop版本: Hadoop 2.6.0-cdh5.15.0
• Spark版本: SPARK 1.6.0-cdh5.15.0
• JDK.1.8.0_191
• scala2.10.7

技能標籤

• Spark ShuffleMapTask 內存中的數據Spill到臨時文件
• 臨時文件中的數據是如何定入的，如何按partition升序排序，再按Key升序排序寫入(key,value)數據
• 每一個臨時文件，都存入對應的每一個分區有多少個(key,value)對，有多少次流提交數組，數組中保留每次流的大小
• 如何把臨時文件合成一個文件
• 如何把內存中的數據和臨時文件，進行分區，按key,排序後，再寫入合併文件中

內存中數據Spill到磁盤

• ShuffleMapTask進行當前分區的數據讀取(此時讀的是HDFS的當前分區,注意還有一個reduce分區，也就是ShuffleMapTask輸出文件是已經按Reduce分區處理好的)
• SparkEnv指定默認的SortShuffleManager,getWriter()中匹配BaseShuffleHandle對象，返回SortShuffleWriter對象
• SortShuffleWriter，用的是ExternalSorter(外部排序對象進行排序處理),會把rdd.iterator(partition, context)的數據經過iterator插入到ExternalSorter中PartitionedAppendOnlyMap對象中作爲內存中的map對象數據,每插入一條(key,value)的數據後，會對當前的內存中的集合進行判斷，若是知足溢出文件的條件，就會把內存中的數據寫入到SpillFile文件中
• 滿中溢出文件的條件是，每插入32條數據，而且，當前集合中的數據估值大於等於5m時，進行一次判斷，會經過算法驗證對內存的影響，肯定是否能夠溢出內存中的數據到文件，若是知足就把當前內存中的全部數據寫到磁盤spillFile文件中
• SpillFile調用org.apache.spark.util.collection.ExternalSorter.SpillableIterator.spill()方法處理
• WritablePartitionedIterator迭代對象對內存中的數據進行迭代,DiskBlockObjectWriter對象寫入磁盤，寫入的數據格式爲(key,value),不帶partition的
• ExternalSorter.spillMemoryIteratorToDisk()這個方法將內存數據迭代對象WritablePartitionedIterator寫入到一個臨時文件，SpillFile臨時文件用DiskBlockObjectWriter對象來寫入數據
• 臨時文件的格式temp_local_+UUID
• 遍歷內存中的數據寫入到臨時文件，會記錄每一個臨時文件中每一個分區的(key,value)各有多少個，elementsPerPartition(partitionId) += 1 若是說數據很大的話，會每默認每10000條數據進行Flush()一次數據到文件中，會記錄每一次Flush的數據大小batchSizes入到ArrayBuffer中保存
• 而且在數據寫入前，會進行排序，先按key的hash分區，先按partition的升序排序，再按key的升序排序，這樣來寫入文件中，以保證讀取臨時文件時能夠分隔開每一個臨時文件的每一個分區的數據,對於一個臨時文件中一個分區的數據量比較大的話，會按流一批10000個(key,value)進行讀取，讀取的大小訊出在batchSizes數據中，就樣讀取的時候就很是方便了

內存數據Spill和合並

• 把數據insertAll()到ExternalSorter中，完成後，此時若是數據大的話，會進行溢出到臨時文件的操做，數據寫到臨時文件後
• 把當前內存中的數據和臨時文件中的數據進行合併數據文件,合併後的文件只包含(key,value)，而且是按partition升序排序，而後按key升序排序，輸出文件名稱：ShuffleDataBlockId(shuffleId, mapId, NOOP_REDUCE_ID) + UUID 即:"shuffle_" + shuffleId + "" + mapId + "" + reduceId + ".data" + UUID,reduceId爲默認值0
• 還會有一份索引文件： "shuffle_" + shuffleId + "" + mapId + "" + reduceId + ".index" + "." +UUID,索引文件依次存儲每一個partition的位置偏移量
• 數據文件的寫入分兩種狀況，一種是直接內存寫入，沒有溢出臨時文件到磁盤中，這種是直接在內存中操做的（數據量相對小些），另外單獨分析
• 一種是有磁盤溢出文件的，這種狀況是本文重點分析的狀況
• ExternalSorter.partitionedIterator()方法能夠處理全部磁盤中的臨時文件和內存中的文件，返回一個可迭代的對象，裏邊放的元素爲reduce用到的(partition,Iterator(key,value)),迭代器中的數據是按key升序排序的
• 具體是經過ExternalSorter.mergeWithAggregation(),遍歷每個臨時文件中當前partition的數據和內存中當前partition的數據，注意，臨時文件數據讀取時是按partition爲0開始依次遍歷的

源碼分析(內存中數據Spill到磁盤)

ShuffleMapTask

• 調用ShuffleMapTask.runTask()方法處理當前HDFS分區數據

• 調用SparkEnv.get.shuffleManager獲得SortShuffleManager

• SortShuffleManager.getWriter()獲得SortShuffleWriter

• 調用SortShuffleWriter.write()方法

• SparkEnv.create()

val shortShuffleMgrNames = Map(
      "hash" -> "org.apache.spark.shuffle.hash.HashShuffleManager",
      "sort" -> "org.apache.spark.shuffle.sort.SortShuffleManager",
      "tungsten-sort" -> "org.apache.spark.shuffle.sort.SortShuffleManager")
    val shuffleMgrName = conf.get("spark.shuffle.manager", "sort")
    val shuffleMgrClass = shortShuffleMgrNames.getOrElse(shuffleMgrName.toLowerCase, shuffleMgrName)
    val shuffleManager = instantiateClass[ShuffleManager](shuffleMgrClass)

override def runTask(context: TaskContext): MapStatus = {
    // Deserialize the RDD using the broadcast variable.
    val deserializeStartTime = System.currentTimeMillis()
    val ser = SparkEnv.get.closureSerializer.newInstance()
    val (rdd, dep) = ser.deserialize[(RDD[_], ShuffleDependency[_, _, _])](
      ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader)
    _executorDeserializeTime = System.currentTimeMillis() - deserializeStartTime

    metrics = Some(context.taskMetrics)
    var writer: ShuffleWriter[Any, Any] = null
    try {
      val manager = SparkEnv.get.shuffleManager
      writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context)
      writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])
      writer.stop(success = true).get
    } catch {
      case e: Exception =>
        try {
          if (writer != null) {
            writer.stop(success = false)
          }
        } catch {
          case e: Exception =>
            log.debug("Could not stop writer", e)
        }
        throw e
    }
  }

SortShuffleWriter

• 調用SortShuffleWriter.write()方法
• 根據RDDDependency中mapSideCombine是否在map端合併，這個是由算子決定，reduceByKey中mapSideCombine爲true,groupByKey中mapSideCombine爲false,會new ExternalSorter()外部排序對象進行排序
• 而後把records中的數據插入ExternalSorter對象sorter中，數據來源是HDFS當前的分區

/** Write a bunch of records to this task's output */
  override def write(records: Iterator[Product2[K, V]]): Unit = {
    sorter = if (dep.mapSideCombine) {
      require(dep.aggregator.isDefined, "Map-side combine without Aggregator specified!")
      new ExternalSorter[K, V, C](
        context, dep.aggregator, Some(dep.partitioner), dep.keyOrdering, dep.serializer)
    } else {
      // In this case we pass neither an aggregator nor an ordering to the sorter, because we don't
      // care whether the keys get sorted in each partition; that will be done on the reduce side
      // if the operation being run is sortByKey.
      new ExternalSorter[K, V, V](
        context, aggregator = None, Some(dep.partitioner), ordering = None, dep.serializer)
    }
    sorter.insertAll(records)

    // Don't bother including the time to open the merged output file in the shuffle write time,
    // because it just opens a single file, so is typically too fast to measure accurately
    // (see SPARK-3570).
    val output = shuffleBlockResolver.getDataFile(dep.shuffleId, mapId)
    val tmp = Utils.tempFileWith(output)
    try {
      val blockId = ShuffleBlockId(dep.shuffleId, mapId, IndexShuffleBlockResolver.NOOP_REDUCE_ID)
      val partitionLengths = sorter.writePartitionedFile(blockId, tmp)
      shuffleBlockResolver.writeIndexFileAndCommit(dep.shuffleId, mapId, partitionLengths, tmp)
      mapStatus = MapStatus(blockManager.shuffleServerId, partitionLengths)
    } finally {
      if (tmp.exists() && !tmp.delete()) {
        logError(s"Error while deleting temp file ${tmp.getAbsolutePath}")
      }
    }
  }

• ExternalSorter.insertAll()方法
• 該方法會把迭代器records中的數據插入到外部排序對象中
• ExternalSorter中的數據是不進行排序的，是以數組的形式存儲的,健存的爲(partition,key)，值爲Shuffle以前的RDD鏈計算結果 在內存中會對相同的key,進行合併操做，就是map端本地合併，合併的函數就是reduceByKey(+)這個算子中定義的函數
• maybeSpillCollection方法會判斷是否知足磁盤溢出到臨時文件，知足條件，會把當前內存中的數據寫到磁盤中,寫到磁盤中的數據是按partition升序排序，再按key升序排序，就是(key,value)的臨時文件，不帶partition,可是會記錄每一個分區的數量elementsPerPartition(partitionId- 記錄每一次Flush的數據大小batchSizes入到ArrayBuffer中保存
• 內存中的數據存在PartitionedAppendOnlyMap，記住這個對象，後面排序用到了這個裏邊的排序算法

@volatile private var map = new PartitionedAppendOnlyMap[K, C]

def insertAll(records: Iterator[Product2[K, V]]): Unit = {
    // TODO: stop combining if we find that the reduction factor isn't high
    val shouldCombine = aggregator.isDefined

    if (shouldCombine) {
      // Combine values in-memory first using our AppendOnlyMap
      val mergeValue = aggregator.get.mergeValue
      val createCombiner = aggregator.get.createCombiner
      var kv: Product2[K, V] = null
      val update = (hadValue: Boolean, oldValue: C) => {
        if (hadValue) mergeValue(oldValue, kv._2) else createCombiner(kv._2)
      }
      while (records.hasNext) {
        addElementsRead()
        kv = records.next()
        map.changeValue((getPartition(kv._1), kv._1), update)
        maybeSpillCollection(usingMap = true)
      }
    } else {
      // Stick values into our buffer
      while (records.hasNext) {
        addElementsRead()
        val kv = records.next()
        buffer.insert(getPartition(kv._1), kv._1, kv._2.asInstanceOf[C])
        maybeSpillCollection(usingMap = false)
      }
    }
  }

• ExternalSorter.maybeSpillCollection
• estimatedSize當前內存中數據預估佔內存大小
• maybeSpill知足Spill條件就把內存中的數據寫入到臨時文件中
• 調用ExternalSorter.maybeSpill()

/**
   * Spill the current in-memory collection to disk if needed.
   *
   * @param usingMap whether we're using a map or buffer as our current in-memory collection
   */
  private def maybeSpillCollection(usingMap: Boolean): Unit = {
    var estimatedSize = 0L
    if (usingMap) {
      estimatedSize = map.estimateSize()
      if (maybeSpill(map, estimatedSize)) {
        map = new PartitionedAppendOnlyMap[K, C]
      }
    } else {
      estimatedSize = buffer.estimateSize()
      if (maybeSpill(buffer, estimatedSize)) {
        buffer = new PartitionedPairBuffer[K, C]
      }
    }

    if (estimatedSize > _peakMemoryUsedBytes) {
      _peakMemoryUsedBytes = estimatedSize
    }
  }

• ExternalSorter.maybeSpill()
• 對內存中的數據遍歷時，每遍歷32個元素，進行判斷，當前內存是否大於5m，若是大於5m，再進行內存的計算，若是知足就把內存中的數據寫到臨時文件中
• 若是知足條件，調用ExternalSorter.spill()方法，將內存中的數據寫入臨時文件

/**
   * Spills the current in-memory collection to disk if needed. Attempts to acquire more
   * memory before spilling.
   *
   * @param collection collection to spill to disk
   * @param currentMemory estimated size of the collection in bytes
   * @return true if `collection` was spilled to disk; false otherwise
   */
  protected def maybeSpill(collection: C, currentMemory: Long): Boolean = {
    var shouldSpill = false
    if (elementsRead % 32 == 0 && currentMemory >= myMemoryThreshold) {
      // Claim up to double our current memory from the shuffle memory pool
      val amountToRequest = 2 * currentMemory - myMemoryThreshold
      val granted = acquireOnHeapMemory(amountToRequest)
      myMemoryThreshold += granted
      // If we were granted too little memory to grow further (either tryToAcquire returned 0,
      // or we already had more memory than myMemoryThreshold), spill the current collection
      shouldSpill = currentMemory >= myMemoryThreshold
    }
    shouldSpill = shouldSpill || _elementsRead > numElementsForceSpillThreshold
    // Actually spill
    if (shouldSpill) {
      _spillCount += 1
      logSpillage(currentMemory)
      spill(collection)
      _elementsRead = 0
      _memoryBytesSpilled += currentMemory
      releaseMemory()
    }
    shouldSpill
  }

• ExternalSorter.spill()
• 調用方法collection.destructiveSortedWritablePartitionedIterator進行排序，即調用PartitionedAppendOnlyMap.destructiveSortedWritablePartitionedIterator進行排序()方法排序,最終會調用WritablePartitionedPairCollection.destructiveSortedWritablePartitionedIterator()排序，調用方法WritablePartitionedPairCollection.partitionedDestructiveSortedIterator()，沒有實現，調用子類PartitionedAppendOnlyMap.partitionedDestructiveSortedIterator()方法
• 調用方法ExternalSorter.spillMemoryIteratorToDisk() 將磁盤中的數據寫入到spillFile臨時文件中

/**
   * Spill our in-memory collection to a sorted file that we can merge later.
   * We add this file into `spilledFiles` to find it later.
   *
   * @param collection whichever collection we're using (map or buffer)
   */
  override protected[this] def spill(collection: WritablePartitionedPairCollection[K, C]): Unit = {
    val inMemoryIterator = collection.destructiveSortedWritablePartitionedIterator(comparator)
    val spillFile = spillMemoryIteratorToDisk(inMemoryIterator)
    spills.append(spillFile)
  }

• PartitionedAppendOnlyMap.partitionedDestructiveSortedIterator()調用排序算法WritablePartitionedPairCollection.partitionKeyComparator
• 即先按分區數的升序排序，再按key的升序排序

/**
 * Implementation of WritablePartitionedPairCollection that wraps a map in which the keys are tuples
 * of (partition ID, K)
 */
private[spark] class PartitionedAppendOnlyMap[K, V]
  extends SizeTrackingAppendOnlyMap[(Int, K), V] with WritablePartitionedPairCollection[K, V] {

  def partitionedDestructiveSortedIterator(keyComparator: Option[Comparator[K]])
    : Iterator[((Int, K), V)] = {
    val comparator = keyComparator.map(partitionKeyComparator).getOrElse(partitionComparator)
    destructiveSortedIterator(comparator)
  }

  def insert(partition: Int, key: K, value: V): Unit = {
    update((partition, key), value)
  }
}

  /**
   * A comparator for (Int, K) pairs that orders them both by their partition ID and a key ordering.
   */
  def partitionKeyComparator[K](keyComparator: Comparator[K]): Comparator[(Int, K)] = {
    new Comparator[(Int, K)] {
      override def compare(a: (Int, K), b: (Int, K)): Int = {
        val partitionDiff = a._1 - b._1
        if (partitionDiff != 0) {
          partitionDiff
        } else {
          keyComparator.compare(a._2, b._2)
        }
      }
    }
  }
}

• ExternalSorter.spillMemoryIteratorToDisk()
• 建立blockId : temp_shuffle_ + UUID
• 溢出到磁盤臨時文件: temp_shuffle_ + UUID
• 遍歷內存數據inMemoryIterator寫入到磁盤臨時文件spillFile
• 遍歷內存中的數據寫入到臨時文件，會記錄每一個臨時文件中每一個分區的(key,value)各有多少個，elementsPerPartition(partitionId) 若是說數據很大的話，會每默認每10000條數據進行Flush()一次數據到文件中，會記錄每一次Flush的數據大小batchSizes入到ArrayBuffer中保存

/**
   * Spill contents of in-memory iterator to a temporary file on disk.
   */
  private[this] def spillMemoryIteratorToDisk(inMemoryIterator: WritablePartitionedIterator)
      : SpilledFile = {
    // Because these files may be read during shuffle, their compression must be controlled by
    // spark.shuffle.compress instead of spark.shuffle.spill.compress, so we need to use
    // createTempShuffleBlock here; see SPARK-3426 for more context.
    val (blockId, file) = diskBlockManager.createTempShuffleBlock()

    // These variables are reset after each flush
    var objectsWritten: Long = 0
    var spillMetrics: ShuffleWriteMetrics = null
    var writer: DiskBlockObjectWriter = null
    def openWriter(): Unit = {
      assert (writer == null && spillMetrics == null)
      spillMetrics = new ShuffleWriteMetrics
      writer = blockManager.getDiskWriter(blockId, file, serInstance, fileBufferSize, spillMetrics)
    }
    openWriter()

    // List of batch sizes (bytes) in the order they are written to disk
    val batchSizes = new ArrayBuffer[Long]

    // How many elements we have in each partition
    val elementsPerPartition = new Array[Long](numPartitions)

    // Flush the disk writer's contents to disk, and update relevant variables.
    // The writer is closed at the end of this process, and cannot be reused.
    def flush(): Unit = {
      val w = writer
      writer = null
      w.commitAndClose()
      _diskBytesSpilled += spillMetrics.shuffleBytesWritten
      batchSizes.append(spillMetrics.shuffleBytesWritten)
      spillMetrics = null
      objectsWritten = 0
    }

    var success = false
    try {
      while (inMemoryIterator.hasNext) {
        val partitionId = inMemoryIterator.nextPartition()
        require(partitionId >= 0 && partitionId < numPartitions,
          s"partition Id: ${partitionId} should be in the range [0, ${numPartitions})")
        inMemoryIterator.writeNext(writer)
        elementsPerPartition(partitionId) += 1
        objectsWritten += 1

        if (objectsWritten == serializerBatchSize) {
          flush()
          openWriter()
        }
      }
      if (objectsWritten > 0) {
        flush()
      } else if (writer != null) {
        val w = writer
        writer = null
        w.revertPartialWritesAndClose()
      }
      success = true
    } finally {
      if (!success) {
        // This code path only happens if an exception was thrown above before we set success;
        // close our stuff and let the exception be thrown further
        if (writer != null) {
          writer.revertPartialWritesAndClose()
        }
        if (file.exists()) {
          if (!file.delete()) {
            logWarning(s"Error deleting ${file}")
          }
        }
      }
    }

    SpilledFile(file, blockId, batchSizes.toArray, elementsPerPartition)
  }

源碼分析(內存數據Spill合併)

SortShuffleWriter.insertAll

• 即內存中的數據，若是有溢出，寫入到臨時文件後，可能會有多個臨時文件(看數據的大小)

• 這時要開始從全部的臨時文件中，shuffle出按給reduce輸入數據(partition,Iterator),至關於要對多個臨時文件進行合成一個文件，合成的結果按partition升序排序，再按Key升序排序

• SortShuffleWriter.write

• 獲得合成文件shuffleBlockResolver.getDataFile : 格式如 "shuffle_" + shuffleId + "" + mapId + "" + reduceId + ".data" + "." + UUID,reduceId爲默認的0

• 調用關鍵方法ExternalSorter的sorter.writePartitionedFile，這纔是真正合成文件的方法

• 返回值partitionLengths，即爲數據文件中對應索引文件按分區從0到最大分區，每一個分區的數據大小的數組

/** Write a bunch of records to this task's output */
  override def write(records: Iterator[Product2[K, V]]): Unit = {
    sorter = if (dep.mapSideCombine) {
      require(dep.aggregator.isDefined, "Map-side combine without Aggregator specified!")
      new ExternalSorter[K, V, C](
        context, dep.aggregator, Some(dep.partitioner), dep.keyOrdering, dep.serializer)
    } else {
      // In this case we pass neither an aggregator nor an ordering to the sorter, because we don't
      // care whether the keys get sorted in each partition; that will be done on the reduce side
      // if the operation being run is sortByKey.
      new ExternalSorter[K, V, V](
        context, aggregator = None, Some(dep.partitioner), ordering = None, dep.serializer)
    }
    sorter.insertAll(records)

    // Don't bother including the time to open the merged output file in the shuffle write time,
    // because it just opens a single file, so is typically too fast to measure accurately
    // (see SPARK-3570).
    val output = shuffleBlockResolver.getDataFile(dep.shuffleId, mapId)
    val tmp = Utils.tempFileWith(output)
    try {
      val blockId = ShuffleBlockId(dep.shuffleId, mapId, IndexShuffleBlockResolver.NOOP_REDUCE_ID)
      val partitionLengths = sorter.writePartitionedFile(blockId, tmp)
      shuffleBlockResolver.writeIndexFileAndCommit(dep.shuffleId, mapId, partitionLengths, tmp)
      mapStatus = MapStatus(blockManager.shuffleServerId, partitionLengths)
    } finally {
      if (tmp.exists() && !tmp.delete()) {
        logError(s"Error while deleting temp file ${tmp.getAbsolutePath}")
      }
    }
  }

• ExternalSorter.writePartitionedFile
• 按方法名直譯，把數據寫入已分區的文件中
• 若是沒有spill文件，直接按ExternalSorter在內存中排序，用的是TimSort排序算法排序，單獨合出來說，這裏不詳細講
• 若是有spill文件，是咱們重點分析的，這個時候，調用this.partitionedIterator按回按[(partition,Iterator)],按分區升序排序，按(key,value)中key升序排序的數據,並鍵中方法this.partitionedIterator()
• 寫入合併文件中，並返回寫入合併文件中每一個分區的長度，放到lengths數組中，數組索引就是partition

/**
   * Write all the data added into this ExternalSorter into a file in the disk store. This is
   * called by the SortShuffleWriter.
   *
   * @param blockId block ID to write to. The index file will be blockId.name + ".index".
   * @return array of lengths, in bytes, of each partition of the file (used by map output tracker)
   */
  def writePartitionedFile(
      blockId: BlockId,
      outputFile: File): Array[Long] = {

    // Track location of each range in the output file
    val lengths = new Array[Long](numPartitions)

    if (spills.isEmpty) {
      // Case where we only have in-memory data
      val collection = if (aggregator.isDefined) map else buffer
      val it = collection.destructiveSortedWritablePartitionedIterator(comparator)
      while (it.hasNext) {
        val writer = blockManager.getDiskWriter(blockId, outputFile, serInstance, fileBufferSize,
          context.taskMetrics.shuffleWriteMetrics.get)
        val partitionId = it.nextPartition()
        while (it.hasNext && it.nextPartition() == partitionId) {
          it.writeNext(writer)
        }
        writer.commitAndClose()
        val segment = writer.fileSegment()
        lengths(partitionId) = segment.length
      }
    } else {
      // We must perform merge-sort; get an iterator by partition and write everything directly.
      for ((id, elements) <- this.partitionedIterator) {
        if (elements.hasNext) {
          val writer = blockManager.getDiskWriter(blockId, outputFile, serInstance, fileBufferSize,
            context.taskMetrics.shuffleWriteMetrics.get)
          for (elem <- elements) {
            writer.write(elem._1, elem._2)
          }
          writer.commitAndClose()
          val segment = writer.fileSegment()
          lengths(id) = segment.length
        }
      }
    }

    context.taskMetrics().incMemoryBytesSpilled(memoryBytesSpilled)
    context.taskMetrics().incDiskBytesSpilled(diskBytesSpilled)
    context.internalMetricsToAccumulators(
      InternalAccumulator.PEAK_EXECUTION_MEMORY).add(peakMemoryUsedBytes)

    lengths
  }

• this.partitionedIterator()
• 直接調用ExternalSorter.merge()方法
• 臨時文件參數spills
• 內存文件排序算法在這裏調用collection.partitionedDestructiveSortedIterator(comparator)，實際調的是PartitionedAppendOnlyMap.partitionedDestructiveSortedIterator,定義了排序算法partitionKeyComparator，即按partition升序排序，再按key升序排序

/**
   * Return an iterator over all the data written to this object, grouped by partition and
   * aggregated by the requested aggregator. 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.
   * Exposed for testing.
   */
  def partitionedIterator: Iterator[(Int, Iterator[Product2[K, C]])] = {
    val usingMap = aggregator.isDefined
    val collection: WritablePartitionedPairCollection[K, C] = if (usingMap) map else buffer
    if (spills.isEmpty) {
      // Special case: if we have only in-memory data, we don't need to merge streams, and perhaps
      // we don't even need to sort by anything other than partition ID
      if (!ordering.isDefined) {
        // The user hasn't requested sorted keys, so only sort by partition ID, not key
        groupByPartition(destructiveIterator(collection.partitionedDestructiveSortedIterator(None)))
      } else {
        // We do need to sort by both partition ID and key
        groupByPartition(destructiveIterator(
          collection.partitionedDestructiveSortedIterator(Some(keyComparator))))
      }
    } else {
      // Merge spilled and in-memory data
      merge(spills, destructiveIterator(
        collection.partitionedDestructiveSortedIterator(comparator)))
    }
  }

• ExternalSorter.merge()方法
• 0 until numPartitions 從0到numPartitions(不包含)分區循環調用
• IteratorForPartition(p, inMemBuffered),每次取內存中的p分區的數據
• readers是每一個分區是讀全部的臨時文件(由於每份臨時文件，都有可能包含p分區的數據)，
• readers.map(_.readNextPartition())該方法內部用的是每次調一個分區的數據，從0開始，恰好對應的是p分區的數據
• readNextPartition方法即調用SpillReader.readNextPartition()方法
• 對p分區的數據進行mergeWithAggregation合併後，再寫入到合併文件中

/**
   * Merge a sequence of sorted files, giving an iterator over partitions and then over elements
   * inside each partition. This can be used to either write out a new file or return data to
   * the user.
   *
   * Returns 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.
   */
  private def merge(spills: Seq[SpilledFile], inMemory: Iterator[((Int, K), C)])
      : Iterator[(Int, Iterator[Product2[K, C]])] = {
    val readers = spills.map(new SpillReader(_))
    val inMemBuffered = inMemory.buffered
    (0 until numPartitions).iterator.map { p =>
      val inMemIterator = new IteratorForPartition(p, inMemBuffered)
      val iterators = readers.map(_.readNextPartition()) ++ Seq(inMemIterator)
      if (aggregator.isDefined) {
        // Perform partial aggregation across partitions
        (p, mergeWithAggregation(
          iterators, aggregator.get.mergeCombiners, keyComparator, ordering.isDefined))
      } else if (ordering.isDefined) {
        // No aggregator given, but we have an ordering (e.g. used by reduce tasks in sortByKey);
        // sort the elements without trying to merge them
        (p, mergeSort(iterators, ordering.get))
      } else {
        (p, iterators.iterator.flatten)
      }
    }
  }

• SpillReader.readNextPartition()
• readNextItem()是真正讀數臨時文件的方法，
• deserializeStream每次讀取一個流大小，這個大小時在spill輸出文件時寫到batchSizes中的，某個是每一個分區寫一次流，若是分區中的數據很大，就按10000條數據進行一次流，這樣每滿10000次就再讀一次流，這樣就能夠把當前分區裏邊的多少提交流所有讀完
• 一進來就執行nextBatchStream()方法，該方法是按數組batchSizes存儲着每次寫入流時的數據大小
• val batchOffsets = spill.serializerBatchSizes.scanLeft(0L)(_ + _)這個其實取到的值，就恰好是每次流的一位置偏移量，後面的偏移量，恰好是前面全部偏移量之和
• 當前分區的流讀完時，就爲空，就至關於當前分區的數據所有讀完了
• 當partitionId=numPartitions，finished= true說明全部分區的全部文件所有讀完了

def readNextPartition(): Iterator[Product2[K, C]] = new Iterator[Product2[K, C]] {
      val myPartition = nextPartitionToRead
      nextPartitionToRead += 1

      override def hasNext: Boolean = {
        if (nextItem == null) {
          nextItem = readNextItem()
          if (nextItem == null) {
            return false
          }
        }
        assert(lastPartitionId >= myPartition)
        // Check that we're still in the right partition; note that readNextItem will have returned
        // null at EOF above so we would've returned false there
        lastPartitionId == myPartition
      }

      override def next(): Product2[K, C] = {
        if (!hasNext) {
          throw new NoSuchElementException
        }
        val item = nextItem
        nextItem = null
        item
      }
    }

/**
     * Return the next (K, C) pair from the deserialization stream and update partitionId,
     * indexInPartition, indexInBatch and such to match its location.
     *
     * If the current batch is drained, construct a stream for the next batch and read from it.
     * If no more pairs are left, return null.
     */
    private def readNextItem(): (K, C) = {
      if (finished || deserializeStream == null) {
        return null
      }
      val k = deserializeStream.readKey().asInstanceOf[K]
      val c = deserializeStream.readValue().asInstanceOf[C]
      lastPartitionId = partitionId
      // Start reading the next batch if we're done with this one
      indexInBatch += 1
      if (indexInBatch == serializerBatchSize) {
        indexInBatch = 0
        deserializeStream = nextBatchStream()
      }
      // Update the partition location of the element we're reading
      indexInPartition += 1
      skipToNextPartition()
      // If we've finished reading the last partition, remember that we're done
      if (partitionId == numPartitions) {
        finished = true
        if (deserializeStream != null) {
          deserializeStream.close()
        }
      }
      (k, c)
    }

/** Construct a stream that only reads from the next batch */
    def nextBatchStream(): DeserializationStream = {
      // Note that batchOffsets.length = numBatches + 1 since we did a scan above; check whether
      // we're still in a valid batch.
      if (batchId < batchOffsets.length - 1) {
        if (deserializeStream != null) {
          deserializeStream.close()
          fileStream.close()
          deserializeStream = null
          fileStream = null
        }

        val start = batchOffsets(batchId)
        fileStream = new FileInputStream(spill.file)
        fileStream.getChannel.position(start)
        batchId += 1

        val end = batchOffsets(batchId)

        assert(end >= start, "start = " + start + ", end = " + end +
          ", batchOffsets = " + batchOffsets.mkString("[", ", ", "]"))

        val bufferedStream = new BufferedInputStream(ByteStreams.limit(fileStream, end - start))

        val sparkConf = SparkEnv.get.conf
        val stream = blockManager.wrapForCompression(spill.blockId,
          CryptoStreamUtils.wrapForEncryption(bufferedStream, sparkConf))
        serInstance.deserializeStream(stream)
      } else {
        // No more batches left
        cleanup()
        null
      }
    }

end

end