spark源码系列(6) Task、shuffle执行原理

先看ShuffleMapTask#runTask

override def runTask(context: TaskContext): MapStatus = {
   。。。。
    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

首先获取shuffleMananger。从shuffleManager中获取manager的writer方法，

这里要说明下shuffleManager

ShuffleManager是一个接口，主要功能有：

( l ) registerShuffle:每个 RDD 在构建它的父依赖 (这里特指 ShuffleDependency)时， 都会先注册到 ShuffleManager，获取 ShuffleHandler，用于后续数据块的读写等。

(2) getWriter: 可以通过 ShuffleHandler 获取数据块写入器，写数据时通过 Shuffle 的块 解析器 shuffleBlockResolver，获取写入位置(通常将写入位置抽象为 Bucket，位置的选拌则 由洗牌的规则，即 Shu旺le 的分区器决定〉，然后将数据写入到相应位置〈理论上，位置可以 位于任何能存储数据的地方，包括磁盘、内存或其他存储框架等，目前在可插拔框架的儿科 实现中 ， Spark 与 Hadoop 一样都采用磁盘的方式进行存储，主要目的是为了节约内存，同时

提高容错性)。
(3) getReader:可以通过 ShuffleHandIer获取数据块读取器，然后通过 Shuffle 的块解析

器 shuffleBlockResolver，获取指定数据块 。
(4) unregisterShuffle:与注册对应，用于删除元数据等后续清理操作。
( 5) shuffleBlockResolver: Shuffle 的块解析器，通过该解析器，为数据块的读写提供支撑层，便于抽象具体的实现细节。

 

我们之前在性能调优的时候说过有个参数spark.shuffle.manager

这个可以指定选用的shuffleManager，在2.0之后HashShuffleManager已经被移除

所以我们主要看sortShuffleMananger

getWriter方法：

override def getWriter[K, V](
    handle: ShuffleHandle,
    mapId: Int,
    context: TaskContext): ShuffleWriter[K, V] = {
  numMapsForShuffle.putIfAbsent(
    handle.shuffleId, handle.asInstanceOf[BaseShuffleHandle[_, _, _]].numMaps)
  val env = SparkEnv.get
  handle match {
    case unsafeShuffleHandle: SerializedShuffleHandle[K @unchecked, V @unchecked] =>
      new UnsafeShuffleWriter(
        env.blockManager,
        shuffleBlockResolver.asInstanceOf[IndexShuffleBlockResolver],
        context.taskMemoryManager(),
        unsafeShuffleHandle,
        mapId,
        context,
        env.conf)
    case bypassMergeSortHandle: BypassMergeSortShuffleHandle[K @unchecked, V @unchecked] =>
      new BypassMergeSortShuffleWriter(
        env.blockManager,
        shuffleBlockResolver.asInstanceOf[IndexShuffleBlockResolver],
        bypassMergeSortHandle,
        mapId,
        context,
        env.conf)
    case other: BaseShuffleHandle[K @unchecked, V @unchecked, _] =>
      new SortShuffleWriter(shuffleBlockResolver, other, mapId, context)
  }

这边有我们比较熟悉的两个writer

BypassMergeSortShuffleWriter和SortShuffleWriter 这个在我们性能调优参数中有说明过，这里不详细说了。我们以SortShuffleWriter为例。这边我们先将rdd.iterator方法

final def iterator(split: Partition, context: TaskContext): Iterator[T] = {
  if (storageLevel != StorageLevel.NONE) {
    SparkEnv.get.cacheManager.getOrCompute(this, split, context, storageLevel)
  } else {
    computeOrReadCheckpoint(split, context)
  }
}

if分支是从cache中获取，后续说，进入computeOrReadCheckpoint

if (isCheckpointedAndMaterialized) {
  firstParent[T].iterator(split, context)
} else {
  compute(split, context)
}

if分支是从checkpoint中获取，后续说

看compute。这是一个抽象方法，我们随便找一个RDD的实现类，MapPartitionRDD#compute

override def compute(split: Partition, context: TaskContext): Iterator[U] =
  f(context, split.index, firstParent[T].iterator(split, context))

这里的f就是调用我们算子中的函数。

我们进入SortShuffleWriter的write方法

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)
  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)
}

首先会初始化一个ExternalSorter 这个东西具体不深入讲，我们只要知道这玩意是用来排序、聚合、缓存的

 

进入sorter#insertAll方法

while (records.hasNext) {
  addElementsRead()
  val kv = records.next()
  map.insert(getPartition(kv._1), kv._1, kv._2.asInstanceOf[C])
  maybeSpillCollection(usingMap = false)
}

这里会优先把数据写入到内存缓存中，也就是map，然后调用maybeSpillCollection，如果太多会写磁盘

if (maybeSpill(map, estimatedSize)) {
  map = new PartitionedAppendOnlyMap[K, C]
}

maySpill来判断是否会写磁盘，

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 =
      taskMemoryManager.acquireExecutionMemory(amountToRequest, MemoryMode.ON_HEAP, null)
    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
}

首先每获取32k数据，判断如果当前缓冲区超过myMemoryThreshold，就会尝试扩大缓冲区一倍，如果扩大后没有大大一倍，就会落盘，

spill(collection)是落盘动作

这里就不细说了

回到SortShuffleWriter#write方法

val partitionLengths = sorter.writePartitionedFile(blockId, tmp)

这里会把多个文件合并成一个

shuffleBlockResolver.writeIndexFileAndCommit(dep.shuffleId, mapId, partitionLengths, tmp)

接下去则会写索引文件

索引文件主要用处是记录每个partion在一个shuffle文件中的变一辆，以方便拉取数据

最后返回MapStatus。

这边用一张图来总结下SortShuffleWriter的过程

上面长篇大论把shuffleMapTask#runTask方法阐述了一次

ResultMap的runTask方法相对简单，而且涉及到BlockMananger的知识，这个放在下一篇中说明。