Spark Stage切分 源码剖析——DAGScheduler

Spark中的任务管理是很重要的内容，可以说想要理解Spark的计算流程，就必须对它的任务的切分有一定的了解。不然你就看不懂Spark UI,看不懂Spark UI就无法去做优化...因此本篇就从源码的角度说说其中的一部分，Stage的切分——DAG图的创建

先说说概念

在Spark中有几个维度的概念：

• 应用Application，你的代码就是一个应用
• Job，Job是以action为边界的。
• Stage，是按照宽窄依赖来界定的
• Task，最终落实到各个工作节点上的任务，是真正意义上的任务

光说上面的概念，可能还不是很了解它的原理，说的通俗点：

Spark的代码都会运行在一个叫做driver的东西上，然后driver回去解析代码，遇到action操作，就提交一个job；然后从最后一个rdd反向解析这个job的rdd，碰到宽依赖就创建一个stage；最后以stage为单位，创建一个任务集，提交给各个机器去执行。

举个例子，在程序员的世界里，有那么几种角色：产品经理（负责提需求）、项目经理（负责管理研发）、程序员（负责写程序）。当产品经理有什么需求时，会找一下项目经理，给它一份需求文档。项目经理根据需求文档，按照业务拆分成不同的模块，然后以模块为单位分配给程序员。比如电商项目背景里，有的程序员专门负责支付，有的专门负责客服，有的专门负责商品。

这样，对应到Spark中：

• 产品经理，就是client，负责提出一些有目的性的需求
• 项目经理，就是driver程序，负责解析这些需求，把任务按照一定的规则拆分（stage）
• 程序员，就是excutor，负责最终的执行。

那么在Spark中的任务拆分，具体的流程可以参考下面的图：

• 首先在SparkContext初始化的时候会创建DAGScheduler,这个DAGScheduelr每个应用只有一个。然后DAGScheduler创建的时候，会初始化一个事件捕获对象，并且开启监听。之后我们的任务都会发给这个事件监听器，它会按照任务的类型创建不同的任务。
• 再从客户端程序方面说，当我们调用action操作的时候，就会触发runjob，它内部其实就是向前面的那个事件监听器提交一个任务。
• 最后事件监听器调用DAGScheduler的handleJobSubmitted真正的处理
• 处理的时候，会先创建一个resultStage，每个job只有一个resultstage，其余的都是shufflestage.然后根据rdd的依赖关系，按照广度优先的思想遍历rdd，遇到shufflerdd就创建一个新的stage。
• 形成DAG图后，遍历等待执行的stage列表，如果这个stage所依赖的父stage执行完了，它就可以执行了；否则还需要继续等待。
• 最终stage会以taskset的形式，提交给TaskScheduler，然后最后提交给excutor。

任务的接收

SparkContext初始化创建DagScheduler

yyyy-mm-dd

　　

DAGScheduler

private[scheduler] val waitingStages = new HashSet[Stage]
private[scheduler] val runningStages = new HashSet[Stage]
private[scheduler] val failedStages = new HashSet[Stage]
private[scheduler] val activeJobs = new HashSet[ActiveJob]
  
private[scheduler] val eventProcessLoop = new DAGSchedulerEventProcessLoop(this)
  
// 启动事件监听
eventProcessLoop.start()

　　

EventLoop#run

private val eventThread = new Thread(name) {
    setDaemon(true)

    override def run(): Unit = {
      try {
        while (!stopped.get) {
          val event = eventQueue.take()
          try {
            onReceive(event)
          } catch {
            ...
          }
        }
      } catch {...}
    }

  }

　　

DAGSchedulerEventProcessLoop#onReceive

override def onReceive(event: DAGSchedulerEvent): Unit = {
    val timerContext = timer.time()
    try {
      doOnReceive(event)
    } finally {
      timerContext.stop()
    }
  }
private def doOnReceive(event: DAGSchedulerEvent): Unit = event match {
    // 处理Job提交事件
    case JobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties) =>
      dagScheduler.handleJobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties)
    // 处理Map Stage提交事件
    case MapStageSubmitted(jobId, dependency, callSite, listener, properties) =>
      dagScheduler.handleMapStageSubmitted(jobId, dependency, callSite, listener, properties)
    // 处理Stage取消事件
    case StageCancelled(stageId) =>
      dagScheduler.handleStageCancellation(stageId)
    // 处理Job取消事件
    case JobCancelled(jobId) =>
      dagScheduler.handleJobCancellation(jobId)
    // 处理Job组取消事件
    case JobGroupCancelled(groupId) =>
      dagScheduler.handleJobGroupCancelled(groupId)
    // 处理所以Job取消事件
    case AllJobsCancelled =>
      dagScheduler.doCancelAllJobs()
    // 处理Executor分配事件
    case ExecutorAdded(execId, host) =>
      dagScheduler.handleExecutorAdded(execId, host)
    // 处理Executor丢失事件
    case ExecutorLost(execId) =>
      dagScheduler.handleExecutorLost(execId, fetchFailed = false)

    case BeginEvent(task, taskInfo) =>
      dagScheduler.handleBeginEvent(task, taskInfo)

    case GettingResultEvent(taskInfo) =>
      dagScheduler.handleGetTaskResult(taskInfo)
    // 处理完成事件
    case completion @ CompletionEvent(task, reason, _, _, taskInfo, taskMetrics) =>
      dagScheduler.handleTaskCompletion(completion)
    // 处理task集失败事件
    case TaskSetFailed(taskSet, reason, exception) =>
      dagScheduler.handleTaskSetFailed(taskSet, reason, exception)
    // 处理重新提交失败Stage事件
    case ResubmitFailedStages =>
      dagScheduler.resubmitFailedStages()
  }

　　

任务的提交

RDD#collect()

提交任务

def collect(): Array[T] = withScope {
    val results = sc.runJob(this, (iter: Iterator[T]) => iter.toArray)
    Array.concat(results: _*)
  }

SparkContext#runJob

def runJob[T, U: ClassTag](
      rdd: RDD[T],
      func: (TaskContext, Iterator[T]) => U,
      partitions: Seq[Int],
      resultHandler: (Int, U) => Unit): Unit = {
    ...
    dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, resultHandler, localProperties.get)
    ...
  }

DAGScheduler#runJob

override def onReceive(event: DAGSchedulerEvent): Unit = {
    val timerContext = timer.time()
    try {
      doOnReceive(event)
    } finally {
      timerContext.stop()
    }
  }
private def doOnReceive(event: DAGSchedulerEvent): Unit = event match {
    // 处理Job提交事件
    case JobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties) =>
      dagScheduler.handleJobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties)
    // 处理Map Stage提交事件
    case MapStageSubmitted(jobId, dependency, callSite, listener, properties) =>
      dagScheduler.handleMapStageSubmitted(jobId, dependency, callSite, listener, properties)
    // 处理Stage取消事件
    case StageCancelled(stageId) =>
      dagScheduler.handleStageCancellation(stageId)
    // 处理Job取消事件
    case JobCancelled(jobId) =>
      dagScheduler.handleJobCancellation(jobId)
    // 处理Job组取消事件
    case JobGroupCancelled(groupId) =>
      dagScheduler.handleJobGroupCancelled(groupId)
    // 处理所以Job取消事件
    case AllJobsCancelled =>
      dagScheduler.doCancelAllJobs()
    // 处理Executor分配事件
    case ExecutorAdded(execId, host) =>
      dagScheduler.handleExecutorAdded(execId, host)
    // 处理Executor丢失事件
    case ExecutorLost(execId) =>
      dagScheduler.handleExecutorLost(execId, fetchFailed = false)

    case BeginEvent(task, taskInfo) =>
      dagScheduler.handleBeginEvent(task, taskInfo)

    case GettingResultEvent(taskInfo) =>
      dagScheduler.handleGetTaskResult(taskInfo)
    // 处理完成事件
    case completion @ CompletionEvent(task, reason, _, _, taskInfo, taskMetrics) =>
      dagScheduler.handleTaskCompletion(completion)
    // 处理task集失败事件
    case TaskSetFailed(taskSet, reason, exception) =>
      dagScheduler.handleTaskSetFailed(taskSet, reason, exception)
    // 处理重新提交失败Stage事件
    case ResubmitFailedStages =>
      dagScheduler.resubmitFailedStages()
  }

　　

DAGScheduler#submitJob

def submitJob[T, U](
      rdd: RDD[T],
      func: (TaskContext, Iterator[T]) => U,
      partitions: Seq[Int],
      callSite: CallSite,
      resultHandler: (Int, U) => Unit,
      properties: Properties): JobWaiter[U] = {
    ...
    val waiter = new JobWaiter(this, jobId, partitions.size, resultHandler)
    eventProcessLoop.post(JobSubmitted(
      jobId, rdd, func2, partitions.toArray, callSite, waiter,
      SerializationUtils.clone(properties)))
    ...
  }

　　

job的切分

DAGScheduler#handleJobSubmitted

private[scheduler] def handleJobSubmitted(jobId: Int,
      finalRDD: RDD[_],
      func: (TaskContext, Iterator[_]) => _,
      partitions: Array[Int],
      callSite: CallSite,
      listener: JobListener,
      properties: Properties) {
    var finalStage: ResultStage = null
    try {
      finalStage = newResultStage(finalRDD, func, partitions, jobId, callSite)
    } catch {
      ...
    }

    //生成 ActiveJob
    val job = new ActiveJob(jobId, finalStage, callSite, listener, properties)
    clearCacheLocs()
   ...
    submitStage(finalStage)
    submitWaitingStages()
  }

　　

DAGScheduler#newRessultStage

private[scheduler] def handleJobSubmitted(jobId: Int,
      finalRDD: RDD[_],
      func: (TaskContext, Iterator[_]) => _,
      partitions: Array[Int],
      callSite: CallSite,
      listener: JobListener,
      properties: Properties) {
    var finalStage: ResultStage = null
    try {
      finalStage = newResultStage(finalRDD, func, partitions, jobId, callSite)
    } catch {
      ...
    }

    //生成 ActiveJob
    val job = new ActiveJob(jobId, finalStage, callSite, listener, properties)
    clearCacheLocs()
   ...
    submitStage(finalStage)
    submitWaitingStages()
  }

　　

DAGScheduler$getParentStagesAndId

private def getParentStagesAndId(rdd: RDD[_], firstJobId: Int): (List[Stage], Int) = {
    val parentStages = getParentStages(rdd, firstJobId)
    val id = nextStageId.getAndIncrement()
    (parentStages, id)
  }

DAGScheduler#getParentStages

private[scheduler] def handleJobSubmitted(jobId: Int,
      finalRDD: RDD[_],
      func: (TaskContext, Iterator[_]) => _,
      partitions: Array[Int],
      callSite: CallSite,
      listener: JobListener,
      properties: Properties) {
    var finalStage: ResultStage = null
    try {
      finalStage = newResultStage(finalRDD, func, partitions, jobId, callSite)
    } catch {
      ...
    }

    //生成 ActiveJob
    val job = new ActiveJob(jobId, finalStage, callSite, listener, properties)
    clearCacheLocs()
   ...
    submitStage(finalStage)
    submitWaitingStages()
  }

　　

DAGScheduler#getShuffleMapStage

private def getShuffleMapStage(
      shuffleDep: ShuffleDependency[_, _, _],
      firstJobId: Int): ShuffleMapStage = {
    shuffleToMapStage.get(shuffleDep.shuffleId) match {
      case Some(stage) => stage //如果已经生成过，直接返回
      case None =>              //如果没有生成过，创建新的stage
        // We are going to register ancestor shuffle dependencies
        // 为所有的shuffle stage生成 ShuffleMapStage
        getAncestorShuffleDependencies(shuffleDep.rdd).foreach { dep =>
          shuffleToMapStage(dep.shuffleId) = newOrUsedShuffleStage(dep, firstJobId)
        }
        // Then register current shuffleDep
        val stage = newOrUsedShuffleStage(shuffleDep, firstJobId)
        shuffleToMapStage(shuffleDep.shuffleId) = stage
        stage
    }
  }

　　

DAGScheduler#newOrUsedShuffleStage

DAGScheduler#newShuffleMapStage

private def newShuffleMapStage(
      rdd: RDD[_],
      numTasks: Int,
      shuffleDep: ShuffleDependency[_, _, _],
      firstJobId: Int,
      callSite: CallSite): ShuffleMapStage = {
    //获得当前stage的父stage
    val (parentStages: List[Stage], id: Int) = getParentStagesAndId(rdd, firstJobId)
    val stage: ShuffleMapStage = new ShuffleMapStage(id, rdd, numTasks, parentStages,
      firstJobId, callSite, shuffleDep)

    stageIdToStage(id) = stage
    updateJobIdStageIdMaps(firstJobId, stage)// 更新该job中包含的stage
    stage
  }

DAGScheduler#submitStage

private def submitS

private def newOrUsedShuffleStage(
      shuffleDep: ShuffleDependency[_, _, _],
      firstJobId: Int): ShuffleMapStage = {
    val rdd = shuffleDep.rdd
    val numTasks = rdd.partitions.length
    val stage = newShuffleMapStage(rdd, numTasks, shuffleDep, firstJobId, rdd.creationSite)
    if (mapOutputTracker.containsShuffle(shuffleDep.shuffleId)) {
      val serLocs = mapOutputTracker.getSerializedMapOutputStatuses(shuffleDep.shuffleId)
      val locs = MapOutputTracker.deserializeMapStatuses(serLocs)
      (0 until locs.length).foreach { i =>
        if (locs(i) ne null) {
          // locs(i) will be null if missing
          stage.addOutputLoc(i, locs(i))
        }
      }
    } else {
      // Kind of ugly: need to register RDDs with the cache and map output tracker here
      // since we can't do it in the RDD constructor because # of partitions is unknown
      logInfo("Registering RDD " + rdd.id + " (" + rdd.getCreationSite + ")")
      mapOutputTracker.registerShuffle(shuffleDep.shuffleId, rdd.partitions.length)
    }
    stage
  }

　　

tage(stage: Stage) { val jobId = activeJobForStage(stage) if (jobId.isDefined) { logDebug("submitStage(" + stage + ")") if (!waitingStages(stage) && !runningStages(stage) && !failedStages(stage)) { val missing = getMissingParentStages(stage).sortBy(_.id) //获取到Parent Stage后，递归调用上面那个方法按照StageId小的先提交的原则 logDebug("missing: " + missing) if (missing.isEmpty) { logInfo("Submitting " + stage + " (" + stage.rdd + "), which has no missing parents") submitMissingTasks(stage, jobId.get) } else { for (parent <- missing) { submitStage(parent) } waitingStages += stage } } } else { abortStage(stage, "No active job for stage " + stage.id, None) } }

DAGScheduler#getMissingParentStages

private def getMissingParentStages(stage: Stage): List[Stage] = {
    val missing = new HashSet[Stage]
    val visited = new HashSet[RDD[_]]
    // We are manually maintaining a stack here to prevent StackOverflowError
    // caused by recursively visiting
    val waitingForVisit = new Stack[RDD[_]]
    def visit(rdd: RDD[_]) {
      if (!visited(rdd)) {
        visited += rdd
        val rddHasUncachedPartitions = getCacheLocs(rdd).contains(Nil)
        if (rddHasUncachedPartitions) {
          for (dep <- rdd.dependencies) {
            dep match {
              case shufDep: ShuffleDependency[_, _, _] =>
                val mapStage = getShuffleMapStage(shufDep, stage.firstJobId)
                if (!mapStage.isAvailable) {
                  missing += mapStage
                }
              case narrowDep: NarrowDependency[_] =>
                waitingForVisit.push(narrowDep.rdd)
            }
          }
        }
      }
    }
    waitingForVisit.push(stage.rdd)
    while (waitingForVisit.nonEmpty) {
      visit(waitingForVisit.pop())
    }
    missing.toList
  }

　　

DAGScheduler#submitMissingTasks

private def submitMissingTasks(stage: Stage, jobId: Int) {
    logDebug("submitMissingTasks(" + stage + ")")
    // Get our pending tasks and remember them in our pendingTasks entry
    stage.pendingPartitions.clear()

    // First figure out the indexes of partition ids to compute.
    val partitionsToCompute: Seq[Int] = stage.findMissingPartitions()

    // Create internal accumulators if the stage has no accumulators initialized.
    // Reset internal accumulators only if this stage is not partially submitted
    // Otherwise, we may override existing accumulator values from some tasks
    if (stage.internalAccumulators.isEmpty || stage.numPartitions == partitionsToCompute.size) {
      stage.resetInternalAccumulators()
    }

    // Use the scheduling pool, job group, description, etc. from an ActiveJob associated
    // with this Stage
    val properties = jobIdToActiveJob(jobId).properties

    runningStages += stage
    // SparkListenerStageSubmitted should be posted before testing whether tasks are
    // serializable. If tasks are not serializable, a SparkListenerStageCompleted event
    // will be posted, which should always come after a corresponding SparkListenerStageSubmitted
    // event.
    stage match {
      case s: ShuffleMapStage =>
        outputCommitCoordinator.stageStart(stage = s.id, maxPartitionId = s.numPartitions - 1)
      case s: ResultStage =>
        outputCommitCoordinator.stageStart(
          stage = s.id, maxPartitionId = s.rdd.partitions.length - 1)
    }
    val taskIdToLocations: Map[Int, Seq[TaskLocation]] = try {
      stage match {
        case s: ShuffleMapStage =>
          partitionsToCompute.map { id => (id, getPreferredLocs(stage.rdd, id))}.toMap
        case s: ResultStage =>
          val job = s.activeJob.get
          partitionsToCompute.map { id =>
            val p = s.partitions(id)
            (id, getPreferredLocs(stage.rdd, p))
          }.toMap
      }
    } catch {
      case NonFatal(e) =>
        stage.makeNewStageAttempt(partitionsToCompute.size)
        listenerBus.post(SparkListenerStageSubmitted(stage.latestInfo, properties))
        abortStage(stage, s"Task creation failed: $e\n${e.getStackTraceString}", Some(e))
        runningStages -= stage
        return
    }

    stage.makeNewStageAttempt(partitionsToCompute.size, taskIdToLocations.values.toSeq)
    listenerBus.post(SparkListenerStageSubmitted(stage.latestInfo, properties))

    // TODO: Maybe we can keep the taskBinary in Stage to avoid serializing it multiple times.
    // Broadcasted binary for the task, used to dispatch tasks to executors. Note that we broadcast
    // the serialized copy of the RDD and for each task we will deserialize it, which means each
    // task gets a different copy of the RDD. This provides stronger isolation between tasks that
    // might modify state of objects referenced in their closures. This is necessary in Hadoop
    // where the JobConf/Configuration object is not thread-safe.
    var taskBinary: Broadcast[Array[Byte]] = null
    try {
      // For ShuffleMapTask, serialize and broadcast (rdd, shuffleDep).
      // For ResultTask, serialize and broadcast (rdd, func).
      val taskBinaryBytes: Array[Byte] = stage match {
        case stage: ShuffleMapStage =>
          closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef).array()
        case stage: ResultStage =>
          closureSerializer.serialize((stage.rdd, stage.func): AnyRef).array()
      }

      taskBinary = sc.broadcast(taskBinaryBytes)
    } catch {
      // In the case of a failure during serialization, abort the stage.
      case e: NotSerializableException =>
        abortStage(stage, "Task not serializable: " + e.toString, Some(e))
        runningStages -= stage

        // Abort execution
        return
      case NonFatal(e) =>
        abortStage(stage, s"Task serialization failed: $e\n${e.getStackTraceString}", Some(e))
        runningStages -= stage
        return
    }

    val tasks: Seq[Task[_]] = try {
      stage match {
        case stage: ShuffleMapStage =>
          partitionsToCompute.map { id =>
            val locs = taskIdToLocations(id)
            val part = stage.rdd.partitions(id)
            new ShuffleMapTask(stage.id, stage.latestInfo.attemptId,
              taskBinary, part, locs, stage.internalAccumulators)
          }

        case stage: ResultStage =>
          val job = stage.activeJob.get
          partitionsToCompute.map { id =>
            val p: Int = stage.partitions(id)
            val part = stage.rdd.partitions(p)
            val locs = taskIdToLocations(id)
            new ResultTask(stage.id, stage.latestInfo.attemptId,
              taskBinary, part, locs, id, stage.internalAccumulators)
          }
      }
    } catch {
      case NonFatal(e) =>
        abortStage(stage, s"Task creation failed: $e\n${e.getStackTraceString}", Some(e))
        runningStages -= stage
        return
    }

    if (tasks.size > 0) {
      logInfo("Submitting " + tasks.size + " missing tasks from " + stage + " (" + stage.rdd + ")")
      stage.pendingPartitions ++= tasks.map(_.partitionId)
      logDebug("New pending partitions: " + stage.pendingPartitions)
      taskScheduler.submitTasks(new TaskSet(
        tasks.toArray, stage.id, stage.latestInfo.attemptId, jobId, properties))
      stage.latestInfo.submissionTime = Some(clock.getTimeMillis())
    } else {
      // Because we posted SparkListenerStageSubmitted earlier, we should mark
      // the stage as completed here in case there are no tasks to run
      markStageAsFinished(stage, None)

      val debugString = stage match {
        case stage: ShuffleMapStage =>
          s"Stage ${stage} is actually done; " +
            s"(available: ${stage.isAvailable}," +
            s"available outputs: ${stage.numAvailableOutputs}," +
            s"partitions: ${stage.numPartitions})"
        case stage : ResultStage =>
          s"Stage ${stage} is actually done; (partitions: ${stage.numPartitions})"
      }
      logDebug(debugString)
    }
  }

　　

DAGScheduler#submitWaitingStages

private def submitWaitingStages() {
    // TODO: We might want to run this less often, when we are sure that something has become
    // runnable that wasn't before.
    logTrace("Checking for newly runnable parent stages")
    logTrace("running: " + runningStages)
    logTrace("waiting: " + waitingStages)
    logTrace("failed: " + failedStages)
    val waitingStagesCopy = waitingStages.toArray
    waitingStages.clear()
    for (stage <- waitingStagesCopy.sortBy(_.firstJobId)) {
      submitStage(stage)
    }
  }

　　

参考

• http://blog.csdn.net/dabokele/article/details/51902617
• http://blog.csdn.net/zhouzx2010/article/details/51965196
• http://www.cnblogs.com/luogankun/p/3826245.html