【Spark】DAGScheduler源代码浅析

DAGScheduler

DAGScheduler的主要任务是基于Stage构建DAG，决定每个任务的最佳位置

记录哪个RDD或者Stage输出被物化

面向stage的调度层。为job生成以stage组成的DAG。提交TaskSet给TaskScheduler运行

又一次提交shuffle输出丢失的stage

每个Stage内。都是独立的tasks，他们共同运行同一个computefunction，享有同样的shuffledependencies。DAG在切分stage的时候是按照出现shuffle为界限的。

DAGScheduler实例化

以下的代码是SparkContext实例化DAGScheduler的过程：

  @volatile private[spark] var dagScheduler: DAGScheduler = _
  try {
    dagScheduler = new DAGScheduler(this)
  } catch {
    case e: Exception => {
      try {
        stop()
      } finally {
        throw new SparkException("Error while constructing DAGScheduler", e)
      }
    }
  }

以下代码显示了DAGScheduler的构造函数定义中，通过绑定TaskScheduler的方式创建，当中次构造函数去调用主构造函数来将sc的字段填充入參：

private[spark]
class DAGScheduler(
    private[scheduler] val sc: SparkContext,
    private[scheduler] val taskScheduler: TaskScheduler,
    listenerBus: LiveListenerBus,
    mapOutputTracker: MapOutputTrackerMaster,
    blockManagerMaster: BlockManagerMaster,
    env: SparkEnv,
    clock: Clock = new SystemClock())
  extends Logging {
  def this(sc: SparkContext, taskScheduler: TaskScheduler) = {
    this(
      sc,
      taskScheduler,
      sc.listenerBus,
      sc.env.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster],
      sc.env.blockManager.master,
      sc.env)
  }
  def this(sc: SparkContext) = this(sc, sc.taskScheduler)

作业提交与DAGScheduler操作

Action的大部分操作会进行作业（job）的提交，源代码1.0版的job提交过程的大致调用链是：sc.runJob()–>dagScheduler.runJob–>dagScheduler.submitJob—>dagSchedulerEventProcessActor.JobSubmitted–>dagScheduler.handleJobSubmitted–>dagScheduler.submitStage–>dagScheduler.submitMissingTasks–>taskScheduler.submitTasks。

详细的作业提交运行期的函数调用为：

sc.runJob->dagScheduler.runJob->submitJob

DAGScheduler::submitJob会创建JobSummitted的event发送给内嵌类eventProcessActor（在源代码1.4中，submitJob函数中，使用DAGSchedulerEventProcessLoop类进行事件的处理）

eventProcessActor在接收到JobSubmmitted之后调用processEvent处理函数

job到stage的转换，生成finalStage并提交运行。关键是调用submitStage

在submitStage中会计算stage之间的依赖关系，依赖关系分为宽依赖和窄依赖两种

假设计算中发现当前的stage没有不论什么依赖或者全部的依赖都已经准备完毕，则提交task

提交task是调用函数submitMissingTasks来完毕

task真正运行在哪个worker上面是由TaskScheduler来管理，也就是上面的submitMissingTasks会调用TaskScheduler::submitTasks

TaskSchedulerImpl中会依据Spark的当前运行模式来创建对应的backend,假设是在单机运行则创建LocalBackend

LocalBackend收到TaskSchedulerImpl传递进来的ReceiveOffers事件

receiveOffers->executor.launchTask->TaskRunner.run

DAGScheduler的runJob函数

DAGScheduler.runjob最后把结果通过resultHandler保存返回。

这里DAGScheduler的runJob函数调用DAGScheduler的submitJob函数来提交任务：

  def runJob[T, U: ClassTag](
      rdd: RDD[T],
      func: (TaskContext, Iterator[T]) => U,
      partitions: Seq[Int],
      callSite: CallSite,
      allowLocal: Boolean,
      resultHandler: (Int, U) => Unit,
      properties: Properties): Unit = {
    val start = System.nanoTime
    val waiter = submitJob(rdd, func, partitions, callSite, allowLocal, resultHandler, properties)
    waiter.awaitResult() match {
      case JobSucceeded => {
        logInfo("Job %d finished: %s, took %f s".format
          (waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
      }
      case JobFailed(exception: Exception) =>
        logInfo("Job %d failed: %s, took %f s".format
          (waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
        throw exception
    }
  }

作业提交的调度

在Spark源代码1.4.0中，DAGScheduler的submitJob函数不再使用DAGEventProcessActor进行事件处理和消息通信，而是使用DAGSchedulerEventProcessLoop类实例eventProcessLoop进行JobSubmitted事件的post动作。

以下是submitJob函数代码：

  /**
   * Submit a job to the job scheduler and get a JobWaiter object back. The JobWaiter object
   * can be used to block until the the job finishes executing or can be used to cancel the job.
   */
  def submitJob[T, U](
      rdd: RDD[T],
      func: (TaskContext, Iterator[T]) => U,
      partitions: Seq[Int],
      callSite: CallSite,
      allowLocal: Boolean,
      resultHandler: (Int, U) => Unit,
      properties: Properties): JobWaiter[U] = {
    // Check to make sure we are not launching a task on a partition that does not exist.
    val maxPartitions = rdd.partitions.length
    partitions.find(p => p >= maxPartitions || p < 0).foreach { p =>
      throw new IllegalArgumentException(
        "Attempting to access a non-existent partition: " + p + ". " +
          "Total number of partitions: " + maxPartitions)
    }
    val jobId = nextJobId.getAndIncrement()
    if (partitions.size == 0) {
      return new JobWaiter[U](this, jobId, 0, resultHandler)
    }
    assert(partitions.size > 0)
    val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _]
    val waiter = new JobWaiter(this, jobId, partitions.size, resultHandler)
    eventProcessLoop.post(JobSubmitted(
      jobId, rdd, func2, partitions.toArray, allowLocal, callSite, waiter, properties))
    waiter
  }

当eventProcessLoop对象投递了JobSubmitted事件之后，对象内的eventThread线程实例对事件进行处理。不断从事件队列中取出事件，调用onReceive函数处理事件。当匹配到JobSubmitted事件后。调用DAGScheduler的handleJobSubmitted函数并传入jobid、rdd等參数来处理Job。

handleJobSubmitted函数

Job处理过程中handleJobSubmitted比較关键，该函数主要负责RDD的依赖性分析。生成finalStage，并依据finalStage来产生ActiveJob。

在handleJobSubmitted函数源代码中。给出了部分凝视：

  private[scheduler] def handleJobSubmitted(jobId: Int,
      finalRDD: RDD[_],
      func: (TaskContext, Iterator[_]) => _,
      partitions: Array[Int],
      allowLocal: Boolean,
      callSite: CallSite,
      listener: JobListener,
      properties: Properties) {
    var finalStage: Stage = null
    try {
      // New stage creation may throw an exception if, for example, jobs are run on a
      // HadoopRDD whose underlying HDFS files have been deleted.
      finalStage = newStage(finalRDD, partitions.size, None, jobId, callSite)
    } catch {
      //错误处理，告诉监听器作业失败。返回....
      case e: Exception =>
        logWarning("Creating new stage failed due to exception - job: " + jobId, e)
        listener.jobFailed(e)
        return
    }
    if (finalStage != null) {
      val job = new ActiveJob(jobId, finalStage, func, partitions, callSite, listener, properties)
      clearCacheLocs()
      logInfo("Got job %s (%s) with %d output partitions (allowLocal=%s)".format(
        job.jobId, callSite.shortForm, partitions.length, allowLocal))
      logInfo("Final stage: " + finalStage + "(" + finalStage.name + ")")
      logInfo("Parents of final stage: " + finalStage.parents)
      logInfo("Missing parents: " + getMissingParentStages(finalStage))
      val shouldRunLocally =
        localExecutionEnabled && allowLocal && finalStage.parents.isEmpty && partitions.length == 1
      val jobSubmissionTime = clock.getTimeMillis()
      if (shouldRunLocally) {
        // 非常短、没有父stage的本地操作，比方 first() or take() 的操作本地运行
        // Compute very short actions like first() or take() with no parent stages locally.
        listenerBus.post(
          SparkListenerJobStart(job.jobId, jobSubmissionTime, Seq.empty, properties))
        runLocally(job)
      } else {
        // collect等操作走的是这个过程。更新相关的关系映射，用监听器监听，然后提交作业
        jobIdToActiveJob(jobId) = job
        activeJobs += job
        finalStage.resultOfJob = Some(job)
        val stageIds = jobIdToStageIds(jobId).toArray
        val stageInfos = stageIds.flatMap(id => stageIdToStage.get(id).map(_.latestInfo))
        listenerBus.post(
          SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos, properties))
        // 提交stage
        submitStage(finalStage)
      }
    }
    // 提交stage
    submitWaitingStages()
  }

小结

该篇文章介绍了DAGScheduler从SparkContext中进行实例化，到运行Action操作时提交任务调用runJob函数，进而介绍了提交任务的消息调度，和处理Job函数handleJobSubmitted函数。

因为在handleJobSubmitted函数中涉及到依赖性分析和stage的源代码内容，于是我计划在下一篇文章里进行介绍和源代码分析。

转载请注明作者Jason Ding及其出处

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