Spark 源码阅读——任务提交过程

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当我们在使用spark编写mr作业是，最后都要涉及到调用reduce,foreach或者是count这类action来触发作业的提交，所以，当我们查看这些方法的源码时，发现底层都调用了SparkContext的runJob方法，而SparkContext的runJob方法又调用的DAGScheduler的runJob方法：

def runJob[T, U: ClassTag](
  rdd: RDD[T],
  func: (TaskContext, Iterator[T]) => U,
  partitions: Seq[Int],
    resultHandler: (Int, U) => Unit): Unit = {
  if (stopped.get()) {
    throw new IllegalStateException("SparkContext has been shutdown")
  }
  val callSite = getCallSite
  val cleanedFunc = clean(func)
  logInfo("Starting job: " + callSite.shortForm)
  if (conf.getBoolean("spark.logLineage", false)) {
    logInfo("RDD's recursive dependencies:\n" + rdd.toDebugString)
  }
  dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, esultHandler, localProperties.get)
  progressBar.foreach(_.finishAll())
  rdd.doCheckpoint()
}

这里以rdd和分区信息和对结果集处理的回调函数为参数进入到：

  def runJob[T, U](
      rdd: RDD[T],
      func: (TaskContext, Iterator[T]) => U,
      partitions: Seq[Int],
      callSite: CallSite,
      resultHandler: (Int, U) => Unit,
      properties: Properties): Unit = {
    val start = System.nanoTime
    val waiter = submitJob(rdd, func, partitions, callSite, resultHandler, properties)
    // Note: Do not call Await.ready(future) because that calls `scala.concurrent.blocking`,
    // which causes concurrent SQL executions to fail if a fork-join pool is used. Note that
    // due to idiosyncrasies in Scala, `awaitPermission` is not actually used anywhere so it's
    // safe to pass in null here. For more detail, see SPARK-13747.
    val awaitPermission = null.asInstanceOf[scala.concurrent.CanAwait]
    waiter.completionFuture.ready(Duration.Inf)(awaitPermission)
    waiter.completionFuture.value.get match {
      case scala.util.Success(_) =>
        logInfo("Job %d finished: %s, took %f s".format
          (waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
      case scala.util.Failure(exception) =>
        logInfo("Job %d failed: %s, took %f s".format
          (waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
        // SPARK-8644: Include user stack trace in exceptions coming from DAGScheduler.
        val callerStackTrace = Thread.currentThread().getStackTrace.tail
        exception.setStackTrace(exception.getStackTrace ++ callerStackTrace)
        throw exception
    }
  }

然后调用submitJob方法：

  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] = {
    // 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 immediately if the job is running 0 tasks
      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, callSite, waiter,
      SerializationUtils.clone(properties)))
    waiter
  }

我们注意到里面有一行eventProcessLoop.post(JobSubmitted(...))的代码，这是向消息队列中放入一个作业提交的消息，由另一个线程来循环从队列中取出消息消费，执行相应的逻辑。我们可以看到在DAGScheduler类定义的最后一行调用了eventProcessLoop.start方法来启动这个时间循环线程。 在另一个线程中，通过scala的case class模式匹配并执行了DAGScheduler的handleJobSubmitted方法，这是一个比较核心的方法，所有生成stage，以及stage之间的依赖关系解析，作业的生成，都是在这里完成的。

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 {
      // New stage creation may throw an exception if, for example, jobs are run on a
      // HadoopRDD whose underlying HDFS files have been deleted.
      finalStage = newResultStage(finalRDD, func, partitions, jobId, callSite)
    } catch {
      case e: Exception =>
        logWarning("Creating new stage failed due to exception - job: " + jobId, e)
        listener.jobFailed(e)
        return
    }

    val job = new ActiveJob(jobId, finalStage, callSite, listener, properties)
    clearCacheLocs()
    logInfo("Got job %s (%s) with %d output partitions".format(
      job.jobId, callSite.shortForm, partitions.length))
    logInfo("Final stage: " + finalStage + " (" + finalStage.name + ")")
    logInfo("Parents of final stage: " + finalStage.parents)
    logInfo("Missing parents: " + getMissingParentStages(finalStage))

    val jobSubmissionTime = clock.getTimeMillis()
    jobIdToActiveJob(jobId) = job
    activeJobs += job
    finalStage.setActiveJob(job)
    val stageIds = jobIdToStageIds(jobId).toArray
    val stageInfos = stageIds.flatMap(id => stageIdToStage.get(id).map(_.latestInfo))
    listenerBus.post(
      SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos, properties))
    submitStage(finalStage)

    submitWaitingStages()
  }

首先是在

finalStage = newResultStage(finalRDD, func, partitions, jobId, callSite)

这里将stage之间的依赖关系解析出来，同时根据依赖关系从小到大生成stage id。

  private def newResultStage(
      rdd: RDD[_],
      func: (TaskContext, Iterator[_]) => _,
      partitions: Array[Int],
      jobId: Int,
      callSite: CallSite): ResultStage = {
    val (parentStages: List[Stage], id: Int) = getParentStagesAndId(rdd, jobId)
    val stage = new ResultStage(id, rdd, func, partitions, parentStages, jobId, callSite)
    stageIdToStage(id) = stage
    updateJobIdStageIdMaps(jobId, stage)
    stage
  }

getParentStagesAndId：

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

  private def getParentStages(rdd: RDD[_], firstJobId: Int): List[Stage] = {
    val parents = 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(r: RDD[_]) {
      if (!visited(r)) {
        visited += r
        // Kind of ugly: need to register RDDs with the cache here since
        // we can't do it in its constructor because # of partitions is unknown
        for (dep <- r.dependencies) {
          dep match {
            case shufDep: ShuffleDependency[_, _, _] =>
              parents += getShuffleMapStage(shufDep, firstJobId)
            case _ =>
              waitingForVisit.push(dep.rdd)
          }
        }
      }
    }
    waitingForVisit.push(rdd)
    while (waitingForVisit.nonEmpty) {
      visit(waitingForVisit.pop())
    }
    parents.toList
  }

可以看到这里使用栈结构深度依次遍历了每一个rdd的所有依赖，如果是shuffle dependency则生成shuffle stage，其他的依赖则先放到栈里，再依次遍历。这里在生成shuffleMapStage的过程中又会递归的调用getParentStagesAndId方法，所以最后生成的finalStage是一个处于依赖树最顶端的包含其所有依赖的子依赖树的结构，stage id的生成从依赖链最底端，从小到大生成。

之后以finalStage为参数调用submitStage来提交作业，但是在提交的过程中，它会依次递归的解析和提交每个stage所依赖的父stage，最终最先提交的是没有任何依赖的stage。

  private def submitStage(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)
        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)
    }
  }

通过submitMissingTasks提交stage的所有task。在submitMissingTasks方法中， 首先计算task的分发策略，

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: $e\n${Utils.exceptionString(e)}", Some(e))
        runningStages -= stage
        return
    }

然后序列化task，

    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 =>
          JavaUtils.bufferToArray(
            closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef))
        case stage: ResultStage =>
          JavaUtils.bufferToArray(closureSerializer.serialize((stage.rdd, stage.func): AnyRef))
      }

将序列化后的task广播出去，

  taskBinary = sc.broadcast(taskBinaryBytes)

然后将tasks信息封装成task对象数组，

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.latestInfo.taskMetrics, properties)
          }

        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, properties, stage.latestInfo.taskMetrics)
          }
      }
    } catch {
      case NonFatal(e) =>
        abortStage(stage, s"Task creation failed: $e\n${Utils.exceptionString(e)}", Some(e))
        runningStages -= stage
        return
    }

调用taskScheduler提交task集合

  taskScheduler.submitTasks(new TaskSet(
        tasks.toArray, stage.id, stage.latestInfo.attemptId, jobId, properties))

这个方法里主要是将taskSet交给TaskSetManager去管理，另外比较关键的是调用了schedulableBuilder中的addTaskSetManager，SchedulableBuilder本身是应用程序级别的调度器，它自己支持两种调度模式，一种是FIFO，另一种是FAIR，调度策略可以通过spark-env.sh中的spark.scheduler.mode进行具体的设置，默认情况下是FIFO。最后在submitTasks中调用了

  backend.reviveOffers()

这里调用了CoarseGrainedSchedulerBackend.reviveOffers给driverEndpoint发送了一个ReviveOffers case object，这个消息其实是发给driverEndpoint自己的（详情见sparkde RpcEnv模块），也就是说最后处理的这个消息的还是driverEndpoint本身。这里会触发driverEndpoint的recieve方法然后路由到makeOffers方法。

    private def makeOffers() {
      // Filter out executors under killing
      val activeExecutors = executorDataMap.filterKeys(executorIsAlive)
      val workOffers = activeExecutors.map { case (id, executorData) =>
        new WorkerOffer(id, executorData.executorHost, executorData.freeCores)
      }.toSeq
      launchTasks(scheduler.resourceOffers(workOffers))
    }

在makeOffers方法中，首先准备好所有可以用于计算的Executor，然后找出可以的workOffers(代表了所有可用ExecutorBackend中可以使用的CPU Cores信息）WorkerOffer会告我们具体Executor可用的资源。而确定task具体运行在哪个ExecutorBackend上的算法是有TaskSetManager的resourceOffers方法决定的，具体算法我们后续讨论。再通过调用launchTask把任务发送给ExecutorBackend去执行。代码如下：

    private def launchTasks(tasks: Seq[Seq[TaskDescription]]) {
      for (task <- tasks.flatten) {
        val serializedTask = ser.serialize(task)
        if (serializedTask.limit >= maxRpcMessageSize) {
          scheduler.taskIdToTaskSetManager.get(task.taskId).foreach { taskSetMgr =>
            try {
              var msg = "Serialized task %s:%d was %d bytes, which exceeds max allowed: " +
                "spark.rpc.message.maxSize (%d bytes). Consider increasing " +
                "spark.rpc.message.maxSize or using broadcast variables for large values."
              msg = msg.format(task.taskId, task.index, serializedTask.limit, maxRpcMessageSize)
              taskSetMgr.abort(msg)
            } catch {
              case e: Exception => logError("Exception in error callback", e)
            }
          }
        }
        else {
          val executorData = executorDataMap(task.executorId)
          executorData.freeCores -= scheduler.CPUS_PER_TASK

          logInfo(s"Launching task ${task.taskId} on executor id: ${task.executorId} hostname: " +
            s"${executorData.executorHost}.")

          executorData.executorEndpoint.send(LaunchTask(new SerializableBuffer(serializedTask)))
        }
      }
    }

可以看到这里有将序列化后的task发送给executor的逻辑，所以整体的提交作业到这里就结束了

出自：https://my.oschina.net/nalenwind/blog/1786172