Spark TaskSchedulerImpl TaskSet处理

视频分享

Spark TaskSchedulerImpl TaskSet原理分析(bilibili视频) : https://www.bilibili.com/video/av37442139/?p=20
Spark TaskSchedulerImpl TaskSet原码分析(bilibili视频) : https://www.bilibili.com/video/av37442139/?p=21

图解

TaskSchedulerImpl提交任务集

在DAGScheduler.scal中件中的submitMissingTasks()方法中调用 taskScheduler.submitTasks
把任务集通过任务调度器进行提交

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

任务调度器实现

override def submitTasks(taskSet: TaskSet) {
    val tasks = taskSet.tasks
    logInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks")
    this.synchronized {
      val manager = createTaskSetManager(taskSet, maxTaskFailures)
      val stage = taskSet.stageId
      val stageTaskSets =
        taskSetsByStageIdAndAttempt.getOrElseUpdate(stage, new HashMap[Int, TaskSetManager])
      stageTaskSets(taskSet.stageAttemptId) = manager
      val conflictingTaskSet = stageTaskSets.exists { case (_, ts) =>
        ts.taskSet != taskSet && !ts.isZombie
      }
      if (conflictingTaskSet) {
        throw new IllegalStateException(s"more than one active taskSet for stage $stage:" +
          s" ${stageTaskSets.toSeq.map{_._2.taskSet.id}.mkString(",")}")
      }
      schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties)

      if (!isLocal && !hasReceivedTask) {
        starvationTimer.scheduleAtFixedRate(new TimerTask() {
          override def run() {
            if (!hasLaunchedTask) {
              logWarning("Initial job has not accepted any resources; " +
                "check your cluster UI to ensure that workers are registered " +
                "and have sufficient resources")
            } else {
              this.cancel()
            }
          }
        }, STARVATION_TIMEOUT_MS, STARVATION_TIMEOUT_MS)
      }
      hasReceivedTask = true
    }
    backend.reviveOffers()
  }

把任务集放到TaskSetManager(任务集管理器)中
TaskSetManager(任务集管理器)继承 Schedulable，（可调度元素，就是把到调度池队列中的一个元素，供调度使用）

val manager = createTaskSetManager(taskSet, maxTaskFailures)

把任务集管理器增加到指定调度类型(FIFO,PAIR)的调度池中，也就是调度池中的调度队列中schedulableQueue
此时，相当于需要调度的任务已有了，存放在调度池中，下面是用具体的调度算法，按指定的顺序调度池中的任务

schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties)

任务调度器的submitTasks()方法中调用 backend.reviveOffers()方法,backend为SparkDeploySchedulerBackend,继承CoarseGrainedSchedulerBackend,所以调用的是CoarseGrainedSchedulerBackend中的reviveOffers()方法

 backend.reviveOffers()

相当于是给Driver发送消息ReviveOffers

   override def reviveOffers() {
    driverEndpoint.send(ReviveOffers)
  }

driverEndpoint 中receive()方法处理消息,调用makeOffers()方法

     case ReviveOffers =>
        makeOffers()

scheduler.resourceOffers(workOffers)会计算出需要启动的任务序列
resourceOffers()方法中调用方法得到调度任务的队列（按指定顺序的） rootPool.getSortedTaskSetQueue()
launchTasks()方法把启动任务消息发送给executor

   // Make fake resource offers on all executors
    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)
      }.toIndexedSeq
      launchTasks(scheduler.resourceOffers(workOffers))
    }

计算当前stage转换的TaskSet中的部分任务，发送执行任务的消息executor处理

/**
   * Called by cluster manager to offer resources on slaves. We respond by asking our active task
   * sets for tasks in order of priority. We fill each node with tasks in a round-robin manner so
   * that tasks are balanced across the cluster.
   */
  def resourceOffers(offers: IndexedSeq[WorkerOffer]): Seq[Seq[TaskDescription]] = synchronized {
    // Mark each slave as alive and remember its hostname
    // Also track if new executor is added
    var newExecAvail = false
    for (o <- offers) {
      if (!hostToExecutors.contains(o.host)) {
        hostToExecutors(o.host) = new HashSet[String]()
      }
      if (!executorIdToTaskCount.contains(o.executorId)) {
        hostToExecutors(o.host) += o.executorId
        executorAdded(o.executorId, o.host)
        executorIdToHost(o.executorId) = o.host
        executorIdToTaskCount(o.executorId) = 0
        newExecAvail = true
      }
      for (rack <- getRackForHost(o.host)) {
        hostsByRack.getOrElseUpdate(rack, new HashSet[String]()) += o.host
      }
    }

    // Before making any offers, remove any nodes from the blacklist whose blacklist has expired. Do
    // this here to avoid a separate thread and added synchronization overhead, and also because
    // updating the blacklist is only relevant when task offers are being made.
    blacklistTrackerOpt.foreach(_.applyBlacklistTimeout())

    val filteredOffers = blacklistTrackerOpt.map { blacklistTracker =>
      offers.filter { offer =>
        !blacklistTracker.isNodeBlacklisted(offer.host) &&
          !blacklistTracker.isExecutorBlacklisted(offer.executorId)
      }
    }.getOrElse(offers)

    // Randomly shuffle offers to avoid always placing tasks on the same set of workers.
    val shuffledOffers = Random.shuffle(filteredOffers)
    // Build a list of tasks to assign to each worker.
    val tasks = shuffledOffers.map(o => new ArrayBuffer[TaskDescription](o.cores))
    val availableCpus = shuffledOffers.map(o => o.cores).toArray
    val sortedTaskSets = rootPool.getSortedTaskSetQueue
    for (taskSet <- sortedTaskSets) {
      logDebug("parentName: %s, name: %s, runningTasks: %s".format(
        taskSet.parent.name, taskSet.name, taskSet.runningTasks))
      if (newExecAvail) {
        taskSet.executorAdded()
      }
    }

    // Take each TaskSet in our scheduling order, and then offer it each node in increasing order
    // of locality levels so that it gets a chance to launch local tasks on all of them.
    // NOTE: the preferredLocality order: PROCESS_LOCAL, NODE_LOCAL, NO_PREF, RACK_LOCAL, ANY
    for (taskSet <- sortedTaskSets) {
      var launchedAnyTask = false
      var launchedTaskAtCurrentMaxLocality = false
      for (currentMaxLocality <- taskSet.myLocalityLevels) {
        do {
          launchedTaskAtCurrentMaxLocality = resourceOfferSingleTaskSet(
            taskSet, currentMaxLocality, shuffledOffers, availableCpus, tasks)
          launchedAnyTask |= launchedTaskAtCurrentMaxLocality
        } while (launchedTaskAtCurrentMaxLocality)
      }
      if (!launchedAnyTask) {
        taskSet.abortIfCompletelyBlacklisted(hostToExecutors)
      }
    }

    if (tasks.size > 0) {
      hasLaunchedTask = true
    }
    return tasks
  }

TaskSchedulerImpl中方法resourceOffers()中调用 resourceOfferSingleTaskSet()方法
就算看当前的任务集中任务，按worker机器的cpu内核数进行分配每次发送几个任务给executor进行启动
例: stage中TaskSet包含的任务个数是3个，worker 机器的cpu内核数为2，此时就需要把TastSet中的任务3个，拆分成两次，每一次是2个任务，第二次是1个任务，并行任务按cpu内核最大数来决定

for (taskSet <- sortedTaskSets) {
      var launchedAnyTask = false
      var launchedTaskAtCurrentMaxLocality = false
      for (currentMaxLocality <- taskSet.myLocalityLevels) {
        do {
          launchedTaskAtCurrentMaxLocality = resourceOfferSingleTaskSet(
            taskSet, currentMaxLocality, shuffledOffers, availableCpus, tasks)
          launchedAnyTask |= launchedTaskAtCurrentMaxLocality
        } while (launchedTaskAtCurrentMaxLocality)
      }
      if (!launchedAnyTask) {
        taskSet.abortIfCompletelyBlacklisted(hostToExecutors)
      }
    }

看具体的方法 taskSet.resourceOffer().从当前stage的taskSet还剩下未处理的任务中，取出worker机器分配的cpu数取新的任务，发送给executor执行

private def resourceOfferSingleTaskSet(
      taskSet: TaskSetManager,
      maxLocality: TaskLocality,
      shuffledOffers: Seq[WorkerOffer],
      availableCpus: Array[Int],
      tasks: IndexedSeq[ArrayBuffer[TaskDescription]]) : Boolean = {
    var launchedTask = false
    // nodes and executors that are blacklisted for the entire application have already been
    // filtered out by this point
    for (i <- 0 until shuffledOffers.size) {
      val execId = shuffledOffers(i).executorId
      val host = shuffledOffers(i).host
      if (availableCpus(i) >= CPUS_PER_TASK) {
        try {
          for (task <- taskSet.resourceOffer(execId, host, maxLocality)) {
            tasks(i) += task
            val tid = task.taskId
            taskIdToTaskSetManager(tid) = taskSet
            taskIdToExecutorId(tid) = execId
            executorIdToTaskCount(execId) += 1
            availableCpus(i) -= CPUS_PER_TASK
            assert(availableCpus(i) >= 0)
            launchedTask = true
          }
        } catch {
          case e: TaskNotSerializableException =>
            logError(s"Resource offer failed, task set ${taskSet.name} was not serializable")
            // Do not offer resources for this task, but don't throw an error to allow other
            // task sets to be submitted.
            return launchedTask
        }
      }
    }
    return launchedTask
  }

该方法重点看 dequeueTask()方法

 /**
   * Respond to an offer of a single executor from the scheduler by finding a task
   *
   * NOTE: this function is either called with a maxLocality which
   * would be adjusted by delay scheduling algorithm or it will be with a special
   * NO_PREF locality which will be not modified
   *
   * @param execId the executor Id of the offered resource
   * @param host  the host Id of the offered resource
   * @param maxLocality the maximum locality we want to schedule the tasks at
   */
  @throws[TaskNotSerializableException]
  def resourceOffer(
      execId: String,
      host: String,
      maxLocality: TaskLocality.TaskLocality)
    : Option[TaskDescription] =
  {
    val offerBlacklisted = taskSetBlacklistHelperOpt.exists { blacklist =>
      blacklist.isNodeBlacklistedForTaskSet(host) ||
        blacklist.isExecutorBlacklistedForTaskSet(execId)
    }
    if (!isZombie && !offerBlacklisted) {
      val curTime = clock.getTimeMillis()

      var allowedLocality = maxLocality

      if (maxLocality != TaskLocality.NO_PREF) {
        allowedLocality = getAllowedLocalityLevel(curTime)
        if (allowedLocality > maxLocality) {
          // We're not allowed to search for farther-away tasks
          allowedLocality = maxLocality
        }
      }

      dequeueTask(execId, host, allowedLocality).map { case ((index, taskLocality, speculative)) =>
        // Found a task; do some bookkeeping and return a task description
        val task = tasks(index)
        val taskId = sched.newTaskId()
        // Do various bookkeeping
        copiesRunning(index) += 1
        val attemptNum = taskAttempts(index).size
        val info = new TaskInfo(taskId, index, attemptNum, curTime,
          execId, host, taskLocality, speculative)
        taskInfos(taskId) = info
        taskAttempts(index) = info :: taskAttempts(index)
        // Update our locality level for delay scheduling
        // NO_PREF will not affect the variables related to delay scheduling
        if (maxLocality != TaskLocality.NO_PREF) {
          currentLocalityIndex = getLocalityIndex(taskLocality)
          lastLaunchTime = curTime
        }
        // Serialize and return the task
        val startTime = clock.getTimeMillis()
        val serializedTask: ByteBuffer = try {
          Task.serializeWithDependencies(task, sched.sc.addedFiles, sched.sc.addedJars, ser)
        } catch {
          // If the task cannot be serialized, then there's no point to re-attempt the task,
          // as it will always fail. So just abort the whole task-set.
          case NonFatal(e) =>
            val msg = s"Failed to serialize task $taskId, not attempting to retry it."
            logError(msg, e)
            abort(s"$msg Exception during serialization: $e")
            throw new TaskNotSerializableException(e)
        }
        if (serializedTask.limit > TaskSetManager.TASK_SIZE_TO_WARN_KB * 1024 &&
          !emittedTaskSizeWarning) {
          emittedTaskSizeWarning = true
          logWarning(s"Stage ${task.stageId} contains a task of very large size " +
            s"(${serializedTask.limit / 1024} KB). The maximum recommended task size is " +
            s"${TaskSetManager.TASK_SIZE_TO_WARN_KB} KB.")
        }
        addRunningTask(taskId)

        // We used to log the time it takes to serialize the task, but task size is already
        // a good proxy to task serialization time.
        // val timeTaken = clock.getTime() - startTime
        val taskName = s"task ${info.id} in stage ${taskSet.id}"
        logInfo(s"Starting $taskName (TID $taskId, $host, executor ${info.executorId}, " +
          s"partition ${task.partitionId}, $taskLocality, ${serializedTask.limit} bytes)")

        sched.dagScheduler.taskStarted(task, info)
        new TaskDescription(taskId = taskId, attemptNumber = attemptNum, execId,
          taskName, index, serializedTask)
      }
    } else {
      None
    }
  }

该方法重点看 TaskLocality.isAllowed(maxLocality, TaskLocality.ANY)
allPendingTasks中的数据是在TaskSetManager实例方法中调用，并且会按反序增加(......2,1,0)
调用 dequeueTaskFromList方法，移除最后一个任务，也就是任务集索引中排在最前的任务

  for (i <- (0 until numTasks).reverse) {
    addPendingTask(i)
  }

 /**
   * Dequeue a pending task for a given node and return its index and locality level.
   * Only search for tasks matching the given locality constraint.
   *
   * @return An option containing (task index within the task set, locality, is speculative?)
   */
  private def dequeueTask(execId: String, host: String, maxLocality: TaskLocality.Value)
    : Option[(Int, TaskLocality.Value, Boolean)] =
  {
    for (index <- dequeueTaskFromList(execId, host, getPendingTasksForExecutor(execId))) {
      return Some((index, TaskLocality.PROCESS_LOCAL, false))
    }

    if (TaskLocality.isAllowed(maxLocality, TaskLocality.NODE_LOCAL)) {
      for (index <- dequeueTaskFromList(execId, host, getPendingTasksForHost(host))) {
        return Some((index, TaskLocality.NODE_LOCAL, false))
      }
    }

    if (TaskLocality.isAllowed(maxLocality, TaskLocality.NO_PREF)) {
      // Look for noPref tasks after NODE_LOCAL for minimize cross-rack traffic
      for (index <- dequeueTaskFromList(execId, host, pendingTasksWithNoPrefs)) {
        return Some((index, TaskLocality.PROCESS_LOCAL, false))
      }
    }

    if (TaskLocality.isAllowed(maxLocality, TaskLocality.RACK_LOCAL)) {
      for {
        rack <- sched.getRackForHost(host)
        index <- dequeueTaskFromList(execId, host, getPendingTasksForRack(rack))
      } {
        return Some((index, TaskLocality.RACK_LOCAL, false))
      }
    }

    if (TaskLocality.isAllowed(maxLocality, TaskLocality.ANY)) {
      for (index <- dequeueTaskFromList(execId, host, allPendingTasks)) {
        return Some((index, TaskLocality.ANY, false))
      }
    }

    // find a speculative task if all others tasks have been scheduled
    dequeueSpeculativeTask(execId, host, maxLocality).map {
      case (taskIndex, allowedLocality) => (taskIndex, allowedLocality, true)}
  }

判断当前stage的TaskSet中是示还有未被处理的Task，如果还有就继续找出来发送给Executor执行

  /**
   * Dequeue a pending task from the given list and return its index.
   * Return None if the list is empty.
   * This method also cleans up any tasks in the list that have already
   * been launched, since we want that to happen lazily.
   */
  private def dequeueTaskFromList(
      execId: String,
      host: String,
      list: ArrayBuffer[Int]): Option[Int] = {
    var indexOffset = list.size
    while (indexOffset > 0) {
      indexOffset -= 1
      val index = list(indexOffset)
      if (!isTaskBlacklistedOnExecOrNode(index, execId, host)) {
        // This should almost always be list.trimEnd(1) to remove tail
        list.remove(indexOffset)
        if (copiesRunning(index) == 0 && !successful(index)) {
          return Some(index)
        }
      }
    }
    None
  }

TaskSet中的任务发送给Executor消息LaunchTask

粗粒度调度器调用启动任务的方法
给executor 发送消息 LaunchTask()

// Launch tasks returned by a set of resource offers
    private def launchTasks(tasks: Seq[Seq[TaskDescription]]) {
      for (task <- tasks.flatten) {
        val serializedTask = ser.serialize(task)
        if (serializedTask.limit >= akkaFrameSize - AkkaUtils.reservedSizeBytes) {
          scheduler.taskIdToTaskSetManager.get(task.taskId).foreach { taskSetMgr =>
            try {
              var msg = "Serialized task %s:%d was %d bytes, which exceeds max allowed: " +
                "spark.akka.frameSize (%d bytes) - reserved (%d bytes). Consider increasing " +
                "spark.akka.frameSize or using broadcast variables for large values."
              msg = msg.format(task.taskId, task.index, serializedTask.limit, akkaFrameSize,
                AkkaUtils.reservedSizeBytes)
              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
          executorData.executorEndpoint.send(LaunchTask(new SerializableBuffer(serializedTask)))
        }
      }
    }

executor

CoarseGrainedExecutorBackend 收到消息: LaunchTask()
receive() 消息处理

    case LaunchTask(data) =>
      if (executor == null) {
        exitExecutor(1, "Received LaunchTask command but executor was null")
      } else {
        val taskDesc = ser.deserialize[TaskDescription](data.value)
        logInfo("Got assigned task " + taskDesc.taskId)
        executor.launchTask(this, taskId = taskDesc.taskId, attemptNumber = taskDesc.attemptNumber,
          taskDesc.name, taskDesc.serializedTask)
      }

executor 通过线程池调用 TaskRunner
TaskRunner的run()会被调用

  def launchTask(
      context: ExecutorBackend,
      taskId: Long,
      attemptNumber: Int,
      taskName: String,
      serializedTask: ByteBuffer): Unit = {
    val tr = new TaskRunner(context, taskId = taskId, attemptNumber = attemptNumber, taskName,
      serializedTask)
    runningTasks.put(taskId, tr)
    threadPool.execute(tr)
  }

launchTask 的run()执行后会调用 statusUpdate()方法，发送任务状态为已完成
CoarseGrainedExecutorBacker中的 statusUpdate()方法会给Driver发送消息StatusUpdate（）

execBackend.statusUpdate(taskId, TaskState.FINISHED, serializedResult)

  override def statusUpdate(taskId: Long, state: TaskState, data: ByteBuffer) {
    val msg = StatusUpdate(executorId, taskId, state, data)
    driver match {
      case Some(driverRef) => driverRef.send(msg)
      case None => logWarning(s"Drop $msg because has not yet connected to driver")
    }
  }

反向推，调度池中的调度任务如何移除

Pool 中有removeScheduler()方法

该方法被调用 -> TaskSchedulerImpl中 taskSetFinished()

  override def removeSchedulable(schedulable: Schedulable) {
    schedulableQueue.remove(schedulable)
    schedulableNameToSchedulable.remove(schedulable.name)
  }

TaskSchedulerImpl中 taskSetFinished()

该方法被调用 -> TaskSetManager.maybeFinishTaskSet()

  /**
   * Called to indicate that all task attempts (including speculated tasks) associated with the
   * given TaskSetManager have completed, so state associated with the TaskSetManager should be
   * cleaned up.
   */
  def taskSetFinished(manager: TaskSetManager): Unit = synchronized {
    taskSetsByStageIdAndAttempt.get(manager.taskSet.stageId).foreach { taskSetsForStage =>
      taskSetsForStage -= manager.taskSet.stageAttemptId
      if (taskSetsForStage.isEmpty) {
        taskSetsByStageIdAndAttempt -= manager.taskSet.stageId
      }
    }
    manager.parent.removeSchedulable(manager)
    logInfo(s"Removed TaskSet ${manager.taskSet.id}, whose tasks have all completed, from pool" +
      s" ${manager.parent.name}")
  }

TaskSetManager.maybeFinishTaskSet()

runningTasks 为0时才被调用，说明此时任务都已经运行完了
&& 条件(tasksSuccessful == numTasks) 即，所有的任务都运行成功才说明是整个任务集已完成,TaskSetManager.handleSuccessfulTask()方法中，每一个任务完成成功后，会tasksSuccessful += 1
该方法被调用 TaskSetManager.handleSuccessfulTask()

  private def maybeFinishTaskSet() {
    if (isZombie && runningTasks == 0) {
      sched.taskSetFinished(this)
      if (tasksSuccessful == numTasks) {
        blacklistTracker.foreach(_.updateBlacklistForSuccessfulTaskSet(
          taskSet.stageId,
          taskSet.stageAttemptId,
          taskSetBlacklistHelperOpt.get.execToFailures))
      }
    }
  }

TaskSetManager.handleSuccessfulTask()

被TaskSetGetter.enqueueSuccessfulTask()调用
TaskSetGetter.enqueueSuccessfulTask()被TaskSchedulerImpl.statusUpdate()方法调用
executor 在执行完任务后，触发发送消息: StatusUpdate

/**
   * Marks the task as successful and notifies the DAGScheduler that a task has ended.
   */
  def handleSuccessfulTask(tid: Long, result: DirectTaskResult[_]): Unit = {
    val info = taskInfos(tid)
    val index = info.index
    info.markSuccessful()
    removeRunningTask(tid)
    // This method is called by "TaskSchedulerImpl.handleSuccessfulTask" which holds the
    // "TaskSchedulerImpl" lock until exiting. To avoid the SPARK-7655 issue, we should not
    // "deserialize" the value when holding a lock to avoid blocking other threads. So we call
    // "result.value()" in "TaskResultGetter.enqueueSuccessfulTask" before reaching here.
    // Note: "result.value()" only deserializes the value when it's called at the first time, so
    // here "result.value()" just returns the value and won't block other threads.
    sched.dagScheduler.taskEnded(
      tasks(index), Success, result.value(), result.accumUpdates, info, result.metrics)
    if (!successful(index)) {
      tasksSuccessful += 1
      logInfo(s"Finished task ${info.id} in stage ${taskSet.id} (TID ${info.taskId}) in" +
        s" ${info.duration} ms on ${info.host} (executor ${info.executorId})" +
        s" ($tasksSuccessful/$numTasks)")
      // Mark successful and stop if all the tasks have succeeded.
      successful(index) = true
      if (tasksSuccessful == numTasks) {
        isZombie = true
      }
    } else {
      logInfo("Ignoring task-finished event for " + info.id + " in stage " + taskSet.id +
        " because task " + index + " has already completed successfully")
    }
    maybeFinishTaskSet()
  }

粗粒度后端调度器处理消息StatusUpdate

CoarseGrainedSchedulerBackend.DriverEndpoint的receive()方法中处理消息:StatusUpdate()
调用方法 scheduler.statusUpdate(taskId, state, data.value)

override def receive: PartialFunction[Any, Unit] = {
      case StatusUpdate(executorId, taskId, state, data) =>
        scheduler.statusUpdate(taskId, state, data.value)
        if (TaskState.isFinished(state)) {
          executorDataMap.get(executorId) match {
            case Some(executorInfo) =>
              executorInfo.freeCores += scheduler.CPUS_PER_TASK
              makeOffers(executorId)
            case None =>
              // Ignoring the update since we don't know about the executor.
              logWarning(s"Ignored task status update ($taskId state $state) " +
                s"from unknown executor with ID $executorId")
          }
        }

def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) {
    var failedExecutor: Option[String] = None
    synchronized {
      try {
        if (state == TaskState.LOST && taskIdToExecutorId.contains(tid)) {
          // We lost this entire executor, so remember that it's gone
          val execId = taskIdToExecutorId(tid)

          if (executorIdToTaskCount.contains(execId)) {
            removeExecutor(execId,
              SlaveLost(s"Task $tid was lost, so marking the executor as lost as well."))
            failedExecutor = Some(execId)
          }
        }
        taskIdToTaskSetManager.get(tid) match {
          case Some(taskSet) =>
            if (TaskState.isFinished(state)) {
              taskIdToTaskSetManager.remove(tid)
              taskIdToExecutorId.remove(tid).foreach { execId =>
                if (executorIdToTaskCount.contains(execId)) {
                  executorIdToTaskCount(execId) -= 1
                }
              }
            }
            if (state == TaskState.FINISHED) {
              taskSet.removeRunningTask(tid)
              taskResultGetter.enqueueSuccessfulTask(taskSet, tid, serializedData)
            } else if (Set(TaskState.FAILED, TaskState.KILLED, TaskState.LOST).contains(state)) {
              taskSet.removeRunningTask(tid)
              taskResultGetter.enqueueFailedTask(taskSet, tid, state, serializedData)
            }
          case None =>
            logError(
              ("Ignoring update with state %s for TID %s because its task set is gone (this is " +
                "likely the result of receiving duplicate task finished status updates)")
                .format(state, tid))
        }
      } catch {
        case e: Exception => logError("Exception in statusUpdate", e)
      }
    }
    // Update the DAGScheduler without holding a lock on this, since that can deadlock
    if (failedExecutor.isDefined) {
      dagScheduler.executorLost(failedExecutor.get)
      backend.reviveOffers()
    }
  }

thinktothings

Spark TaskSchedulerImpl TaskSet处理

Spark TaskSchedulerImpl TaskSet处理

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图解

TaskSchedulerImpl提交任务集

TaskSet中的任务发送给Executor消息LaunchTask

executor

反向推，调度池中的调度任务如何移除

Pool 中有removeScheduler()方法

TaskSchedulerImpl中 taskSetFinished()

TaskSetManager.maybeFinishTaskSet()

TaskSetManager.handleSuccessfulTask()

粗粒度后端调度器处理消息StatusUpdate