[spark] TaskScheduler 任务提交与调度源码解析

在DAGScheduler划分为Stage并以TaskSet的形式提交给TaskScheduler后,再由TaskScheduler通过TaskSetMagager对taskSet的task进行调度与执行。

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

submitTasks方法的实现在TaskScheduler的实现类TaskSchedulerImpl中。先看整个实现:

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()
  }
val manager = createTaskSetManager(taskSet, maxTaskFailures)

先为当前TaskSet创建TaskSetManager,TaskSetManager负责管理一个单独taskSet的每一个task,决定某个task是否在一个executor上启动,如果task失败,负责重试task直到task重试次数,并通过延迟调度来执行task的位置感知调度。

val stageTaskSets =
        taskSetsByStageIdAndAttempt.getOrElseUpdate(stage, new HashMap[Int, TaskSetManager])
      stageTaskSets(taskSet.stageAttemptId) = manager

key为stageId,value为一个HashMap,其中key为stageAttemptId,value为TaskSet。

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(",")}")
      }

isZombie是TaskSetManager中所有tasks是否不需要执行(成功执行或者stage被删除)的一个标记,如果该TaskSet没有被完全执行并且已经存在和新进来的taskset一样的另一个TaskSet,则抛出异常,确保一个stage不能有两个taskSet同时运行。

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

将当前taskSet添加到调度池中,schedulableBuilder的类型是SchedulerBuilder的一个trait,有两个实现FIFOSchedulerBuilder和 FairSchedulerBuilder,并且默认采用的是FIFO方式。

schedulableBuilder是SparkContext 中newTaskSchedulerImpl(sc)在创建TaskSchedulerImpl的时候通过scheduler.initialize(backend)的initialize方法对schedulableBuilder进行了实例化。

def initialize(backend: SchedulerBackend) {
    this.backend = backend
    // temporarily set rootPool name to empty
    rootPool = new Pool("", schedulingMode, 0, 0)
    schedulableBuilder = {
      schedulingMode match {
        case SchedulingMode.FIFO =>
          new FIFOSchedulableBuilder(rootPool)
        case SchedulingMode.FAIR =>
          new FairSchedulableBuilder(rootPool, conf)
        case _ =>
          throw new IllegalArgumentException(s"Unsupported spark.scheduler.mode: $schedulingMode")
      }
    }
    schedulableBuilder.buildPools()
  }
backend.reviveOffers()

接下来调用了SchedulerBackend的riviveOffers方法向schedulerBackend申请资源。backend也是通过scheduler.initialize(backend)的参数传递过来的,具体是在SparkContext 中被创建的。

val (sched, ts) = SparkContext.createTaskScheduler(this, master, deployMode)

回到向schedulerBackend申请资源, 调用CoarseGrainedSchedulerBackend的reviveOffers方法,该方法给driverEndpoint发送ReviveOffer消息。

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

driverEndpoint收到ReviveOffer消息后调用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))
    }

该方法先过滤出活跃的executor并封装成WorkerOffer,WorkerOffer包含executorId、host、可用的cores三个信息。这里的executorDataMap是HashMap[String, ExecutorData]类型,key为executorId,value为对应executor的信息,包括host、RPC信息、totalCores、freeCores。

在客户端向Master注册Application的时候,Master已经为Application分配并启动好Executor,然后注册给CoarseGrainedSchedulerBackend,注册信息就是存储在executorDataMap数据结构中。

launchTasks(scheduler.resourceOffers(workOffers))

先看里面的scheduler.resourceOffers(workOffers),TaskSchedulerImpl调用resourceOffers方法通过准备好的资源获得要被执行的Seq[TaskDescription],交给CoarseGrainedSchedulerBackend分发到各个executor上执行。下面看具体实现:

def resourceOffers(offers: Seq[WorkerOffer]): Seq[Seq[TaskDescription]] = synchronized {
    //标记是否有新的executor加入
    var newExecAvail = false
    // 更新executor,host,rack信息
    for (o <- offers) {
      executorIdToHost(o.executorId) = o.host
      executorIdToTaskCount.getOrElseUpdate(o.executorId, 0)
      if (!executorsByHost.contains(o.host)) {
        executorsByHost(o.host) = new HashSet[String]()
        executorAdded(o.executorId, o.host)
        newExecAvail = true
      }
      for (rack <- getRackForHost(o.host)) {
        hostsByRack.getOrElseUpdate(rack, new HashSet[String]()) += o.host
      }
    }

    // 随机打乱offers,避免多个task集中分配到某些节点上,为了负载均衡
    val shuffledOffers = Random.shuffle(offers)
    // 建一个二维数组,保存每个Executor上将要分配的那些task
    val tasks = shuffledOffers.map(o => new ArrayBuffer[TaskDescription](o.cores))
    //每个executor上可用的cores
    val availableCpus = shuffledOffers.map(o => o.cores).toArray
    //返回排序过的TaskSet队列,有FIFO及Fair两种排序规则,默认为FIFO
    val sortedTaskSets = rootPool.getSortedTaskSetQueue
    for (taskSet <- sortedTaskSets) {
      logDebug("parentName: %s, name: %s, runningTasks: %s".format(
        taskSet.parent.name, taskSet.name, taskSet.runningTasks))
      if (newExecAvail) { // 如果该executor是新分配来的
        taskSet.executorAdded() // 重新计算TaskSetManager的就近原则
      }
    }

    // 利用双重循环对每一个taskSet依照调度的顺序,依次按照本地性级别顺序尝试启动task
    // 根据taskSet及locality遍历所有可用的executor,找出可以在各个executor上启动的task,
    // 加到tasks:Seq[Seq[TaskDescription]]中
    // 数据本地性级别顺序:PROCESS_LOCAL, NODE_LOCAL, NO_PREF, RACK_LOCAL, ANY
    var launchedTask = false
    for (taskSet <- sortedTaskSets; maxLocality <- taskSet.myLocalityLevels) {
      do {
       //将计算资源按照就近原则分配给taskSet,用于执行其中的task
        launchedTask = resourceOfferSingleTaskSet(
            taskSet, maxLocality, shuffledOffers, availableCpus, tasks)
      } while (launchedTask)
    }

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

跟进resourceOfferSingleTaskSet方法:

private def resourceOfferSingleTaskSet(
      taskSet: TaskSetManager,
      maxLocality: TaskLocality,
      shuffledOffers: Seq[WorkerOffer],
      availableCpus: Array[Int],
      tasks: Seq[ArrayBuffer[TaskDescription]]) : Boolean = {
    var launchedTask = false
    //遍历所有executor
    for (i <- 0 until shuffledOffers.size) {
      val execId = shuffledOffers(i).executorId
      val host = shuffledOffers(i).host
      //若当前executor可用的core数满足一个task所需的core数
      if (availableCpus(i) >= CPUS_PER_TASK) {
        try {
          //获取taskSet哪些task可以在该executor上启动
          for (task <- taskSet.resourceOffer(execId, host, maxLocality)) {
            //将需要在该executor启动的task添加到tasks中
            tasks(i) += task 
            val tid = task.taskId 
            taskIdToTaskSetManager(tid) = taskSet // task -> taskSetManager
            taskIdToExecutorId(tid) = execId // task -> executorId
            executorIdToTaskCount(execId) += 1 //该executor上的task+1
            executorsByHost(host) += execId // host -> executorId
            availableCpus(i) -= CPUS_PER_TASK //该executor上可用core数减去对应task的core数
            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
  }

这个方法主要是遍历所有可用的executor,在core满足一个task所需core的条件下,通过resourceOffer方法获取taskSet能在该executor上启动的task,并添加到tasks中予以返回。下面具体看resourceOffer的实现:

def resourceOffer(
      execId: String,
      host: String,
      maxLocality: TaskLocality.TaskLocality)
    : Option[TaskDescription] =
  {
    if (!isZombie) {
      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) match {
        case Some((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, partition ${task.partitionId}," +
            s" $taskLocality, ${serializedTask.limit} bytes)")

          sched.dagScheduler.taskStarted(task, info)
          return Some(new TaskDescription(taskId = taskId, attemptNumber = attemptNum, execId,
            taskName, index, serializedTask))
        case _ =>
      }
    }
    None
  }
 if (maxLocality != TaskLocality.NO_PREF) {
        allowedLocality = getAllowedLocalityLevel(curTime)
        if (allowedLocality > maxLocality) {
          // We're not allowed to search for farther-away tasks
          allowedLocality = maxLocality
        }
      }

getAllowedLocalityLevel(curTime)会根据延迟调度调整合适的Locality,目的都是尽可能的以最好的locality来启动每一个task,getAllowedLocalityLevel返回的是当前taskSet中所有未执行的task的最高locality,以该locality作为本次调度能容忍的最差locality,在后续的搜索中只搜索本地性比这个级别好的情况。allowedLocality 最终取以getAllowedLocalityLevel(curTime)返回的locality和maxLocality中级别较高的locality。

根据allowedLocality寻找合适的task,若返回不为空,则说明在该executor上分配了task,然后进行信息跟新,将taskid加入到runningTask中,跟新延迟调度信息,序列化task,通知DAGScheduler,最后返回taskDescription,我们来看看dequeueTask的实现:

private def dequeueTask(execId: String, host: String, maxLocality: TaskLocality.Value)
    : Option[(Int, TaskLocality.Value, Boolean)] =
  {
    for (index <- dequeueTaskFromList(execId, getPendingTasksForExecutor(execId))) {
      return Some((index, TaskLocality.PROCESS_LOCAL, false))
    }

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

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

    if (TaskLocality.isAllowed(maxLocality, TaskLocality.ANY)) {
      for (index <- dequeueTaskFromList(execId, 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)}
  }

首先看是否存在execId对应的PROCESS_LOCAL类别的任务,如果存在,取出来调度,如果不存在,只在比allowedLocality大或者等于的级别上去查看是否存在execId对应类别的任务,若有则调度。

其中的dequeueTaskFromList是从execId对应类别(如PROCESS_LOCAL)的任务列表中尾部取出一个task返回其在taskSet中的taskIndex,跟进该方法:

private def dequeueTaskFromList(execId: String, list: ArrayBuffer[Int]): Option[Int] = {
    var indexOffset = list.size
    while (indexOffset > 0) {
      indexOffset -= 1
      val index = list(indexOffset)
      if (!executorIsBlacklisted(execId, index)) {
        // This should almost always be list.trimEnd(1) to remove tail
        list.remove(indexOffset)
        if (copiesRunning(index) == 0 && !successful(index)) {
          return Some(index)
        }
      }
    }
    None
  }

这里有个黑名单机制,利用executorIsBlacklisted方法查看该executor是否属于task的黑名单,黑名单记录task上一次失败所在的Executor Id和Host,以及其对应的“黑暗”时间,“黑暗”时间是指这段时间内不要再往这个节点上调度这个Task了。

private def executorIsBlacklisted(execId: String, taskId: Int): Boolean = {
    if (failedExecutors.contains(taskId)) {
      val failed = failedExecutors.get(taskId).get
      return failed.contains(execId) &&
        clock.getTimeMillis() - failed.get(execId).get < EXECUTOR_TASK_BLACKLIST_TIMEOUT
    }
    false
  }

可以看到在dequeueTask方法的最后一段代码:

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

这里是启动推测执行,推测任务是指对一个Task在不同的Executor上启动多个实例,如果有Task实例运行成功,则会干掉其他Executor上运行的实例,只会对运行慢的任务启动推测任务。

通过scheduler.resourceOffers(workOffers)方法返回了在哪些executor上启动哪些task的Seq[Seq[TaskDescription]]信息后,将调用launchTasks来启动各个task,实现如下:

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)))
        }
      }
    }

先将task进行序列化, 如果当前task序列化后的大小超过了128MB-200KB,跳过当前task,并把对应的taskSetManager置为zombie模式,若大小不超过限制,则发送消息到executor启动task执行。

本文参与腾讯云自媒体分享计划,欢迎正在阅读的你也加入,一起分享。

发表于

我来说两句

0 条评论
登录 后参与评论

相关文章

来自专栏XAI

微信公众号发送模板消息 Java实现。

本博文是测试公众号调用模板接口测试。请不要完全复制我的代码。里面的测试代码中有本人测试号的微信模板id。麻烦替换成自己的可以吗? 第一步:创建模板信息 ? ? ...

1.4K110
来自专栏函数式编程语言及工具

ScalaPB(2): 在scala中用gRPC实现微服务

32130
来自专栏mini188

学习AOP之深入一点Spring Aop

上一篇《学习AOP之认识一下SpringAOP》中大体的了解了代理、动态代理及SpringAop的知识。因为写的篇幅长了点所以还是再写一篇吧。接下来开始深入一点...

20990
来自专栏岑玉海

Carbondata源码系列(一)文件生成过程

在滴滴的两年一直在加班,人也变懒了,就很少再写博客了,最近在进行Carbondata和hive集成方面的工作,于是乎需要对Carbondata进行深入的研究。 ...

66660
来自专栏大内老A

通过一个模拟程序让你明白ASP.NET MVC是如何运行的

ASP.NET MVC的路由系统通过对HTTP请求的解析得到表示Controller、Action和其他相关的数据,并以此为依据激活Controller对象,调...

30160
来自专栏Android学习之路

Android中关于UUID的操作

67760
来自专栏DOTNET

asp.net web api 下载之断点续传

一、基本思想 利用 HTTP 请求的Range标头值,来向服务端传递请求数据的开始位置和结束位置。服务端获得这两个参数后,将指定范围内的数据传递给客户端。当客户...

490120
来自专栏java技术学习之道

Spring AOP原理分析一次看懂

18240
来自专栏Spark生态圈

[spark] Task成功执行的结果处理

在文章Task执行流程 中介绍了task是怎么被分配到executor上执行的,本文讲解task成功执行时将结果返回给driver的处理流程。

26140
来自专栏Spark生态圈

[spark] 数据本地化及延迟调度

Spark数据本地化即移动计算而不是移动数据,而现实又是残酷的,不是想要在数据块的地方计算就有足够的资源提供,为了让task能尽可能的以最优本地化级别(Loca...

20120

扫码关注云+社区

领取腾讯云代金券