Spark架构原理-TaskScheduler原理剖析

原文地址：https://blog.csdn.net/zhanglh046/article/details/78486051

TaskScheduler是一个接口，DAGScheduler在提交TaskSet给底层调度器的时候是面向接口TaskScheduler。TaskSchduler的核心任务是提交Taskset到集群运算并汇报结果。其执行过程如下图所示：

• 为TaskSet创建和维护一个TaskSetManager并追踪任务的本地性以及错误信息。
• 遇到Straggle任务会放到其他的节点进行重试。
• 向DAGScheduler汇报执行情况，包括在Shuffle输出lost的时候报告fetch failed错误等信息。

在Standalone模式下StandaloneSchedulerBackend在启动的时候构造AppClient实例并在该实例start的时候启动了ClientEndpoint这个消息循环体。ClientEndpoint在启动的时候会向Master注册当前程序。而StandaloneSchedulerBackend的父类CoarseGrainedSchedulerBackend在start的时候会实例化类型为DriverEndpoint（这就是我们程序运行时候的经典对象的Driver）的消息循环体，StandaloneSchedulerBackend专门负责收集Worker上的资源信息，当ExecutorBackend启动的时候会发送RegisteredExecutor信息向DriverEndpoint注册，此时StandaloneSchedulerBackend就掌握了当前应用程序拥有的计算资源，TaskScheduler就是通过StandaloneSchedulerBackend拥有的计算资源来具体运行Task。

一、核心属性

• Int maxTaskFailures: task最多失败次数
• Boolean isLocal： 是否本地运行
• AtomicLong nextTaskId：递增的task id
• SPECULATION_INTERVAL_MS : 多久检查一次推测任务
• CPUS_PER_TASK: 每一个任务需要的cpu核数
• HashMap[Long, TaskSetManager]taskIdToTaskSetManager: 为TaskSet创建和维护一个TaskSetManager并追踪任务的本地性以及错误信息
• HashMap[Long, String] taskIdToExecutorId: 维护的taskId和executorId的映射
• HashMap[String, HashSet[Long]]executorIdToRunningTaskIds:每一个execuotor上运行的task集合的映射
• HashMap[String, HashSet[String]] hostToExecutors: 主机名和executors之间的映射
• HashMap[String, HashSet[String]] hostsByRack：机架和主机名的映射
• HashMap[String, String] executorIdToHost： executorID和主机名映射
• DAGScheduler dagScheduler：
• SchedulerBackend backend：调度器的通信终端
• SchedulableBuilder schedulableBuilder：调度模式，比如FIFO或者Fair
• schedulingModeConf：所配置的调度模式，默认FIFO
• Pool rootPool: 用于调度TaskManager
• TaskResultGetter taskResultGetter: Task结果获取器

二、重要方法

2.1 初始化和启动方法

我们知道，在SparkContext初始化的时候，就会初始化TaskScheduler以及SchedulerBackend，并且会初始化和启动TaskScheduler。

/** SparkContext.scala*/
def initialize(backend:SchedulerBackend) {
  // 初始化SchedulerBackend
  this.backend= backend
  // 创建一个Pool用于调度TasksetManager
  rootPool = new Pool("",schedulingMode, 0, 0)
  // 通过配置的调度模式，构建SchedulableBuilder
  schedulableBuilder= {
    schedulingModematch{
      case SchedulingMode.FIFO=>
        new FIFOSchedulableBuilder(rootPool)
      case SchedulingMode.FAIR=>
        new FairSchedulableBuilder(rootPool,conf)
      case _ =>
        throw new IllegalArgumentException(s"Unsupportedspark.scheduler.mode:$schedulingMode")
    }
  }
  // 开始构建pool
  schedulableBuilder.buildPools()
}

/** TaskScheduler.scala*/
override def start() {
  // 启动SchedulerBackend的start方法，StandaloneSchedulerBackend在
  // 启动的时候构造AppClient实例并在该实例start的时候启动了ClientEndpoint
  // 这个消息循环体。ClientEndpoint在启动的时候会向Master注册当前程序。
  // 父类CoarseGrainedSchedulerBackend在start的时候会实例化类型为DriverEndpoint（这就是我们程序运行时候的经典对象的Driver）
  backend.start()
  // 如果非本地执行，则检查是否需要推测
  if (!isLocal && conf.getBoolean("spark.speculation", false)) {
    logInfo("Starting speculative execution thread")
    // 如果可以推测则调用speculationSchedule定时调度checkSpeculatableTasks方法
    speculationScheduler.scheduleAtFixedRate(new Runnable {
      override def run(): Unit = Utils.tryOrStopSparkContext(sc) {
        checkSpeculatableTasks()
      }
    }, SPECULATION_INTERVAL_MS, SPECULATION_INTERVAL_MS, TimeUnit.MILLISECONDS)
  }
}

2.2 submitTasks 提交task

/** TaskScheduler.scala*/
override def submitTasks(taskSet: TaskSet) {
  // 获取task集合，TaskSet是对Task的封装
  val tasks = taskSet.tasks
  logInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks")
  this.synchronized {
    // 创建TaskSetManager，用于跟踪每一个Task，task失败进行重试等
    val manager = createTaskSetManager(taskSet, maxTaskFailures)
    // 获取该TaskSet所对应的stageId
    val stage = taskSet.stageId
    // 构建一个映射
    val stageTaskSets =
      taskSetsByStageIdAndAttempt.getOrElseUpdate(stage, new HashMap[Int, TaskSetManager])
    // 将这个创建TaskSetManager放入到映射中
    stageTaskSets(taskSet.stageAttemptId) = manager
    // 如果有冲突的TaskSet，则抛异常
    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(",")}")
    }
    // 申请任务调度，有FIFO和FAIR两种策略。根据executor的空闲资源状态
    // 及locality策略将task分配给executor。调度的数据结构封装为Pool类，
    // 对于FIFO，Pool就是TaskSetManager的队列；对于Fair，则是TaskSetManager
    // 组成的树。Pool维护TaskSet的优先级，等待executor接受资源offer(resourceOffer)
    // 的时候出列并提交executor计算
    schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties)
    // 不是本地且没有接收task，启动一个timer定时调度，如果一直没有task就警告，直到有task
    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
  }
  // SchedulerBackend向driver发送ReviveOffers消息
  backend.reviveOffers()
}

/** CoarseGrainedSchedulerBackend.scala（SparkDeploySchedulerBackend的父类）*/
override def reviveOffers() {
    driverEndpoint.send(ReviveOffers)
}

2.3 DriverEndPoint类的主要方法

2.3.1 receive()

override def receive: PartialFunction[Any, Unit] = {
  // 如果接收StatusUpdate消息，用于状态更新
  case StatusUpdate(executorId, taskId, state, data) =>
    // 调用TaskSchedulerImpl#statusUpdate进行更新
    scheduler.statusUpdate(taskId, state, data.value)
    // 如果Task处于完成状态
    if (TaskState.isFinished(state)) {
      // 通过executor id获取ExecutorData
      executorDataMap.get(executorId) match {
        // 如果存在数据
        case Some(executorInfo) =>
          // 则更新executor的cpu核数
          executorInfo.freeCores += scheduler.CPUS_PER_TASK
          // 获取集群中可用的executor列表,发起task
          makeOffers(executorId)
        case None =>
          logWarning(s"Ignored task status update ($taskId state $state) " +
            s"from unknown executor with ID $executorId")
      }
    }
  // 如果发送ReviveOffers消息
  case ReviveOffers =>
    // 获取集群中可用的executor列表,发起task
    makeOffers()
  // 如果是KillTask消息，表示kill掉这个task
  case KillTask(taskId, executorId, interruptThread) =>
    executorDataMap.get(executorId) match {
      // 向Executor发送KillTask的消息
      case Some(executorInfo) =>
        executorInfo.executorEndpoint.send(KillTask(taskId, executorId, interruptThread))
      case None =>
        // Ignoring the task kill since the executor is not registered.
        logWarning(s"Attempted to kill task $taskId for unknown executor $executorId.")
    }
}

2.3.2 receiverAndReply()

override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
  // 接收RegisterExecutor表示向Executor注册
  case RegisterExecutor(executorId, executorRef, hostname, cores, logUrls) =>
    // 如果已经注册过，则会返回RegisterExecutorFailed向executor注册失败的消息
    if (executorDataMap.contains(executorId)) {
      executorRef.send(RegisterExecutorFailed("Duplicate executor ID: " + executorId))
      context.reply(true)
    } else {
      // 获取executor的地址
      val executorAddress = if (executorRef.address != null) {
          executorRef.address
        } else {
          context.senderAddress
        }
      logInfo(s"Registered executor $executorRef ($executorAddress) with ID $executorId")
      // 更新集合
      addressToExecutorId(executorAddress) = executorId
      // 重新计算现在的总的CPU核数
      totalCoreCount.addAndGet(cores)
      // 计算现在已经注册executor数量
      totalRegisteredExecutors.addAndGet(1)
      // 构建一个Executor数据
      val data = new ExecutorData(executorRef, executorRef.address, hostname,
        cores, cores, logUrls)
      // 然后开始注册executor
      CoarseGrainedSchedulerBackend.this.synchronized {
        executorDataMap.put(executorId, data)
        if (currentExecutorIdCounter < executorId.toInt) {
          currentExecutorIdCounter = executorId.toInt
        }
        if (numPendingExecutors > 0) {
          numPendingExecutors -= 1
          logDebug(s"Decremented number of pending executors ($numPendingExecutors left)")
        }
      }
      // 然后返回消息RegisteredExecutor
      executorRef.send(RegisteredExecutor)
      context.reply(true)
      listenerBus.post(
        SparkListenerExecutorAdded(System.currentTimeMillis(), executorId, data))
      // 获取有效的executor，开始发起任务
      makeOffers()
    }
  // 接收StopDriver消息,表示停止Driver
  case StopDriver =>
    context.reply(true)
    stop()
  // 接收StopExecutors消息,表示停止Executor
  case StopExecutors =>
    logInfo("Asking each executor to shut down")
    // 遍历注册所有的executor,然后向Executor终端发送StopExecutor消息
    for ((_, executorData) <- executorDataMap) {
      executorData.executorEndpoint.send(StopExecutor)
    }
    context.reply(true)
  // 接收RemoveExecutor消息,表示删除Executor
  case RemoveExecutor(executorId, reason) =>
    executorDataMap.get(executorId).foreach(_.executorEndpoint.send(StopExecutor))
    removeExecutor(executorId, reason)
    context.reply(true)
  // 接收RetrieveSparkAppConfig消息，表示获取application相关的配置信息
  case RetrieveSparkAppConfig =>
    val reply = SparkAppConfig(sparkProperties,
      SparkEnv.get.securityManager.getIOEncryptionKey())
    context.reply(reply)
}

2.3.3 removeExecutor()

private def removeExecutor(executorId: String, reason: ExecutorLossReason): Unit = {
  logDebug(s"Asked to remove executor $executorId with reason $reason")
  // 获取对应的ExecutorData
  executorDataMap.get(executorId) match {
    case Some(executorInfo) =>
      // 从相关集合或者列表移除该executorId
      val killed = CoarseGrainedSchedulerBackend.this.synchronized {
        addressToExecutorId -= executorInfo.executorAddress
        executorDataMap -= executorId
        executorsPendingLossReason -= executorId
        executorsPendingToRemove.remove(executorId).getOrElse(false)
      }
      // 重新计算CPU核数
      totalCoreCount.addAndGet(-executorInfo.totalCores)
      totalRegisteredExecutors.addAndGet(-1)
      scheduler.executorLost(executorId, if (killed) ExecutorKilled else reason)
      listenerBus.post(
        SparkListenerExecutorRemoved(System.currentTimeMillis(), executorId, reason.toString))
    case None =>
      scheduler.sc.env.blockManager.master.removeExecutorAsync(executorId)
      logInfo(s"Asked to remove non-existent executor $executorId")
  }
}

2.3.4 makeOffers获取有效的executor，开始发起任务

private def makeOffers() {
  val activeExecutors = executorDataMap.filterKeys(executorIsAlive)
  val workOffers = activeExecutors.map { case (id, executorData) =>
    new WorkerOffer(id, executorData.executorHost, executorData.freeCores)
  }.toIndexedSeq
  launchTasks(scheduler.resourceOffers(workOffers))
}

private def makeOffers(executorId: String) {
  // 获取集群中可用的executor列表
  if (executorIsAlive(executorId)) {
    val executorData = executorDataMap(executorId)
    // 创建WorkerOffer，只是表示executor上有可用的空闲资源
    val workOffers = IndexedSeq(
      new WorkerOffer(executorId, executorData.executorHost, executorData.freeCores))
    // 发起task
    launchTasks(scheduler.resourceOffers(workOffers))
  }
}

2.3.5 launchTasks()

// 发起task,会把任务一个个发送到worker节点上的CoarseGrainedExecutorBackend,由其内部的executor来执行
private def launchTasks(tasks: Seq[Seq[TaskDescription]]) {
  for (task <- tasks.flatten) {
    // 将每一个task序列化
    val serializedTask = ser.serialize(task)
    // 检查task序列化之后是否超过所允许的rpc消息的最大值
    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 {
      // 获取对应的ExecutorData数据
      val executorData = executorDataMap(task.executorId)
      // Executor的剩余核数就需要减少一个task需要的cpu核数
      executorData.freeCores -= scheduler.CPUS_PER_TASK

      logDebug(s"Launching task ${task.taskId} on executor id: ${task.executorId} hostname: " +
        s"${executorData.executorHost}.")
      // 然后向Executor终端发送LaunchTask，发起task
      executorData.executorEndpoint.send(LaunchTask(new SerializableBuffer(serializedTask)))
    }
  }
}

2.4 resourceOffers 为executor分配task

计算每一个TaskSetMangaer的本地化级别(locality_level);并且对task set尝试使用最小的本地化级别(locality_level), 将task set的task在executor上启动;如果启动不了，放大本地化级别，以此类推直到某种本地化级别尝试成功。

def resourceOffers(offers:IndexedSeq[WorkerOffer]):Seq[Seq[TaskDescription]] = synchronized {
  // 标记每一个slave是可用的且记住主机名
  var newExecAvail= false
  // 遍历有可用资源的Executor
  for (o <- offers) {
    // 如果没有包含了这个executor的host，初始化一个集合，存放host
    if (!hostToExecutors.contains(o.host)) {
      hostToExecutors(o.host) =new HashSet[String]()
    }
    // 如果不包含这个executorId
    if (!executorIdToRunningTaskIds.contains(o.executorId)) {
      hostToExecutors(o.host)+= o.executorId
      // 通知DAGScheduler添加executor
      executorAdded(o.executorId, o.host)
      executorIdToHost(o.executorId) = o.host
      executorIdToRunningTaskIds(o.executorId) =HashSet[Long]()
      newExecAvail = true
    }
    // 遍历主机所在机架
    for (rack <- getRackForHost(o.host)) {
      // 更新hosts和机架的映射
      hostsByRack.getOrElseUpdate(rack,new HashSet[String]())+= o.host
    }
  }

  // 将WorkerOffer打乱,做到负载均衡
  val shuffledOffers= Random.shuffle(offers)
  // 构建一个task列表，然后分配给每一个worker
  val tasks= shuffledOffers.map(o=> new ArrayBuffer[TaskDescription](o.cores))
  // 有效可用的CPU核数
  val availableCpus= shuffledOffers.map(o=> o.cores).toArray
  // 从调度池获取按照调度策略排序好的TaskSetManager
  val sortedTaskSets= rootPool.getSortedTaskSetQueue
  // 如果有新加入的executor，需要重新计算数据本地性
  for (taskSet <- sortedTaskSets) {
    logDebug("parentName: %s, name: %s, runningTasks: %s".format(
      taskSet.parent.name, taskSet.name, taskSet.runningTasks))
    if (newExecAvail) {
      taskSet.executorAdded()
    }
  }
  // 为排好序的TaskSetManager列表分配资源，分配原则是就近原则，按照顺序为
  // PROCESS_LOCAL, NODE_LOCAL, NO_PREF,RACK_LOCAL, ANY
  for (taskSet <- sortedTaskSets) {
    var launchedAnyTask= false
    var launchedTaskAtCurrentMaxLocality=false
    // 计算每一个TaskSetMangaer的本地化级别(locality_level),
    // 并且对task set尝试使用最小的本地化级别(locality_level),将task set的task在executor上启动
    // 如果启动不了，放大本地化级别，以此类推直到某种本地化级别尝试成功
    for (currentMaxLocality <- taskSet.myLocalityLevels) {
      do {
        launchedTaskAtCurrentMaxLocality= resourceOfferSingleTaskSet(
          taskSet, currentMaxLocality, shuffledOffers,availableCpus, tasks)
        launchedAnyTask|= launchedTaskAtCurrentMaxLocality
      } while (launchedTaskAtCurrentMaxLocality)
    }
    // 如果这个task在任何本地化级别都启动不了，有可能在黑名单
    if (!launchedAnyTask) {
      taskSet.abortIfCompletelyBlacklisted(hostToExecutors)
    }
  }
  if (tasks.size> 0) {
    hasLaunchedTask= true
  }
  return tasks
}

2.5 resourceOfferSingleTaskSet 分配单个TaskSet里的task到executor

调用resourceOffer方法找到在executor上，哪些TaskSet的task可以通过当前本地化级别启动；遍历在该executor上当前本地化级别可以运行的task。

private defresourceOfferSingleTaskSet(
    taskSet: TaskSetManager,
    maxLocality: TaskLocality,
    shuffledOffers: Seq[WorkerOffer],
    availableCpus: Array[Int],
    tasks: IndexedSeq[ArrayBuffer[TaskDescription]]) : Boolean = {
  // 默认发起task为false
  var launchedTask= false
  // 遍历所有executor
  for (i <- 0 until shuffledOffers.size) {
    // 获取executorId和host
    val execId= shuffledOffers(i).executorId
    val host= shuffledOffers(i).host
    // 必须要有每一个task可供分配的的CPU核数，否则直接返回
    if (availableCpus(i) >=CPUS_PER_TASK) {
      try {
        // 调用resourceOffer方法找到在executor上，哪些TaskSet的task可以通过当前本地化级别启动
        // 遍历在该executor上当前本地化级别可以运行的task
        for (task <- taskSet.resourceOffer(execId,host, maxLocality)) {
          // 如果存在，则把每一个task放入要在当前executor运行的task数组里面
          // 即指定executor要运行的task
          tasks(i) += task
          // 将相应的分配信息加入内存缓存
          val tid= task.taskId
          taskIdToTaskSetManager(tid) =taskSet
          taskIdToExecutorId(tid) =execId
          executorIdToRunningTaskIds(execId).add(tid)
          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 notoffer resources for this task, but don't throw an error to allow other
          // task sets to be submitted.
          return launchedTask
      }
    }
  }
  return launchedTask
}