Spark2.4.0源码分析之WorldCount 任务调度器(七)

源码分析之WorldCount 任务调度器(七)"/>

Spark2.4.0源码分析之WorldCount 任务调度器(七)

Spark2.4.0源码分析之WorldCount 任务调度器(七)

更多资源

• github: .4.0

时序图

• .4.0/blob/master/md/image/example/spark-sql-dataset/worldCount/worldCount.taskScheduler.jpg
  

主要内容描述

• 理解TaskSet是如何提交到任务调度器池，任务集如何被调度
• 理解Worker可用资源算法，Worker可用资源分配任务调度池中的任务
• 任务发送给executor去执行

程序

TaskSchedulerImpl.submitTasks

• 任务调度器，处理任务集
• 将任务集转化成TaskSetManager,因为TaskSetManager继承Schedulable，调度池中放的元素为Schedulable,调度池来调度任务，所以需要将TaskSet转化成可调度的对象TaskSetManager

val manager = createTaskSetManager(taskSet, maxTaskFailures)

  // Label as private[scheduler] to allow tests to swap in different task set managers if necessaryprivate[scheduler] def createTaskSetManager(taskSet: TaskSet,maxTaskFailures: Int): TaskSetManager = {new TaskSetManager(this, taskSet, maxTaskFailures, blacklistTrackerOpt)}

• TaskSetManager加到调度池中，供任务调度器调度，也就是由高度池决定，TaskSet里边的任务什么时候被调用
• SparkContext对象构建时，已经构建了默认的FIFO调度模式，就是先进先出，先来的先开始调度

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

• 15秒后开始执行，如果hasLaunchedTask = true,说明任务调度器已经分配当前TaskSet中的任务，发送给Executor去执行
• hasLaunchedTask = false,说明15秒后，当前TaskSet中的任务还没有发送给Executor去执行，说明没有可用的资源分配，所以任务调度器才没有把任务分配出去，所以就进行集群没有可用的资源分配的提示

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}

• StandaloneSchedulerBackend.reviveOffers()调度，StandaloneSchedulerBackend没有重写reviveOffers()函数，所以调用CoarseGrainedSchedulerBackend.reviveOffers

 backend.reviveOffers()

• TaskSchedulerImpl.submitTasks函数

override def submitTasks(taskSet: TaskSet) {val tasks = taskSet.taskslogInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks")this.synchronized {val manager = createTaskSetManager(taskSet, maxTaskFailures)val stage = taskSet.stageIdval stageTaskSets =taskSetsByStageIdAndAttempt.getOrElseUpdate(stage, new HashMap[Int, TaskSetManager])stageTaskSets(taskSet.stageAttemptId) = managerval 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()}

CoarseGrainedSchedulerBackend.reviveOffers

• 给Driver发送消息:ReviveOffers
• DriverEndpoint.receive()函数会接收消息，进行消息类型匹配，匹配上后就进行处理

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

CoarseGrainedSchedulerBackend.DriverEndpoint.recieve

• DriverEndpoint.receive()接收到消息:ReviveOffers
• 调用CoarseGrainedSchedulerBackend.DriverEndpoint.makeOffers()函数，来计算可用的资源，去分配任务

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_TASKmakeOffers(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")}}case ReviveOffers =>makeOffers()case KillTask(taskId, executorId, interruptThread, reason) =>executorDataMap.get(executorId) match {case Some(executorInfo) =>executorInfo.executorEndpoint.send(KillTask(taskId, executorId, interruptThread, reason))case None =>// Ignoring the task kill since the executor is not registered.logWarning(s"Attempted to kill task $taskId for unknown executor $executorId.")}case KillExecutorsOnHost(host) =>scheduler.getExecutorsAliveOnHost(host).foreach { exec =>killExecutors(exec.toSeq, adjustTargetNumExecutors = false, countFailures = false,force = true)}case UpdateDelegationTokens(newDelegationTokens) =>executorDataMap.values.foreach { ed =>ed.executorEndpoint.send(UpdateDelegationTokens(newDelegationTokens))}case RemoveExecutor(executorId, reason) =>// We will remove the executor's state and cannot restore it. However, the connection// between the driver and the executor may be still alive so that the executor won't exit// automatically, so try to tell the executor to stop itself. See SPARK-13519.executorDataMap.get(executorId).foreach(_.executorEndpoint.send(StopExecutor))removeExecutor(executorId, reason)}

CoarseGrainedSchedulerBackend.DriverEndpoint.makeOffers()

• 过滤有效的executor

 val activeExecutors = executorDataMap.filterKeys(executorIsAlive)

• 有效的executor计算可用的Worker资源

 val workOffers = activeExecutors.map {case (id, executorData) =>new WorkerOffer(id, executorData.executorHost, executorData.freeCores,Some(executorData.executorAddress.hostPort))}.toIndexedSeq

• scheduler.resourceOffers(workOffers),调度器为TaskSchedulerImpl，该函数内部执行，在可用的worker上去分配任务，会返回待分配的任务
• CoarseGrainedSchedulerBackend.DriverEndpoint
  .launchTasks()函数，会给executor去发送消息:LaunchTask,Executor收到该消息，会去启动该任务，并运行，相当于执行该任务

    // Make fake resource offers on all executorsprivate def makeOffers() {// Make sure no executor is killed while some task is launching on itval taskDescs = CoarseGrainedSchedulerBackend.this.synchronized {// Filter out executors under killingval activeExecutors = executorDataMap.filterKeys(executorIsAlive)val workOffers = activeExecutors.map {case (id, executorData) =>new WorkerOffer(id, executorData.executorHost, executorData.freeCores,Some(executorData.executorAddress.hostPort))}.toIndexedSeqscheduler.resourceOffers(workOffers)}if (!taskDescs.isEmpty) {launchTasks(taskDescs)}}

TaskSchedulerImpl.resourceOffers

• 对worker资源进行黑名单过滤

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

• 对worker资源进行打散，使所有的worker都更能均匀的分配到任务

val shuffledOffers = shuffleOffers(filteredOffers)

• 计算worker上还剩多少可用的cpu core

val availableCpus = shuffledOffers.map(o => o.cores).toArray

• 从任务调度池中取出已排好序的所有的可调度元素(TaskSetManager)

val sortedTaskSets = rootPool.getSortedTaskSetQueue

• 用的默认FIFO调度算法，先来的任务先分配

  override def getSortedTaskSetQueue: ArrayBuffer[TaskSetManager] = {val sortedTaskSetQueue = new ArrayBuffer[TaskSetManager]val sortedSchedulableQueue =schedulableQueue.asScala.toSeq.sortWith(taskSetSchedulingAlgorithmparator)for (schedulable <- sortedSchedulableQueue) {sortedTaskSetQueue ++= schedulable.getSortedTaskSetQueue}sortedTaskSetQueue}

• 返回对象 Vector(ArrayBuffer,ArrayBuffer),理解为，每台worker分配几个任务，这个时修还没有开始分配，只是先实例化对象

 val tasks = shuffledOffers.map(o => new ArrayBuffer[TaskDescription](o.cores / CPUS_PER_TASK))

• 循环分配TaskSet中的任务给tasks变量，分配任务的规则,遍历所有可用的worker资源，首先每台worker上分配任务集中的一个任务，如果资源没分配完，会再循环一次，再给可用的worker每台分配一个任务，直至，可用的资源分配完了，或任务集中的任务分配完了，就本次分配完成，把分配好的tasks变量返回出去

 var launchedTaskAtCurrentMaxLocality = falsedo {launchedTaskAtCurrentMaxLocality = resourceOfferSingleTaskSet(taskSet,currentMaxLocality, shuffledOffers, availableCpus, tasks, addressesWithDescs)launchedAnyTask |= launchedTaskAtCurrentMaxLocality} while (launchedTaskAtCurrentMaxLocality)

• TaskSchedulerImpl.resourceOffers函数

 /*** 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 addedvar newExecAvail = falsefor (o <- offers) {if (!hostToExecutors.contains(o.host)) {hostToExecutors(o.host) = new HashSet[String]()}if (!executorIdToRunningTaskIds.contains(o.executorId)) {hostToExecutors(o.host) += o.executorIdexecutorAdded(o.executorId, o.host)executorIdToHost(o.executorId) = o.hostexecutorIdToRunningTaskIds(o.executorId) = HashSet[Long]()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)val shuffledOffers = shuffleOffers(filteredOffers)// Build a list of tasks to assign to each worker.val tasks = shuffledOffers.map(o => new ArrayBuffer[TaskDescription](o.cores / CPUS_PER_TASK))val availableCpus = shuffledOffers.map(o => o.cores).toArrayval availableSlots = shuffledOffers.map(o => o.cores / CPUS_PER_TASK).sumval sortedTaskSets = rootPool.getSortedTaskSetQueuefor (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, ANYfor (taskSet <- sortedTaskSets) {// Skip the barrier taskSet if the available slots are less than the number of pending tasks.if (taskSet.isBarrier && availableSlots < taskSet.numTasks) {// Skip the launch process.// TODO SPARK-24819 If the job requires more slots than available (both busy and free// slots), fail the job on submit.logInfo(s"Skip current round of resource offers for barrier stage ${taskSet.stageId} " +s"because the barrier taskSet requires ${taskSet.numTasks} slots, while the total " +s"number of available slots is $availableSlots.")} else {var launchedAnyTask = false// Record all the executor IDs assigned barrier tasks on.val addressesWithDescs = ArrayBuffer[(String, TaskDescription)]()for (currentMaxLocality <- taskSet.myLocalityLevels) {var launchedTaskAtCurrentMaxLocality = falsedo {launchedTaskAtCurrentMaxLocality = resourceOfferSingleTaskSet(taskSet,currentMaxLocality, shuffledOffers, availableCpus, tasks, addressesWithDescs)launchedAnyTask |= launchedTaskAtCurrentMaxLocality} while (launchedTaskAtCurrentMaxLocality)}if (!launchedAnyTask) {taskSet.abortIfCompletelyBlacklisted(hostToExecutors)}if (launchedAnyTask && taskSet.isBarrier) {// Check whether the barrier tasks are partially launched.// TODO SPARK-24818 handle the assert failure case (that can happen when some locality// requirements are not fulfilled, and we should revert the launched tasks).require(addressesWithDescs.size == taskSet.numTasks,s"Skip current round of resource offers for barrier stage ${taskSet.stageId} " +s"because only ${addressesWithDescs.size} out of a total number of " +s"${taskSet.numTasks} tasks got resource offers. The resource offers may have " +"been blacklisted or cannot fulfill task locality requirements.")// materialize the barrier coordinator.maybeInitBarrierCoordinator()// Update the taskInfos into all the barrier task properties.val addressesStr = addressesWithDescs// Addresses ordered by partitionId.sortBy(_._2.partitionId).map(_._1).mkString(",")addressesWithDescs.foreach(_._2.properties.setProperty("addresses", addressesStr))logInfo(s"Successfully scheduled all the ${addressesWithDescs.size} tasks for barrier " +s"stage ${taskSet.stageId}.")}}}// TODO SPARK-24823 Cancel a job that contains barrier stage(s) if the barrier tasks don't get// launched within a configured time.if (tasks.size > 0) {hasLaunchedTask = true}return tasks}

TaskSchedulerImpl.resourceOfferSingleTaskSet

• 遍历所有的可用worker资源，进行TaskSet中的任务分配，每个worker分配一个任务，分配完后，返回，如果还可以继续分配，下次循环再分配，如此，分配完所有的worker可用资源，或者是分配完所有的TaskSet中的任务

 private def resourceOfferSingleTaskSet(taskSet: TaskSetManager,maxLocality: TaskLocality,shuffledOffers: Seq[WorkerOffer],availableCpus: Array[Int],tasks: IndexedSeq[ArrayBuffer[TaskDescription]],addressesWithDescs: ArrayBuffer[(String, TaskDescription)]) : Boolean = {var launchedTask = false// nodes and executors that are blacklisted for the entire application have already been// filtered out by this pointfor (i <- 0 until shuffledOffers.size) {val execId = shuffledOffers(i).executorIdval host = shuffledOffers(i).hostif (availableCpus(i) >= CPUS_PER_TASK) {try {for (task <- taskSet.resourceOffer(execId, host, maxLocality)) {tasks(i) += taskval tid = task.taskIdtaskIdToTaskSetManager.put(tid, taskSet)taskIdToExecutorId(tid) = execIdexecutorIdToRunningTaskIds(execId).add(tid)availableCpus(i) -= CPUS_PER_TASKassert(availableCpus(i) >= 0)// Only update hosts for a barrier task.if (taskSet.isBarrier) {// The executor address is expected to be non empty.addressesWithDescs += (shuffledOffers(i).address.get -> task)}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}

CoarseGrainedSchedulerBackend.DriverEndpoint.launchTasks

• 循环所有的任务，依次把任务发送给executor执行
• 到这里任务集转化成TaskSetManager做为可调度元素，经调度器默认FIFO算法调度，对worker上的可用资源分配任务，把任务分配给executor上去执行，任务调度器任务调度的流程已完成

// Launch tasks returned by a set of resource offersprivate def launchTasks(tasks: Seq[Seq[TaskDescription]]) {for (task <- tasks.flatten) {val serializedTask = TaskDescription.encode(task)if (serializedTask.limit() >= maxRpcMessageSize) {Option(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_TASKlogDebug(s"Launching task ${task.taskId} on executor id: ${task.executorId} hostname: " +s"${executorData.executorHost}.")executorData.executorEndpoint.send(LaunchTask(new SerializableBuffer(serializedTask)))}}}

end