Task提交

在上一节中的 Stage提交中我们提到，最终stage被封装成TaskSet，使用taskScheduler.submitTasks提交，具体代码如下：

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

Stage由一系列的tasks组成，这些task被封装成TaskSet，TaskSet类定义如下：

/** * A set of tasks submitted together to the low-level TaskScheduler, usually representing * missing partitions of a particular stage. */ private[spark] class TaskSet( val tasks: Array[Task[_]], val stageId: Int, val stageAttemptId: Int, val priority: Int, val properties: Properties) { val id: String = stageId + "." + stageAttemptId override def toString: String = "TaskSet " + id }

submitTasks方法定义在TaskScheduler Trait当中，目前TaskScheduler 只有一个子类TaskSchedulerImpl，其submitTasks方法源码如下：

//TaskSchedulerImpl类中的submitTasks方法 override def submitTasks(taskSet: TaskSet) { val tasks = taskSet.tasks logInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks") this.synchronized { //创建TaskSetManager，TaskSetManager用于对TaskSet中的Task进行调度，包括跟踪Task的运行、Task失败重试等 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中添加TaskSetManager，用于完成所有TaskSet的调度，即整个Spark程序生成的DAG图对应Stage的TaskSet调度 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 } //为Task分配运行资源 backend.reviveOffers() }

SchedulerBackend有多种实现，如下图所示： 我们以SparkDeploySchedulerBackend为例进行说明，SparkDeploySchedulerBackend继承自CoarseGrainedSchedulerBackend中的reviveOffers方法，具有代码如下：

//CoarseGrainedSchedulerBackend中定义的reviveOffers方法 override def reviveOffers() { //driverEndpoint发送ReviveOffers消息，由DriverEndPoint接受处理 driverEndpoint.send(ReviveOffers) }

driverEndpoint的类型是RpcEndpointRef

//CoarseGrainedSchedulerBackend中的成员变量driverEndpoint var driverEndpoint: RpcEndpointRef = null

它具有如下定义形式：

//RpcEndpointRef是远程RpcEndpoint的引用，它是一个抽象类，有一个子类AkkaRpcEndpointRef /** * A reference for a remote [[RpcEndpoint]]. [[RpcEndpointRef]] is thread-safe. */ private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf) extends Serializable with Logging //在底层采用的是Akka进行实现 private[akka] class AkkaRpcEndpointRef( @transient defaultAddress: RpcAddress, @transient _actorRef: => ActorRef, @transient conf: SparkConf, @transient initInConstructor: Boolean = true) extends RpcEndpointRef(conf) with Logging { lazy val actorRef = _actorRef override lazy val address: RpcAddress = { val akkaAddress = actorRef.path.address RpcAddress(akkaAddress.host.getOrElse(defaultAddress.host), akkaAddress.port.getOrElse(defaultAddress.port)) } override lazy val name: String = actorRef.path.name private[akka] def init(): Unit = { // Initialize the lazy vals actorRef address name } if (initInConstructor) { init() } override def send(message: Any): Unit = { actorRef ! AkkaMessage(message, false) } //其它代码省略

DriverEndpoint中的receive方法接收driverEndpoint.send(ReviveOffers)发来的消息，DriverEndpoint继承了ThreadSafeRpcEndpoint trait，具体如下：

class DriverEndpoint(override val rpcEnv: RpcEnv, sparkProperties: Seq[(String, String)]) extends ThreadSafeRpcEndpoint with Logging

ThreadSafeRpcEndpoint 继承 RpcEndpoint trait，RpcEndpoint对receive方法进行了描述，具体如下：

/** * Process messages from [[RpcEndpointRef.send]] or [[RpcCallContext.reply)]]. If receiving a * unmatched message, [[SparkException]] will be thrown and sent to `onError`. */ def receive: PartialFunction[Any, Unit] = { case _ => throw new SparkException(self + " does not implement 'receive'") }

DriverEndpoint 中的对其receive方法进行了重写，具体实现如下：

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") } } //重要！处理发送来的ReviveOffers消息 case ReviveOffers => makeOffers() case KillTask(taskId, executorId, interruptThread) => executorDataMap.get(executorId) match { 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.") } }

从上面的代码可以看到，处理ReviveOffers消息时，调用的是makeOffers方法

// Make fake resource offers on all executors private def makeOffers() { // Filter out executors under killing //所有可用的Executor val activeExecutors = executorDataMap.filterKeys(!executorsPendingToRemove.contains(_)) //WorkOffer表示Executor上可用的资源， val workOffers = activeExecutors.map { case (id, executorData) => new WorkerOffer(id, executorData.executorHost, executorData.freeCores) }.toSeq //先调用TaskSchedulerImpl的resourceOffers方法，为Task的运行分配资源 //再调用CoarseGrainedSchedulerBackend中的launchTasks方法启动Task的运行，最终Task被提交到Worker节点上的Executor上运行 launchTasks(scheduler.resourceOffers(workOffers)) }

上面的代码逻辑全部是在Driver端进行的，调用完launchTasks方法后，Task的执行便在Worker节点上运行了，至此完成Task的提交。 关于resourceOffers方法及launchTasks方法的具体内容，在后续章节中将进行进一步的解析。