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 + "." + stageAttemptIdoverride 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.taskslogInfo("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.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 = _actorRefoverride lazy val address: RpcAddress = {val akkaAddress = actorRef.path.addressRpcAddress(akkaAddress.host.getOrElse(defaultAddress.host),akkaAddress.port.getOrElse(defaultAddress.port))}override lazy val name: String = actorRef.path.nameprivate[akka] def init(): Unit = {// Initialize the lazy valsactorRefaddressname}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_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")}}//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 executorsprivate def makeOffers() {// Filter out executors under killing//所有可用的Executorval 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。