Spark standalone启动和submit源码分析

Master

org.apache.spark.deploy.master.Master

main -> startSystemAndActor 
-> val actor = actorSystem.actorOf(
      Props(classOf[Master], host, boundPort, webUiPort, securityMgr, conf), actorName)
 创建actor master
  -> Master的构造方法和prestart
-> preStart() 
->     context.system.scheduler.schedule(0 millis, WORKER_TIMEOUT millis, self, CheckForWorkerTimeOut)
->  定时向自己发送 CheckForWorkerTimeOut 消息监听 worker 信息
  -> receiveWithLogging -> case CheckForWorkerTimeOut -> timeOutDeadWorkers 移除超时worker

Workder

org.apache.sprak.deploy.worker.Worker
main -> startSystemAndActor	
-> actorSystem.actorOf(Props(classOf[Worker], host, boundPort, webUiPort, cores, memory,
      masterAkkaUrls, systemName, actorName,  workDir, conf, securityMgr), name = actorName)
 创建actor Worker
  -> Worker的构造方法和prestart  -> registerWithMaster()  -> tryRegisterAllMasters 
   -> actor ! RegisterWorker 向master发送注册消息
   ->Master.receiveWithLogging.(case RegisterWorker) -> sender ! RegisteredWorker (向Worker发送注册成功消息)
   ->Worker.receiveWithLogging.(case RegisteredWorker) 
      // 定时发送心跳
     -> context.system.scheduler.schedule(0 millis, HEARTBEAT_MILLIS millis, self, SendHeartbeat)
     case SendHeartbeat =>
      if (connected) {
    
     master ! Heartbeat(workerId) }

spark 任务提交流程

org.apache.spark.deploy.SparkSubmit 
main -> submit -> doRunMain -> runMain
 ->  val mainMethod = mainClass.getMethod("main", new Array[String](0).getClass)
  -> mainMethod.invoke(null, childArgs.toArray)
  反射调用执行用户代码
 object WordCount{
    
    
  //transformation均返回RDD，action无返回？  & 相对于groupbykey 有combine 预聚合
  def main(args: Array[String]): Unit = {
    
    
	  val conf = new SparkConf().setAppName("wc").setMaster("spark://DESKTOP-B8SSFJJ:7077")
	  // SparkContext 实例在SparkSubmit(Driver端执行)
	  val sc = new SparkContext(conf)
}

new SparkContext(conf) ->
 *  spark 提交任务到集群的入口
 *  主构造器
 *  1.调用createSparkEnv创建sparkEnv，里面创建重要对象 ActorSystem
 *     createSparkEnv-> SparkEnv.createDriverEnv -> create ->  AkkaUtils.createActorSystem -> doCreateActorSystem
 *       ->  val actorSystem = ActorSystem(name, akkaConf)
 *  2.创建taskScheduler  根据提交任务的url进行匹配 
 *    SparkContext.createTaskScheduler ->val scheduler = new TaskSchedulerImpl(sc)
 *                                     ->val backend = new SparkDeploySchedulerBackend(scheduler, sc, masterUrls)
 *     SparkDeploySchedulerBackend   里面有AppClient，AppClient里有  ClientActor，
 *     CoarseGrainedSchedulerBackend 里面 有   DriverActor
 *     继承结构: SparkDeploySchedulerBackend extends CoarseGrainedSchedulerBackend extends SchedulerBackend 
 *  3.创建DagScheduler
 *     dagScheduler = new DAGScheduler(this)
 *  4.启动taskScheduler 
 *     taskScheduler.start

sparkContext 创建时序图

taskScheduler启动的时候会创建 DriverActor和ClientActor，
driverActor 与 Executor进行通信，向Executor提交task
clientActor 与 Master通信 向master发送注册应用的请求

taskScheduler.start() -> TaskSchedulerImpl.start -> SparkDeploySchedulerBackEnd.start
-> super.start() -> CoarseGrainedSchedulerBackend.start()
      // TODO 通过actorSystem 创建driverActor
    driverActor = actorSystem.actorOf(
      // TODO 调用DriverActor构造器和 preStart方法
      Props(new DriverActor(properties)), name = CoarseGrainedSchedulerBackend.ACTOR_NAME)
  
  DriverActor -> preStart
      // TODO 周期调用ReviveOffers 查看有没任务提交
      context.system.scheduler.schedule(0.millis, reviveInterval.millis, self, ReviveOffers)

-> SparkDeploySchedulerBackEnd.start(){
    
    
    super.start()
    /**
     *  TODO 重要，这个参数是以后executor的实现类
     *  org.apache.spark.executor.CoarseGrainedExecutorBackend   worker上启动Executor
     */
    val command = Command("org.apache.spark.executor.CoarseGrainedExecutorBackend",
    // 当前执行的application的一些情况
    // 包括application最大需要多少cpucore，每个slave需要多少内存
    // TODO 封装参数到ApplicationDescription
    val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command,
      appUIAddress, sc.eventLogDir, sc.eventLogCodec)

    // TODO 创建AppClient 把 ApplicationDescription 通过主构造器传入
    client = new AppClient(sc.env.actorSystem, masters, appDesc, this, conf)
    // TODO 在这个方法里创建ClientActor用于与master通信
    client.start()
    -> actor = actorSystem.actorOf(Props(new ClientActor))
      -> ClientActor.preStart -> registerWithMaster() -> actor ! RegisterApplication(appDescription)
   
    Master.RegisterApplication => {
    
    
      // 注册application 
      registerApplication(app)
      // master向clientActor发送注册成功的消息
      sender ! RegisteredApplication(app.id, masterUrl)
      // master开始调度资源， 其实就是把任务启动到哪些worker上
      schedule()
    }
      向master注册，并将应用信息发送给master，master将应用信息发送给worker上启动Executor(CoarseGrainedExecutorBackend)

schedule() -> Spark Master 资源调度算法

master发送消息让worker启动Executor

scheduler -> launchExecutor(){
    
    
    /**
     * TODO master发送消息给worker，把参数通过case class传递给worker，让他启动Executor
     */
    worker.actor ! LaunchExecutor(masterUrl,
      exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory)

       /**
     * TODO master向ClientActor发送消息，告诉他Executor启动了
     *     324 行   val app = createApplication(description, sender) 这个sender是clientActor发送过来的
     *     所以这个exec.application.driver 是 ClientActor 而不是DriverActor
     */
    exec.application.driver ! ExecutorAdded(
      exec.id, worker.id, worker.hostPort, exec.cores, exec.memory)
}

worker.actor ! LaunchExecutor -> Worker.LaunchExecutor 
-> val manager = new ExecutorRunner;manager.start() -> fetchAndRunExecutor 
  -> process = builder.start()  // 类似于 process.execute("bin/java  .java")  
     -> org.apache.spark.executor.CoarseGrainedExecutorBackend.main -> run(){
    
    
        // 在Executor里创建ActorSystem
        val (fetcher, _) = AkkaUtils.createActorSystem(
        // 跟driver建立连接
        val driver = fetcher.actorSelection(driverUrl)
		
		// CoarseGrainedExecutorBackend 真正进行通信的Actor
		env.actorSystem.actorOf(
        Props(classOf[CoarseGrainedExecutorBackend],
          driverUrl, executorId, sparkHostPort, cores, userClassPath, env),
        name = "Executor")

      CoarseGrainedExecutorBackend.preStart() 
       // Executor向DriverActor发送RegisterExecutor消息
     -> driver ! RegisterExecutor(executorId, hostPort, cores, extractLogUrls)
     
     DriverActor.RegisterExecutor 
     // driveractor向executor发送消息，告诉executor注册成功
     => sender ! RegisteredExecutor;
        //查看是否任务需要提交 (driveractor提交给executor) 计算
        makeOffers()
     }
     
     CoarseGrainedExecutorBackend.RegisteredExecutor
     // 创建一个Executor实例，用来执行业务逻辑
     => executor = new Executor(executorId, hostname, env, userClassPath, isLocal = false){
    
    
       // 创建线程池
       val threadPool = Utils.newDaemonCachedThreadPool("Executor task launch worker")
       // Executor向driveractor发送心跳
       startDriverHeartbeater
     }
     
       // 向executorrunner线程所属的worker actor，发送ExecutorStateChanged消息
    ->  worker ! ExecutorStateChanged(appId, execId, state, Some(message), Some(exitCode))

Worker.(case ExecutorStateChanged) 
// 直接向master也发送一个ExecutorStateChanged消息
-> master ! ExecutorStateChanged(appId, execId, state, message, exitStatus)

接着继续执行用户代码到action算子触发计算

sc.textFile("/aaa.txt") 
	.flatMap(_.split(" "))  
	.map((_,1)) 
	.reduceByKey(_+_) 
	.saveAsTextFile()

saveAsTextFile(触发action) -> saveAsHadoopFile -> saveAsHadoopDataset -> self.context.runJob
-> dagScheduler.runJob() -> submitJob() 
注：在SparkContext创建 DagScheduler时
private[spark] class DAGScheduler(){
    
    
  private[scheduler] val eventProcessLoop = new DAGSchedulerEventProcessLoop(this)
  eventProcessLoop.start() -> eventThread.start() -> EventLoop.run(){
    
    
    // 事件阻塞队列，有事件进入 通过模式匹配判断事件的类型，来处理对应事件
    private val eventQueue: BlockingQueue[E] = new LinkedBlockingDeque[E]()
    val event = eventQueue.take()
    DAGSchedulerEventProcessLoop.onReceive()
  } 
}
-> dagScheduler.runJob() -> DAGScheduler.submitJob(){
    
    
    /**
     * 先将数据封装到事件JobSubmitted中，然后将事件放入到eventProcessLoop 的阻塞队列中
     * EventLoop  有eventThread 的run方法 一直运行，从队列里取出JobSubmitted 执行 onReceive
     * 这个onReceive 实现类是 DagScheduler.DAGSchedulerEventProcessLoop.onReceive
     */
    eventProcessLoop.post(JobSubmitted(
      jobId, rdd, func2, partitions.toArray, allowLocal, callSite, waiter, properties))
} 
1) stage 划分
  从finalrdd 从后往前 划分stage，最后返回 finalstage，这个stage里有所有的父stage
=> DAGSchedulerEventProcessLoop.onReceive() -> case JobSubmitted -> dagScheduler.handleJobSubmitted(){
    
    
  // 划分stage 创建一个Stage对象，并将stage加入DAGScheduler内部的内存缓存中
  finalStage = newStage(finalRDD, partitions.size, None, jobId, callSite){
    
    
    // 获取父Stage 
    val parentStages = getParentStages(rdd, jobId)
    // 这个是最后那个stage
    val stage = new Stage(id, rdd, numTasks, shuffleDep, parentStages, jobId, callSite)
    stage  // 这个stage就是finalStage
  }
  getParentStages(){
    
    
      val parents = new HashSet[Stage]    // 存放父stage（宽依赖）
      val visited = new HashSet[RDD[_]]   // 
      val waitingForVisit = new Stack[RDD[_]]  // 存放窄依赖的下一个rdd
          def visit(r: RDD[_]) {
    
    
      // visited(r) = HashSet.apply = visited.contains(r)
      if (!visited(r)) {
    
    
        visited += r
        for (dep <- r.dependencies) {
    
    
          dep match {
    
    
            case shufDep: ShuffleDependency[_, _, _] =>
              // TODO 把宽依赖传进去，获得父stage
              parents += getShuffleMapStage(shufDep, jobId)
            case _ =>
              // 窄依赖，父依赖加入waitingForVisit，进入while循环，下次处理
              waitingForVisit.push(dep.rdd)
          }
        }
      }
    }
     // finalRDD 放入stack中
    waitingForVisit.push(rdd)
    /**
     * 循环处理直到waitingForVisit为空
     */
    while (!waitingForVisit.isEmpty) {
    
    
      visit(waitingForVisit.pop())
    }
    // TODO 如果全是窄依赖，就没有父stage，parents为空
    parents.toList      
  }
  
  parents += getShuffleMapStage(shufDep, jobId){
    
    
          val stage =
          // TODO 创建父stage
          newOrUsedStage(shuffleDep.rdd, shuffleDep.rdd.partitions.size, shuffleDep, jobId,
            shuffleDep.rdd.creationSite)
  }
  newOrUsedStage()
   // 递归调用 newStage,从后往前 划分 stage ，并返回 最后那个stage （finalStage）
  => val stage = newStage(rdd, numTasks, Some(shuffleDep), jobId, callSite)
 
  2) stage 提交  从finalStage 往前递归，找到第一个stage，从前往后提交
  submitStage(finalStage)
  private def submitStage(stage: Stage) {
    
    
    val jobId = activeJobForStage(stage)
    if (jobId.isDefined) {
    
    
      logDebug("submitStage(" + stage + ")")
      if (!waitingStages(stage) && !runningStages(stage) && !failedStages(stage)) {
    
    
        
        // TODO -> 调用getMissingParentStages 去获取当前这个stage的父stage
        val missing = getMissingParentStages(stage).sortBy(_.id)
        logDebug("missing: " + missing)

        /**
         * 这里会递归调用，直到最初的stage没有父stage
         *  首先提交第一个stage，  stage0
         *  其余stage，此时全部都在waitingStages里面
         *
         *  递归出口
         *  判断父stage是否为空，为空意味他是第一个stage
         *  从第一个stage开始提交
         */
        if (missing == Nil) {
    
    
          logInfo("Submitting " + stage + " (" + stage.rdd + "), which has no missing parents")
          // TODO 开始提交最前的stage，从前往后提交
          submitMissingTasks(stage, jobId.get)
        } else {
    
    
          /**
           * 递归调用submit方法去提交父stage
           * 这里的递归，就是stage划分算法的推动者和精髓
           *
           * 有父stage ，就递归当前方法
           */
          for (parent <- missing) {
    
    
            submitStage(parent)
          }
          // 将当前stage放入waitingStage等待执行的队列中
          waitingStages += stage
        }
      }
    } else {
    
    
      abortStage(stage, "No active job for stage " + stage.id)
    }
  }
}

提交task

submitMissingTasks() -> taskScheduler.submitTasks -> backend.reviveOffers(){
    
    
   // TODO 向DriverActor发消息
   driverActor ! ReviveOffers
}
-> DriverActor(case ReviveOffers ) -> makeOffers -> launchTasks()
 // 向executor提交task
 executorData.executorActor ! LaunchTask
 -> CoarseGrainedExecutorBackend(case LaunchTask) -> executor.launchTask(){
    
    
     val tr = new TaskRunner
     threadPool.execute(tr)
}-> TaskRunner.run -> task.run -> runTask (ResultTask,SuffleMapTask)
   --> ResultTask.runTask()
      // 调用这个函数  ->rdd.iterator 迭代出数据使用func函数计算
    -> func(context, rdd.iterator(partition, context))