19、Executor原理剖析与源码分析

一、原理图解

二、源码分析

1、Executor注册机制

worker中为Application启动的executor，实际上是启动了这个CoarseGrainedExecutorBackend进程；


Executor注册机制：
###org.apache.spark.executor/CoarseGrainedExecutorBackend.scala

/**
    * 在actor的初始化方法中
    */
  override def preStart() {
    logInfo("Connecting to driver: " + driverUrl)
    // 获取了driver的executor
    driver = context.actorSelection(driverUrl)
    // 向driver发送RegisterExecutor消息，driver是CoarseGrainedSchedulerBackend的一个内部类
    // driver注册executor成功之后，会发送回来RegisteredExecutor消息
    driver ! RegisterExecutor(executorId, hostPort, cores, extractLogUrls)
    context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])
  }





###org.apache.spark.executor/CoarseGrainedExecutorBackend.scala

override def receiveWithLogging = {
    // driver注册executor成功之后，会发送回来RegisteredExecutor消息
    // 此时，CoarseGrainedExecutorBackend会创建Executor对象，作为执行句柄
    // 其实它的大部分功能，都是通过Executor实现的
    case RegisteredExecutor =>
      logInfo("Successfully registered with driver")
      val (hostname, _) = Utils.parseHostPort(hostPort)
      executor = new Executor(executorId, hostname, env, userClassPath, isLocal = false)

3、启动Task

###org.apache.spark.executor/CoarseGrainedExecutorBackend.scala

    // 启动task
    case LaunchTask(data) =>
      if (executor == null) {
        logError("Received LaunchTask command but executor was null")
        System.exit(1)
      } else {
        // 反序列化task
        val ser = env.closureSerializer.newInstance()
        val taskDesc = ser.deserialize[TaskDescription](data.value)
        logInfo("Got assigned task " + taskDesc.taskId)
        // 用内部的执行句柄，Executor的launchTask()方法来启动一个task
        executor.launchTask(this, taskId = taskDesc.taskId, attemptNumber = taskDesc.attemptNumber,
          taskDesc.name, taskDesc.serializedTask)
      }





###org.apache.spark.executor/Executor.scala

  def launchTask(
      context: ExecutorBackend,
      taskId: Long,
      attemptNumber: Int,
      taskName: String,
      serializedTask: ByteBuffer) {
    // 对于每一个task，都会创建一个TaskRunner
    // TaskRunner继承的是Java多线程中的Runnable接口
    val tr = new TaskRunner(context, taskId = taskId, attemptNumber = attemptNumber, taskName,
      serializedTask)
    // 将TaskRunner放入内存缓存
    runningTasks.put(taskId, tr)
    // Executor内部有一个Java线程池，这里其实将task封装在一个线程中(TaskRunner),直接将线程丢入线程池，进行执行
    // 线程池是自动实现了排队机制的，也就是说，如果线程池内的线程暂时没有空闲的，那么丢进去的线程都是要排队的
    threadPool.execute(tr)
  }