Spark内核源码解析九：executor原理解析和源码解析

  // Make fake resource offers on just one executor
    def makeOffers(executorId: String) {
      val executorData = executorDataMap(executorId)
      launchTasks(scheduler.resourceOffers(
        Seq(new WorkerOffer(executorId, executorData.executorHost, executorData.freeCores))))
    }

    // Launch tasks returned by a set of resource offers
    def launchTasks(tasks: Seq[Seq[TaskDescription]]) {
      for (task <- tasks.flatten) {
        val ser = SparkEnv.get.closureSerializer.newInstance()
        val serializedTask = ser.serialize(task)
        if (serializedTask.limit >= akkaFrameSize - AkkaUtils.reservedSizeBytes) {
          val taskSetId = scheduler.taskIdToTaskSetId(task.taskId)
          scheduler.activeTaskSets.get(taskSetId).foreach { taskSet =>
            try {
              var msg = "Serialized task %s:%d was %d bytes, which exceeds max allowed: " +
                "spark.akka.frameSize (%d bytes) - reserved (%d bytes). Consider increasing " +
                "spark.akka.frameSize or using broadcast variables for large values."
              msg = msg.format(task.taskId, task.index, serializedTask.limit, akkaFrameSize,
                AkkaUtils.reservedSizeBytes)
              taskSet.abort(msg)
            } catch {
              case e: Exception => logError("Exception in error callback", e)
            }
          }
        }
        else {
          val executorData = executorDataMap(task.executorId)
          executorData.freeCores -= scheduler.CPUS_PER_TASK
          executorData.executorActor ! LaunchTask(new SerializableBuffer(serializedTask))
        }
      }
    }

override def preStart() {
    logInfo("Connecting to driver: " + driverUrl)
    driver = context.actorSelection(driverUrl)
    // 向driver发送反向注册消息
    driver ! RegisterExecutor(executorId, hostPort, cores, extractLogUrls)
    context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])
  }

override def receiveWithLogging = {
    // Driver注册executor成功后，会发送回来RegisteredExecutor消息，
    // Executor会创建executor作为执行句柄，其实他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)

    case RegisterExecutorFailed(message) =>
      logError("Slave registration failed: " + message)
      System.exit(1)

      // 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)
        executor.launchTask(this, taskId = taskDesc.taskId, attemptNumber = taskDesc.attemptNumber,
          taskDesc.name, taskDesc.serializedTask)
      }

    case KillTask(taskId, _, interruptThread) =>
      if (executor == null) {
        logError("Received KillTask command but executor was null")
        System.exit(1)
      } else {
        executor.killTask(taskId, interruptThread)
      }

    case x: DisassociatedEvent =>
      if (x.remoteAddress == driver.anchorPath.address) {
        logError(s"Driver $x disassociated! Shutting down.")
        System.exit(1)
      } else {
        logWarning(s"Received irrelevant DisassociatedEvent $x")
      }

    case StopExecutor =>
      logInfo("Driver commanded a shutdown")
      executor.stop()
      context.stop(self)
      context.system.shutdown()
  }

  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)
        executor.launchTask(this, taskId = taskDesc.taskId, attemptNumber = taskDesc.attemptNumber,
          taskDesc.name, taskDesc.serializedTask)
      }

 // 对于每一个task都会创建一个taskRunner，继承了java的Runnable接口可以作为一个线程任务进行调用，后面会放入线程池中进行执行
  def launchTask(
      context: ExecutorBackend,
      taskId: Long,
      attemptNumber: Int,
      taskName: String,
      serializedTask: ByteBuffer) {
    val tr = new TaskRunner(context, taskId = taskId, attemptNumber = attemptNumber, taskName,
      serializedTask)
    // 放入内存缓存
    runningTasks.put(taskId, tr)
    threadPool.execute(tr)
  }