Spark源码分析 – Executor

ExecutorBackend

很简单的接口

package org.apache.spark.executor
/**
 * A pluggable interface used by the Executor to send updates to the cluster scheduler.
 */
private[spark] trait ExecutorBackend {
  def statusUpdate(taskId: Long, state: TaskState, data: ByteBuffer)
}

 

StandaloneExecutorBackend

维护executor, 并负责注册executor以及executor和driver之间的通信

private[spark] class StandaloneExecutorBackend(
    driverUrl: String,
    executorId: String,
    hostPort: String,
    cores: Int)
  extends Actor
  with ExecutorBackend
  with Logging {
  var executor: Executor = null
  var driver: ActorRef = null

  override def preStart() {
    logInfo("Connecting to driver: " + driverUrl)
    driver = context.actorFor(driverUrl) // 创建driver actor ref, 以便于和driver通信
    driver ! RegisterExecutor(executorId, hostPort, cores) // 向driver注册executor
  }

  override def receive = {
    case RegisteredExecutor(sparkProperties) =>
      logInfo("Successfully registered with driver")
      // Make this host instead of hostPort ? 
      executor = new Executor(executorId, Utils.parseHostPort(hostPort)._1, sparkProperties) // 当注册成功后, 创建Executor

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

    case LaunchTask(taskDesc) =>
      logInfo("Got assigned task " + taskDesc.taskId)
      if (executor == null) {
        logError("Received launchTask but executor was null")
        System.exit(1)
      } else {
        executor.launchTask(this, taskDesc.taskId, taskDesc.serializedTask) // 调用executor.launchTask,启动task
      }

    case Terminated(_) | RemoteClientDisconnected(_, _) | RemoteClientShutdown(_, _) =>
      logError("Driver terminated or disconnected! Shutting down.")
      System.exit(1)
  }

  override def statusUpdate(taskId: Long, state: TaskState, data: ByteBuffer) {
    driver ! StatusUpdate(executorId, taskId, state, data) // 当task状态变化时, 报告给driver actor
  }
}

Executor

对于Executor, 维护一个threadPool, 可以run多个task, 取决于core的个数
所以对于launchTask, 就是在threadPool中挑个thread去run TaskRunner

private[spark] class Executor(
    executorId: String,
    slaveHostname: String,
    properties: Seq[(String, String)])
  extends Logging
{

  // Initialize Spark environment (using system properties read above)
  val env = SparkEnv.createFromSystemProperties(executorId, slaveHostname, 0, false, false)
  SparkEnv.set(env)

  // Start worker thread pool
  val threadPool = new ThreadPoolExecutor(
    1, 128, 600, TimeUnit.SECONDS, new SynchronousQueue[Runnable])

  def launchTask(context: ExecutorBackend, taskId: Long, serializedTask: ByteBuffer) {
    threadPool.execute(new TaskRunner(context, taskId, serializedTask))
  }

}

 

TaskRunner

  class TaskRunner(context: ExecutorBackend, taskId: Long, serializedTask: ByteBuffer)
    extends Runnable {

    override def run() {
      try {
        SparkEnv.set(env)
        Accumulators.clear()
        val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(serializedTask) // 反序列化
        updateDependencies(taskFiles, taskJars)
        val task = ser.deserialize[Task[Any]](taskBytes, Thread.currentThread.getContextClassLoader) // 反序列化 
        attemptedTask = Some(task)
        logInfo("Its epoch is " + task.epoch)
        env.mapOutputTracker.updateEpoch(task.epoch)
        taskStart = System.currentTimeMillis()
        val value = task.run(taskId.toInt)  // 调用task.run执行真正的逻辑
        val taskFinish = System.currentTimeMillis()

        val accumUpdates = Accumulators.values
        val result = new TaskResult(value, accumUpdates, task.metrics.getOrElse(null)) // 生成TaskResult
        val serializedResult = ser.serialize(result) // 将TaskResult序列化
        logInfo("Serialized size of result for " + taskId + " is " + serializedResult.limit)

        context.statusUpdate(taskId, TaskState.FINISHED, serializedResult) // 将任务完成和taskresult,通过statusUpdate报告给driver
        logInfo("Finished task ID " + taskId)
      } catch { // 处理各种fail, 同样也要用statusUpdate event通知driver
        case ffe: FetchFailedException => {
          val reason = ffe.toTaskEndReason
          context.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason))
        }

        case t: Throwable => {
          val serviceTime = (System.currentTimeMillis() - taskStart).toInt
          val metrics = attemptedTask.flatMap(t => t.metrics)
          for (m <- metrics) {
            m.executorRunTime = serviceTime
            m.jvmGCTime = getTotalGCTime - startGCTime
          }
          val reason = ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace, metrics)
          context.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason))

          // TODO: Should we exit the whole executor here? On the one hand, the failed task may
          // have left some weird state around depending on when the exception was thrown, but on
          // the other hand, maybe we could detect that when future tasks fail and exit then.
          logError("Exception in task ID " + taskId, t)
          //System.exit(1)
        }
      }
    }
  }