Spark源码精读分析计划

Spark Core源码精读计划#15：心跳接收器Heartbe

2019-05-21  本文已影响4人  LittleMagic

目录

前言

按照SparkContext初始化的顺序，下一个应该是心跳接收器HeartbeatReceiver。由于笔者感染乙流仍然没有痊愈，状态不好，文中若有疏漏，请批评指正。

我们已经知道，Executor需要定期向Driver发送心跳信号来表示自己存活，因此HeartbeatReceiver由Driver持有，负责处理各个Executor的心跳消息，监控它们的状态。本文就来简要探究一下HeartbeatReceiver的实现细节。

HeartbeatReceiver类

代码#15.1 - o.a.s.HeartbeatReceiver类定义及成员属性

private[spark] class HeartbeatReceiver(sc: SparkContext, clock: Clock)
  extends SparkListener with ThreadSafeRpcEndpoint with Logging {
  def this(sc: SparkContext) {
    this(sc, new SystemClock)
  }

  sc.listenerBus.addToManagementQueue(this)

  override val rpcEnv: RpcEnv = sc.env.rpcEnv

  private[spark] var scheduler: TaskScheduler = null

  private val executorLastSeen = new mutable.HashMap[String, Long]

  private val slaveTimeoutMs =
    sc.conf.getTimeAsMs("spark.storage.blockManagerSlaveTimeoutMs", "120s")
  private val executorTimeoutMs =
    sc.conf.getTimeAsSeconds("spark.network.timeout", s"${slaveTimeoutMs}ms") * 1000

  private val timeoutIntervalMs =
    sc.conf.getTimeAsMs("spark.storage.blockManagerTimeoutIntervalMs", "60s")
  private val checkTimeoutIntervalMs =
    sc.conf.getTimeAsSeconds("spark.network.timeoutInterval", s"${timeoutIntervalMs}ms") * 1000

  private var timeoutCheckingTask: ScheduledFuture[_] = null

  private val eventLoopThread =
    ThreadUtils.newDaemonSingleThreadScheduledExecutor("heartbeat-receiver-event-loop-thread")

  private val killExecutorThread = ThreadUtils.newDaemonSingleThreadExecutor("kill-executor-thread")

声明和构造

可见，HeartbeatReceiver类继承了SparkListener抽象类，又实现了ThreadSafeRpcEndpoint特征，说明它既是一个监听器，又是一个（线程安全的）RPC端点。我们之前对Spark监听器机制和RPC环境都有了深入的了解，所以这些都是毛毛雨了。

HeartbeatReceiver类有两个构造方法参数，其一是SparkContext，另外一个则是o.a.s.util.Clock特征的实现类SystemClock。SystemClock提供了对系统时间System.currentTimeMillis()的简单封装。

在HeartbeatReceiver构造时，会将其同时加入LiveListenerBus的Executor管理（executorManagement）队列中进行监听。

部分成员属性的含义

下面我们来看HeartbeatReceiver类提供的方法，看看它是如何运作的。

HeartbeatReceiver提供的方法

启动

HeartbeatReceiver作为一个RPC端点，实现了RpcEndpoint.onStart()方法，当RPC环境中的Dispatcher注册RPC端点时，会调用该方法。代码如下。

代码#15.2 - o.a.s.HeartbeatReceiver.onStart()方法

  override def onStart(): Unit = {
    timeoutCheckingTask = eventLoopThread.scheduleAtFixedRate(new Runnable {
      override def run(): Unit = Utils.tryLogNonFatalError {
        Option(self).foreach(_.ask[Boolean](ExpireDeadHosts))
      }
    }, 0, checkTimeoutIntervalMs, TimeUnit.MILLISECONDS)
  }

可见，在HeartbeatReceiver启动时，会让eventLoopThread开始以spark.network.timeoutInterval规定的间隔调度执行，并将ScheduledFuture对象返回给timeoutCheckingTask。该线程只做一件事，就是向HeartbeatReceiver自己发送ExpireDeadHosts消息，并等待回复。后面我们会知道它如何处理该消息。

监听Executor添加和移除

HeartbeatReceiver作为一个监听器，实现了SparkListener.onExecutorAdded()与onExecutorRemoved()方法，用来监听Executor的添加与移除。代码如下。

代码#15.3 - o.a.s.HeartbeatReceiver.onExecutorAdded()/onExecutorRemoved()方法

  override def onExecutorAdded(executorAdded: SparkListenerExecutorAdded): Unit = {
    addExecutor(executorAdded.executorId)
  }

  override def onExecutorRemoved(executorRemoved: SparkListenerExecutorRemoved): Unit = {
    removeExecutor(executorRemoved.executorId)
  }

它们分别调用的addExecutor()和removeExecutor()方法如下所示。

代码#15.4 - o.a.s.HeartbeatReceiver.addExecutor()/removeExecutor()方法

  def addExecutor(executorId: String): Option[Future[Boolean]] = {
    Option(self).map(_.ask[Boolean](ExecutorRegistered(executorId)))
  }

  def removeExecutor(executorId: String): Option[Future[Boolean]] = {
    Option(self).map(_.ask[Boolean](ExecutorRemoved(executorId)))
  }

可见，当监听到Executor的添加或移除时，HeartbeatReceiver就会向自己发送带有Executor ID的ExecutorRegistered或ExecutorRemoved消息，并等待回复。

消息处理与回复

这部分逻辑自然是通过实现RpcEndpoint.receiveAndReply()方法来实现的。

代码#15.5 - o.a.s.HeartbeatReceiver.receiveAndReply()方法

  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
    case ExecutorRegistered(executorId) =>
      executorLastSeen(executorId) = clock.getTimeMillis()
      context.reply(true)
    case ExecutorRemoved(executorId) =>
      executorLastSeen.remove(executorId)
      context.reply(true)
    case TaskSchedulerIsSet =>
      scheduler = sc.taskScheduler
      context.reply(true)
    case ExpireDeadHosts =>
      expireDeadHosts()
      context.reply(true)

    case heartbeat @ Heartbeat(executorId, accumUpdates, blockManagerId) =>
      // 下节讲述
  }

我们来详细看下对每种消息分别是如何处理的。

处理Executor心跳

接着上面的receiveAndReply()方法继续看。

代码#15.6 - o.a.s.HeartbeatReceiver.receiveAndReply()方法

  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
    // ...
    case heartbeat @ Heartbeat(executorId, accumUpdates, blockManagerId) =>
      if (scheduler != null) {
        if (executorLastSeen.contains(executorId)) {
          executorLastSeen(executorId) = clock.getTimeMillis()
          eventLoopThread.submit(new Runnable {
            override def run(): Unit = Utils.tryLogNonFatalError {
              val unknownExecutor = !scheduler.executorHeartbeatReceived(
                executorId, accumUpdates, blockManagerId)
              val response = HeartbeatResponse(reregisterBlockManager = unknownExecutor)
              context.reply(response)
            }
          })
        } else {
          logDebug(s"Received heartbeat from unknown executor $executorId")
          context.reply(HeartbeatResponse(reregisterBlockManager = true))
        }
      } else {
        logWarning(s"Dropping $heartbeat because TaskScheduler is not ready yet")
        context.reply(HeartbeatResponse(reregisterBlockManager = true))
      }

可见，在TaskScheduler不为空的情况下，如果executorLastSeen映射中已经保存有Executor ID，就更新时间戳，并向eventLoopThread线程提交执行TaskScheduler.executorHeartbeatReceived()方法（该方法用于通知Master，使其知道BlockManager是存活状态），并回复HeartbeatResponse消息。值得注意的是，executorHeartbeatReceived()方法会返回一个布尔值，表示Driver是否对Executor持有的BlockManager有感知，如果没有的话，就得在HeartbeatResponse消息中注明需要重新注册BlockManager。

至于executorLastSeen映射中不包含当前Executor ID，或者TaskScheduler为空的情况，都会直接回复需要重新注册BlockManager的HeartbeatResponse消息。

清理超时的Executor

该逻辑由expireDeadHosts()方法来实现。

代码#15.7 - o.a.s.HeartbeatReceiver.expireDeadHosts()方法

  private def expireDeadHosts(): Unit = {
    logTrace("Checking for hosts with no recent heartbeats in HeartbeatReceiver.")
    val now = clock.getTimeMillis()
    for ((executorId, lastSeenMs) <- executorLastSeen) {
      if (now - lastSeenMs > executorTimeoutMs) {
        logWarning(s"Removing executor $executorId with no recent heartbeats: " +
          s"${now - lastSeenMs} ms exceeds timeout $executorTimeoutMs ms")
        scheduler.executorLost(executorId, SlaveLost("Executor heartbeat " +
          s"timed out after ${now - lastSeenMs} ms"))
        killExecutorThread.submit(new Runnable {
          override def run(): Unit = Utils.tryLogNonFatalError {
            sc.killAndReplaceExecutor(executorId)
          }
        })
        executorLastSeen.remove(executorId)
      }
    }
  }

该方法会遍历executorLastSeen映射，取出最后一次心跳的时间戳与当前对比，如果时间差值大于spark.network.timeout，就表示Executor已经超时，执行以下操作：

总结

感觉用文字并没有什么好总结的，应该画一幅图来描述HeartbeatReceiver的工作流程才好。但是我决定早点休息，画图的事情就留到明天中午做吧。

晚安~

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