调用CoarseGrainedExecutorBackend的生命周期方法以及作用是什么？

CoarseGrainedExecutorBackend的生命周期方法 调用CoarseGrainedExecutorBackend的生命周期方法,在preStart()方法主要代码如下:

//TODO 生命周期方法，首先和Driver建立连接 override def preStart() { logInfo("Connecting to driver: " + driverUrl) driver = context.actorSelection(driverUrl) //TODO Executor向Driver发送消息,注册Executor driver ! RegisterExecutor(executorId, hostPort, cores, extractLogUrls) context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) }

这个方法主要是和Driver进行通信，向Driver发送信息,注册Executor我们这里需要看的是DriverActor的代码

//TODO Executor向DriverActor发送的消息 case RegisterExecutor(executorId, hostPort, cores, logUrls) => Utils.checkHostPort(hostPort, "Host port expected " + hostPort) if (executorDataMap.contains(executorId)) { //TODO DriverActor向Executor发送消息，告诉Executor注册失败 sender ! RegisterExecutorFailed("Duplicate executor ID: " + executorId) } else { logInfo("Registered executor: " + sender + " with ID " + executorId) //TODO DriverActor向Executor发送消息，告诉Executor注册成功 sender ! RegisteredExecutor

      addressToExecutorId(sender.path.address) = executorId
      totalCoreCount.addAndGet(cores)
      totalRegisteredExecutors.addAndGet(1)
      val (host, _) = Utils.parseHostPort(hostPort)
      val data = new ExecutorData(sender, sender.path.address, host, cores, cores, logUrls)
      // This must be synchronized because variables mutated
      // in this block are read when requesting executors
      CoarseGrainedSchedulerBackend.this.synchronized {
        executorDataMap.put(executorId, data)
        if (numPendingExecutors > 0) {
          numPendingExecutors -= 1
          logDebug(s"Decremented number of pending executors ($numPendingExecutors left)")
        }
      }
      listenerBus.post(
        SparkListenerExecutorAdded(System.currentTimeMillis(), executorId, data))
      //TODO 查看是否有任务需要提交(DriverActor->Executor)
      makeOffers()
    }

这里进行一个判断后，向Executor发送注册成功后,然后调用makeOffers()查看是否有任务需要提交。这里我们首先看DriverActor向Executor 向Executor发送消息,表示注册成功，然后再次查看makeOffers()方法。