DT大数据梦工厂Spark定制班笔记(009)

Spark Streaming源码解读之Receiver在Driver的精妙实现全生命周期彻底研究和思考

在经过了一系列的有关Spark Streaming Job的考察之后，我们把目光转向Receiver。

Spark Streaming中ReceiverInputDStream都是现实一个Receiver，用来接收数据。而Receiver可以有很多个，并且运行在不同的worker节点上。这些Receiver都是由ReceiverTracker来管理的。

ReceiverTracker的start方法如下（ReceiverTracker.scala 152-164行）

def start(): Unit = synchronized {
  if (isTrackerStarted) {
    throw new SparkException("ReceiverTracker already started")
  }

  if (!receiverInputStreams.isEmpty) {
    endpoint = ssc.env.rpcEnv.setupEndpoint(
      "ReceiverTracker", new ReceiverTrackerEndpoint(ssc.env.rpcEnv))
    if (!skipReceiverLaunch) launchReceivers()
    logInfo("ReceiverTracker started")
    trackerState = Started
  }
}

它首先实例化一个消息通信体ReceiverTrackerEndpoint，然后调用launchReceivers(ReceiverTracker.scala 436-447行).

private def launchReceivers(): Unit = {
  val receivers = receiverInputStreams.map { nis =>
    val rcvr = nis.getReceiver()
    rcvr.setReceiverId(nis.id)
    rcvr
  }

  runDummySparkJob()

  logInfo("Starting " + receivers.length + " receivers")
  endpoint.send(StartAllReceivers(receivers))
}

它的巧妙之处在于会先运行 runDummySparkJob()，从而获得集群中Executor的情况。

然后向消息通信体发送StartAllReceivers消息。

消息通信体收到消息后的处理过程如下所示（ReceiverTracker.scala 468-475行）

case StartAllReceivers(receivers) =>
  val scheduledLocations = schedulingPolicy.scheduleReceivers(receivers, getExecutors)
  for (receiver <- receivers) {
    val executors = scheduledLocations(receiver.streamId)
    updateReceiverScheduledExecutors(receiver.streamId, executors)
    receiverPreferredLocations(receiver.streamId) = receiver.preferredLocation
    startReceiver(receiver, executors)
  }

在这里我们需要展示一下函数startReceiver中一处精妙的实现 （ReceiverTracker.scala 605-611行）

val receiverRDD: RDD[Receiver[_]] =
  if (scheduledLocations.isEmpty) {
    ssc.sc.makeRDD(Seq(receiver), 1)
  } else {
    val preferredLocations = scheduledLocations.map(_.toString).distinct
    ssc.sc.makeRDD(Seq(receiver -> preferredLocations))
  }

在这里Receiver被封装成了RDD！（所以Receiver必须是可以序列化的）

并被提交到集群中运行。（ReceiverTracker.scala 616行）

val future = ssc.sparkContext.submitJob[Receiver[_], Unit, Unit](
  receiverRDD, startReceiverFunc, Seq(0), (_, _) => Unit, ())

在任务被提交到worker节点后，执行如下操作。（receiverTracker.scala 585-602行）

// Function to start the receiver on the worker node
val startReceiverFunc: Iterator[Receiver[_]] => Unit =
  (iterator: Iterator[Receiver[_]]) => {
    if (!iterator.hasNext) {
      throw new SparkException(
        "Could not start receiver as object not found.")
    }
    if (TaskContext.get().attemptNumber() == 0) {
      val receiver = iterator.next()
      assert(iterator.hasNext == false)
      val supervisor = new ReceiverSupervisorImpl(
        receiver, SparkEnv.get, serializableHadoopConf.value, checkpointDirOption)
      supervisor.start()
      supervisor.awaitTermination()
    } else {
      // It's restarted by TaskScheduler, but we want to reschedule it again. So exit it.
    }
  }

此处创建了一个ReceiverSupervisorImpl对象；用来管理具体的Receiver。

它首先会将Receiver注册到ReceiverTracker中 （ReceiverSupervisor.scala 182-186行）

override protected def onReceiverStart(): Boolean = {
  val msg = RegisterReceiver(
    streamId, receiver.getClass.getSimpleName, host, executorId, endpoint)
  trackerEndpoint.askWithRetry[Boolean](msg)
}

注册成功后，启动receiver。（ReceiverSupervisor.scala 144-159行）

def startReceiver(): Unit = synchronized {
  try {
    if (onReceiverStart()) {
      logInfo(s"Starting receiver $streamId")
      receiverState = Started
      receiver.onStart()
      logInfo(s"Called receiver $streamId onStart")
    } else {
      // The driver refused us
      stop("Registered unsuccessfully because Driver refused to start receiver " + streamId, None)
    }
  } catch {
    case NonFatal(t) =>
      stop("Error starting receiver " + streamId, Some(t))
  }
}

上述过程如下图所示 转自http://lqding.blog.51cto.com/9123978/1773912 感谢作者！