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

Posted 2020-08-01

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一：Receiver启动的方式设想
1. Spark Streaming通过Receiver持续不断的从外部数据源接收数据，并把数据汇报给Driver端，由此每个Batch Durations就可以根据汇报的数据生成不同的Job。
2. Receiver属于Spark Streaming应用程序启动阶段，那么我们找Receiver在哪里启动就应该去找Spark Streaming的启动。
3. Receivers和InputDStreams是一一对应的，默认情况下一般只有一个Receiver.

如何启动Receiver？
1. 从Spark Core的角度来看，Receiver的启动Spark Core并不知道，就相当于Linux的内核之上所有的都是应用程序，因此Receiver是通过Job的方式启动的。
2. 一般情况下，只有一个Receiver，但是可以创建不同的数据来源的InputDStream.

final private[streaming] class DStreamGraph extends Serializable with Logging {

  private val inputStreams = new ArrayBuffer[InputDStream[_]]()
  private val outputStreams = new ArrayBuffer[DStream[_]]()

3.  启动Receiver的时候,启动一个Job，这个Job里面有RDD的transformations操作和action的操作，这个Job只有一个partition.这个partition的特殊是里面只有一个成员，这个成员就是启动的Receiver.
4.  这样做的问题：
a)  如果有多个InputDStream，那就要启动多个Receiver，每个Receiver也就相当于分片partition，那我们启动Receiver的时候理想的情况下是在不同的机器上启动Receiver，但是Spark Core的角度来看就是应用程序，感觉不到Receiver的特殊性，所以就会按照正常的Job启动的方式来处理，极有可能在一个Executor上启动多个Receiver.这样的话就可能导致负载不均衡。

b)  有可能启动Receiver失败，只要集群存在Receiver就不应该失败。

c)  运行过程中，就默认的而言如果是一个partition的话，那启动的时候就是一个Task，但是此Task也很可能失败，因此以Task启动的Receiver也会挂掉。

由此，可以得出，对于Receiver失败的话，后果是非常严重的，那么Spark Streaming如何防止这些事的呢，下面就寻找Receiver的创建。
这里先给出答案，后面源码会详细分析：
a) Spark使用一个Job启动一个Receiver.最大程度的保证了负载均衡。
b) Spark Streaming指定每个Receiver运行在那些Executor上。
c) 如果Receiver启动失败，此时并不是Job失败，在内部会重新启动Receiver.

在StreamingContext的start方法被调用的时候，JobScheduler的start方法会被调用。

/**
 * Start the execution of the streams.
 *
 * @throws IllegalStateException if the StreamingContext is already stopped.
 */
def start(): Unit = synchronized {
  state match {
    case INITIALIZED =>
      startSite.set(DStream.getCreationSite())
      StreamingContext.ACTIVATION_LOCK.synchronized {
        StreamingContext.assertNoOtherContextIsActive()
        try {
          validate()

          // Start the streaming scheduler in a new thread, so that thread local properties
          // like call sites and job groups can be reset without affecting those of the
          // current thread.
          ThreadUtils.runInNewThread("streaming-start") {
            sparkContext.setCallSite(startSite.get)
            sparkContext.clearJobGroup()
            sparkContext.setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, "false")
//启动子线程，一方面为了本地初始化工作，另外一方面是不要阻塞主线程。
            scheduler.start()
          }
          state = StreamingContextState.ACTIVE
        } catch {
          case NonFatal(e) =>
            logError("Error starting the context, marking it as stopped", e)
            scheduler.stop(false)
            state = StreamingContextState.STOPPED
            throw e
        }
        StreamingContext.setActiveContext(this)
      }
      shutdownHookRef = ShutdownHookManager.addShutdownHook(
        StreamingContext.SHUTDOWN_HOOK_PRIORITY)(stopOnShutdown)
      // Registering Streaming Metrics at the start of the StreamingContext
      assert(env.metricsSystem != null)
      env.metricsSystem.registerSource(streamingSource)
      uiTab.foreach(_.attach())
      logInfo("StreamingContext started")
    case ACTIVE =>
      logWarning("StreamingContext has already been started")
    case STOPPED =>
      throw new IllegalStateException("StreamingContext has already been stopped")
  }
}

2.  而在JobScheduler的start方法中ReceiverTracker的start方法被调用，Receiver就启动了。

def start(): Unit = synchronized {
  if (eventLoop != null) return // scheduler has already been started

  logDebug("Starting JobScheduler")
  eventLoop = new EventLoop[JobSchedulerEvent]("JobScheduler") {
    override protected def onReceive(event: JobSchedulerEvent): Unit = processEvent(event)

    override protected def onError(e: Throwable): Unit = reportError("Error in job scheduler", e)
  }
  eventLoop.start()

  // attach rate controllers of input streams to receive batch completion updates
  for {
    inputDStream <- ssc.graph.getInputStreams
    rateController <- inputDStream.rateController
  } ssc.addStreamingListener(rateController)

  listenerBus.start(ssc.sparkContext)
  receiverTracker = new ReceiverTracker(ssc)
  inputInfoTracker = new InputInfoTracker(ssc)
//启动receiverTracker
  receiverTracker.start()
  jobGenerator.start()
  logInfo("Started JobScheduler")
}

3.  ReceiverTracker的start方法启动RPC消息通信体，为啥呢？因为receiverTracker会监控整个集群中的Receiver，Receiver转过来要向ReceiverTrackerEndpoint汇报自己的状态，接收的数据，包括生命周期等信息

/** Start the endpoint and receiver execution thread. */
def start(): Unit = synchronized {
  if (isTrackerStarted) {
    throw new SparkException("ReceiverTracker already started")
  }
//Receiver的启动是依据输入数据流的。
  if (!receiverInputStreams.isEmpty) {
    endpoint = ssc.env.rpcEnv.setupEndpoint(
      "ReceiverTracker", new ReceiverTrackerEndpoint(ssc.env.rpcEnv))
    if (!skipReceiverLaunch) launchReceivers()
    logInfo("ReceiverTracker started")
    trackerState = Started
  }
}

4.  基于ReceiverInputDStream(是在Driver端)来获得具体的Receivers实例，然后再把他们分不到Worker节点上。一个ReceiverInputDStream只产生一个Receiver

/**
 * Get the receivers from the ReceiverInputDStreams, distributes them to the
 * worker nodes as a parallel collection, and runs them.
 */
private def launchReceivers(): Unit = {
  val receivers = receiverInputStreams.map(nis => {
//一个数据输入来源(receiverInputDStream)只产生一个Receiver
    val rcvr = nis.getReceiver()
    rcvr.setReceiverId(nis.id)
    rcvr
  })

  runDummySparkJob()

  logInfo("Starting " + receivers.length + " receivers")
//此时的endpoint就是上面代码中在ReceiverTracker的start方法中构造的ReceiverTrackerEndpoint
  endpoint.send(StartAllReceivers(receivers))
}

5.  其中runDummySparkJob()为了确保所有节点活着，而且避免所有的receivers集中在一个节点上。

/**
 * Run the dummy Spark job to ensure that all slaves have registered. This avoids all the
 * receivers to be scheduled on the same node.
 *
 * TODO Should poll the executor number and wait for executors according to
 * "spark.scheduler.minRegisteredResourcesRatio" and
 * "spark.scheduler.maxRegisteredResourcesWaitingTime" rather than running a dummy job.
 */
private def runDummySparkJob(): Unit = {
  if (!ssc.sparkContext.isLocal) {
    ssc.sparkContext.makeRDD(1 to 50, 50).map(x => (x, 1)).reduceByKey(_ + _, 20).collect()
  }
  assert(getExecutors.nonEmpty)
}

ReceiverInputDStream中的getReceiver()方法获得receiver对象然后将它发送到worker节点上实例化receiver,然后去接收数据。
此方法必须要在子类中实现。

/**
 * Gets the receiver object that will be sent to the worker nodes
 * to receive data. This method needs to defined by any specific implementation
 * of a ReceiverInputDStream.
 */
def getReceiver(): Receiver[T] //返回的是Receiver对象

7.  根据继承关系，这里看一下SocketInputDStream中的getReceiver方法。

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def getReceiver(): Receiver[T] = {
    new SocketReceiver(host, port, bytesToObjects, storageLevel)
  }
}

启动后台线程，调用receive方法。

private[streaming]
class SocketReceiver[T: ClassTag](
    host: String,
    port: Int,
    bytesToObjects: InputStream => Iterator[T],
    storageLevel: StorageLevel
  ) extends Receiver[T](storageLevel) with Logging {

  def onStart() {
    // Start the thread that receives data over a connection
    new Thread("Socket Receiver") {
      setDaemon(true)
      override def run() { receive() }
    }.start()
  }

启动socket开始接收数据。

/** Create a socket connection and receive data until receiver is stopped */
  def receive() {
    var socket: Socket = null
    try {
      logInfo("Connecting to " + host + ":" + port)
      socket = new Socket(host, port)
      logInfo("Connected to " + host + ":" + port)
      val iterator = bytesToObjects(socket.getInputStream())
      while(!isStopped && iterator.hasNext) {
        store(iterator.next)
      }
      if (!isStopped()) {
        restart("Socket data stream had no more data")
      } else {
        logInfo("Stopped receiving")
      }
    } catch {
      case e: java.net.ConnectException =>
        restart("Error connecting to " + host + ":" + port, e)
      case NonFatal(e) =>
        logWarning("Error receiving data", e)
        restart("Error receiving data", e)
    } finally {
      if (socket != null) {
        socket.close()
        logInfo("Closed socket to " + host + ":" + port)
      }
    }
  }
}

8.  ReceiverTrackerEndpoint源码如下：

/** RpcEndpoint to receive messages from the receivers. */
private class ReceiverTrackerEndpoint(override val rpcEnv: RpcEnv) extends ThreadSafeRpcEndpoint {

  // TODO Remove this thread pool after https://github.com/apache/spark/issues/7385 is merged
  private val submitJobThreadPool = ExecutionContext.fromExecutorService(
    ThreadUtils.newDaemonCachedThreadPool("submit-job-thread-pool"))

  private val walBatchingThreadPool = ExecutionContext.fromExecutorService(
    ThreadUtils.newDaemonCachedThreadPool("wal-batching-thread-pool"))

  @volatile private var active: Boolean = true

  override def receive: PartialFunction[Any, Unit] = {
    // Local messages
    case StartAllReceivers(receivers) =>
      val scheduledLocations = 
// schedulingPolicy调度策略     
//receivers就是要启动的receiver
//getExecutors获得集群中的Executors的列表
// scheduleReceivers就可以确定receiver可以运行在哪些Executor上
schedulingPolicy.scheduleReceivers(receivers, getExecutors)
      for (receiver <- receivers) {
// scheduledLocations根据receiver的Id就找到了当前那些Executors可以运行Receiver
        val executors = scheduledLocations(receiver.streamId)
        updateReceiverScheduledExecutors(receiver.streamId, executors)
        receiverPreferredLocations(receiver.streamId) = receiver.preferredLocation
//上述代码之后要启动的Receiver确定了，具体Receiver运行在哪些Executors上也确定了。
//循环receivers，每次将一个receiver传入过去。
        startReceiver(receiver, executors)
      }
//用于接收RestartReceiver消息，从新启动Receiver.
    case RestartReceiver(receiver) =>
      // Old scheduled executors minus the ones that are not active any more
//如果Receiver失败的话，从可选列表中减去。
      val oldScheduledExecutors = 
//刚在调度为Receiver分配给哪个Executor的时候会有一些列可选的Executor列表
getStoredScheduledExecutors(receiver.streamId)
//从新获取Executors
      val scheduledLocations = if (oldScheduledExecutors.nonEmpty) {
          // Try global scheduling again
          oldScheduledExecutors
        } else {
//如果可选的Executor使用完了，则会重新执行rescheduleReceiver重新获取Executor.
          val oldReceiverInfo = receiverTrackingInfos(receiver.streamId)
          // Clear "scheduledLocations" to indicate we are going to do local scheduling
          val newReceiverInfo = oldReceiverInfo.copy(
            state = ReceiverState.INACTIVE, scheduledLocations = None)
          receiverTrackingInfos(receiver.streamId) = newReceiverInfo
          schedulingPolicy.rescheduleReceiver(
            receiver.streamId,
            receiver.preferredLocation,
            receiverTrackingInfos,
            getExecutors)
        }
      // Assume there is one receiver restarting at one time, so we don‘t need to update
      // receiverTrackingInfos
//重复调用startReceiver
      startReceiver(receiver, scheduledLocations)
    case c: CleanupOldBlocks =>
      receiverTrackingInfos.values.flatMap(_.endpoint).foreach(_.send(c))
    case UpdateReceiverRateLimit(streamUID, newRate) =>
      for (info <- receiverTrackingInfos.get(streamUID); eP <- info.endpoint) {
        eP.send(UpdateRateLimit(newRate))
      }
    // Remote messages
    case ReportError(streamId, message, error) =>
      reportError(streamId, message, error)
  }

9.  从注释中可以看到，Spark Streaming指定receiver在那些Executors运行，而不是基于Spark Core中的Task来指定。

Spark使用submit Job的方式启动Receiver,而在应用程序执行的时候会有很多Receiver，这个时候是启动一个Receiver呢，还是把所有的Receiver通过这一个Job启动？
在ReceiverTracker的receive方法中startReceiver方法第一个参数就是receiver,从实现的可以看出for循环不断取出receiver,然后调用startReceiver。由此就可以得出一个Job只启动一个Receiver.
如果Receiver启动失败，此时并不会认为是作业失败，会重新发消息给ReceiverTrackerEndpoint重新启动Receiver，这样也就确保了Receivers一定会被启动，这样就不会像Task启动Receiver的话如果失败受重试次数的影响。

/**
 * Start a receiver along with its scheduled executors
 */
private def startReceiver(
    receiver: Receiver[_],
// scheduledLocations指定的是在具体的那台物理机器上执行。
    scheduledLocations: Seq[TaskLocation]): Unit = {
//判断下Receiver的状态是否正常。
  def shouldStartReceiver: Boolean = {
    // It‘s okay to start when trackerState is Initialized or Started
    !(isTrackerStopping || isTrackerStopped)
  }

  val receiverId = receiver.streamId
//如果不需要启动Receiver则会调用onReceiverJobFinish()
  if (!shouldStartReceiver) {
    onReceiverJobFinish(receiverId)
    return
  }

  val checkpointDirOption = Option(ssc.checkpointDir)
  val serializableHadoopConf =
    new SerializableConfiguration(ssc.sparkContext.hadoopConfiguration)

//startReceiverFunc封装了在worker上启动receiver的动作。
  // 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)
// ReceiverSupervisorImpl是Receiver的监控器，同时负责数据的写等操作。
        val supervisor = new ReceiverSupervisorImpl(
          receiver, SparkEnv.get, serializableHadoopConf.value, checkpointDirOption)
        supervisor.start()
        supervisor.awaitTermination()
      } else {
//如果你想重新启动receiver的话，你需要重新完成上面的调度，从新schedule，而不是Task重试。
        // It‘s restarted by TaskScheduler, but we want to reschedule it again. So exit it.
      }
    }

  // Create the RDD using the scheduledLocations to run the receiver in a Spark job
  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))
    }
//receiverId可以看出，receiver只有一个
  receiverRDD.setName(s"Receiver $receiverId")
  ssc.sparkContext.setJobDescription(s"Streaming job running receiver $receiverId")
  ssc.sparkContext.setCallSite(Option(ssc.getStartSite()).getOrElse(Utils.getCallSite()))
//每个Receiver的启动都会触发一个Job,而不是一个作业的Task去启动所有的Receiver.
//应用程序一般会有很多Receiver,
//调用SparkContext的submitJob，为了启动Receiver，启动了Spark一个作业.
  val future = ssc.sparkContext.submitJob[Receiver[_], Unit, Unit](
    receiverRDD, startReceiverFunc, Seq(0), (_, _) => Unit, ())
  // We will keep restarting the receiver job until ReceiverTracker is stopped
  future.onComplete {
    case Success(_) =>
// shouldStartReceiver默认是true
      if (!shouldStartReceiver) {
        onReceiverJobFinish(receiverId) 
      } else {
        logInfo(s"Restarting Receiver $receiverId")
        self.send(RestartReceiver(receiver))
      }

    case Failure(e) =>
      if (!shouldStartReceiver) {
        onReceiverJobFinish(receiverId)
      } else {
        logError("Receiver has been stopped. Try to restart it.", e)
        logInfo(s"Restarting Receiver $receiverId") 
//RestartReceiver
        self.send(RestartReceiver(receiver))
      }
//使用线程池的方式提交Job，这样的好处是可以并发的启动Receiver。
  }(submitJobThreadPool)
  logInfo(s"Receiver ${receiver.streamId} started")
}

10. 当Receiver启动失败的话，就会调用ReceiverTrackEndpoint重新启动一个Spark Job去启动Receiver.

/**
 * This message will trigger ReceiverTrackerEndpoint to restart a Spark job for the receiver.
 */
private[streaming] case class RestartReceiver(receiver: Receiver[_])
  extends ReceiverTrackerLocalMessage

11. 当Receiver关闭的话，并不需要重新启动Spark Job.

/**
 * Call when a receiver is terminated. It means we won‘t restart its Spark job.
 */
private def onReceiverJobFinish(receiverId: Int): Unit = {
  receiverJobExitLatch.countDown()
//使用foreach将receiver从receiverTrackingInfo中去掉。
  receiverTrackingInfos.remove(receiverId).foreach { receiverTrackingInfo =>
    if (receiverTrackingInfo.state == ReceiverState.ACTIVE) {
      logWarning(s"Receiver $receiverId exited but didn‘t deregister")
    }
  }
}

12. Supervisor.start()，在子类ReceiverSupervisorImpl中并没有start方法，因此调用的是父类ReceiverSupervisor的start方法。

/** Start the supervisor */
def start() {
  onStart() //具体实现是子类实现的。
  startReceiver()
}

Onstart方法源码如下：

/**
 * Called when supervisor is started.
 * Note that this must be called before the receiver.onStart() is called to ensure
 * things like [[BlockGenerator]]s are started before the receiver starts sending data.
 */
protected def onStart() { }

其具体实现是在子类的ReceiverSupervivorImpl的onstart方法

override protected def onStart() {
  registeredBlockGenerators.foreach { _.start() }
}

此时的start方法调用的是BlockGenerator的start方法。

/** Start block generating and pushing threads. */
def start(): Unit = synchronized {
  if (state == Initialized) {
    state = Active
    blockIntervalTimer.start()
    blockPushingThread.start()
    logInfo("Started BlockGenerator")
  } else {
    throw new SparkException(
      s"Cannot start BlockGenerator as its not in the Initialized state [state = $state]")
  }
}

Receiver启动全生命周期总流程图如下：
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http://blog.csdn.net/snail_gesture/article/details/51461064

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