5.Spark Streaming流计算框架的运行流程源码分析2

1 spark streaming 程序代码实例

代码如下：

1. object OnlineTheTop3ItemForEachCategory2DB {
2. def main(args: Array[String]){
3. val conf = new SparkConf() //创建SparkConf对象
4. //设置应用程序的名称，在程序运行的监控界面可以看到名称
5. conf.setAppName("OnlineTheTop3ItemForEachCategory2DB")
6. conf.setMaster("spark://Master:7077") //此时，程序在Spark集群
7. //设置batchDuration时间间隔来控制Job生成的频率并且创建Spark Streaming执行的入口
8. val ssc = new StreamingContext(conf, Seconds(5))
9. ssc.checkpoint("/root/Documents/SparkApps/checkpoint")
10. val soketDStream = ssc.socketTextStream("Master", 9999)
11. /// 业务处理逻辑 .....
12. ssc.start()
13. ssc.awaitTermination()
14. }
15. }

 

2 Spark Streaming的运行源码分析

2.1 创建StreamingContext

　

我们将基于以上实例例，粗略地分析一下Spark源码，提示一些有针对性的内容，以了解其运行的主要流程。

1）代码没有直接使用SparkContext，而是使用StreamingContext。

我们来看看StreamingContext 的源码片段：

1. /**
2. * Create a StreamingContext by providing the configuration necessary for a new SparkContext.
3. * @param conf a org.apache.spark.SparkConf object specifying Spark parameters
4. * @param batchDuration the time interval at which streaming data will be divided into batches
5. */
6. def this(conf: SparkConf, batchDuration: Duration) = {
7. this(StreamingContext.createNewSparkContext(conf), null, batchDuration)
8. }

没错，createNewSparkContext就是创建SparkContext：

1. private[streaming] def createNewSparkContext(conf: SparkConf): SparkContext = {
2. new SparkContext(conf)
3. }

 这说明Spark Streaming也是Spark上的一个应用程序。

2）案例最开始的地方肯定要通过数据流创建一个InputDStream。

1. val socketDstram = ssc.socketTextStream("Master", 9999)

socketTextStream方法定义如下：

1. /**
2. * Create a input stream from TCP source hostname:port. Data is received using
3. * a TCP socket and the receive bytes is interpreted as UTF8 encoded `\n` delimited
4. * lines.
5. * @param hostname      Hostname to connect to for receiving data
6. * @param port          Port to connect to for receiving data
7. * @param storageLevel  Storage level to use for storing the received objects
8. *                      (default: StorageLevel.MEMORY_AND_DISK_SER_2)
9. */
10. def socketTextStream(
11. hostname: String,
12. port: Int,
13. storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
14. ): ReceiverInputDStream[String] = withNamedScope("socket text stream") {
15. socketStream[String](hostname, port, SocketReceiver.bytesToLines, storageLevel)
16. }

3）可看到代码最后面调用socketStream。

socketStream定义如下：

1. /**
2. * Create a input stream from TCP source hostname:port. Data is received using
3. * a TCP socket and the receive bytes it interepreted as object using the given
4. * converter.
5. * @param hostname      Hostname to connect to for receiving data
6. * @param port          Port to connect to for receiving data
7. * @param converter     Function to convert the byte stream to objects
8. * @param storageLevel  Storage level to use for storing the received objects
9. * @tparam T            Type of the objects received (after converting bytes to objects)
10. */
11. def socketStream[T: ClassTag](
12. hostname: String,
13. port: Int,
14. converter: (InputStream) => Iterator[T],
15. storageLevel: StorageLevel
16. ): ReceiverInputDStream[T] = {
17. new SocketInputDStream[T](this, hostname, port, converter, storageLevel)
18. }

4）实际上生成SocketInputDStream。

SocketInputDStream类如下：

1. private[streaming]
2. class SocketInputDStream[T: ClassTag](
3. ssc_ : StreamingContext,
4. host: String,
5. port: Int,
6. bytesToObjects: InputStream => Iterator[T],
7. storageLevel: StorageLevel
8. ) extends ReceiverInputDStream[T](ssc_) {
9. def getReceiver(): Receiver[T] = {
10. new SocketReceiver(host, port, bytesToObjects, storageLevel)
11. }
12. }

SocketInputDStream继承ReceiverInputDStream。

其中实现getReceiver方法，返回SocketReceiver对象。
总结一下SocketInputDStream的继承关系：

SocketInputDStream -> ReceiverInputDStream -> InputDStream -> DStream。　　

 

5）DStream是生成RDD的模板，是逻辑级别，当达到Interval的时候这些模板会被batch data实例化成为RDD和DAG。

DStream的generatedRDDs：

1. // RDDs generated, marked as private[streaming] so that testsuites can access it
2. @transient
3. private[streaming] var generatedRDDs = new HashMap[Time, RDD[T]] ()

DStream的getOrCompute：

1. /**
2. * Get the RDD corresponding to the given time; either retrieve it from cache
3. * or compute-and-cache it.
4. */
5. private[streaming] final def getOrCompute(time: Time): Option[RDD[T]] = {
6. // If RDD was already generated, then retrieve it from HashMap,
7. // or else compute the RDD
8. generatedRDDs.get(time).orElse {
9. // Compute the RDD if time is valid (e.g. correct time in a sliding window)
10. // of RDD generation, else generate nothing.
11. if (isTimeValid(time)) {
12. val rddOption = createRDDWithLocalProperties(time, displayInnerRDDOps = false) {
13. // Disable checks for existing output directories in jobs launched by the streaming
14. // scheduler, since we may need to write output to an existing directory during checkpoint
15. // recovery; see SPARK-4835 for more details. We need to have this call here because
16. // compute() might cause Spark jobs to be launched.
17. PairRDDFunctions.disableOutputSpecValidation.withValue(true) {
18. compute(time)
19. }
20. }
21. rddOption.foreach { case newRDD =>
22. // Register the generated RDD for caching and checkpointing
23. if (storageLevel != StorageLevel.NONE) {
24. newRDD.persist(storageLevel)
25. logDebug(s"Persisting RDD ${newRDD.id} for time $time to $storageLevel")
26. }
27. if (checkpointDuration != null && (time - zeroTime).isMultipleOf(checkpointDuration)) {
28. newRDD.checkpoint()
29. logInfo(s"Marking RDD ${newRDD.id} for time $time for checkpointing")
30. }
31. generatedRDDs.put(time, newRDD)
32. }
33. rddOption
34. } else {
35. None
36. }
37. }
38. }

主要是生成RDD，再将生成的RDD放在HashMap中。具体生成RDD过程以后剖析。

目前大致讲了DStream和RDD这些核心概念在Spark Streaming中的使用。

 

2.2 启动StreamingContext

代码实例中的ssc.start() 方法启动StreamingContext,主要的逻辑发生在这个start方法中:

*  在StreamingContext调用start方法的内部其实是会启动JobScheduler的Start方法，进行消息循环，

*  在JobScheduler的start内部会构造JobGenerator和ReceiverTacker，并且调用JobGenerator和

*  ReceiverTacker的start方法：

*

*  1，JobGenerator启动后会不断的根据batchDuration生成一个个的Job

*  其实这里的Job不是Spark Core中所指的Job，它只是基于DStreamGraph而生成的RDD的DAG

*  而已，从Java角度讲，相当于Runnable接口实例，此时要想运行Job需要提交给JobScheduler，

*  在JobScheduler中通过线程池的方式找到一个单独的线程来提交Job到集群运行（其实是在线程中

*  基于RDD的Action触发真正的作业的运行）

*

*  2，ReceiverTracker启动后首先在Spark Cluster中启动Receiver（其实是在Executor中先启动

*  ReceiverSupervisor），在Receiver收到数据后会通过ReceiverSupervisor存储到Executor并且把

*  数据的Metadata信息发送给Driver中的ReceiverTracker，在ReceiverTracker内部会通过

*  ReceivedBlockTracker来管理接受到的元数据信息.

体现Spark Streaming应用运行流程的关键类如下图所示。

 

 

 

下面开启神奇的 源码分析之旅,过程痛苦,痛苦之后是大彻大悟的畅快...........

 

 

1）先看看ScreamingContext的start()。

start()方法启动StreamContext，由于Spark应用程序不能有多个SparkContext对象实例，所以Spark Streaming框架在启动时对状态进行判断。代码如下：

1. /**
2. * Start the execution of the streams.
3. *
4. * @throws IllegalStateException if the StreamingContext is already stopped.
5. */
6. def start(): Unit = synchronized {
7. state match {
8. case INITIALIZED =>
9. startSite.set(DStream.getCreationSite())
10. StreamingContext.ACTIVATION_LOCK.synchronized {
11. StreamingContext.assertNoOtherContextIsActive()
12. try {
13. validate()
14. // Start the streaming scheduler in a new thread, so that thread local properties
15. // like call sites and job groups can be reset without affecting those of the
16. // current thread.
17. //线程本地存储，线程有自己的私有属性，设置这些线程的时候不会影响其他线程，
18. ThreadUtils.runInNewThread("streaming-start") {
19. sparkContext.setCallSite(startSite.get)
20. sparkContext.clearJobGroup()
21. sparkContext.setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, "false")
22. //启动JobScheduler
23. scheduler.start()
24. }
25. state = StreamingContextState.ACTIVE
26. } catch {
27. case NonFatal(e) =>
28. logError("Error starting the context, marking it as stopped", e)
29. scheduler.stop(false)
30. state = StreamingContextState.STOPPED
31. throw e
32. }
33. StreamingContext.setActiveContext(this)
34. }
35. shutdownHookRef = ShutdownHookManager.addShutdownHook(
36. StreamingContext.SHUTDOWN_HOOK_PRIORITY)(stopOnShutdown)
37. // Registering Streaming Metrics at the start of the StreamingContext
38. assert(env.metricsSystem != null)
39. env.metricsSystem.registerSource(streamingSource)
40. uiTab.foreach(_.attach())
41. logInfo("StreamingContext started")
42. case ACTIVE =>
43. logWarning("StreamingContext has already been started")
44. case STOPPED =>
45. throw new IllegalStateException("StreamingContext has already been stopped")
46. }
47. }

初始状态时，会启动JobScheduler。

 

2）接着来看下JobScheduler的启动过程start()。

其中启动了EventLoop、StreamListenerBus、ReceiverTracker和jobGenerator等多项工作。

1. def start(): Unit = synchronized {
2. if (eventLoop != null) return // scheduler has already been started
3. logDebug("Starting JobScheduler")
4. eventLoop = new EventLoop[JobSchedulerEvent]("JobScheduler") {
5. override protected def onReceive(event: JobSchedulerEvent): Unit = processEvent(event)
6. override protected def onError(e: Throwable): Unit = reportError("Error in job scheduler", e)
7. }
8. // 启动消息循环处理线程。用于处理JobScheduler的各种事件。
9. eventLoop.start()
10. // attach rate controllers of input streams to receive batch completion updates
11. for {
12. inputDStream <- ssc.graph.getInputStreams
13. // rateController可以控制输入速度
14. rateController <- inputDStream.rateController
15. } ssc.addStreamingListener(rateController)
16. // 启动监听器。用于更新Spark UI中StreamTab的内容。
17. listenerBus.start(ssc.sparkContext)
18. receiverTracker = new ReceiverTracker(ssc)
19. // 生成InputInfoTracker。用于管理所有的输入的流，以及他们输入的数据统计。这些信息将通过 StreamingListener监听。
20. inputInfoTracker = new InputInfoTracker(ssc)
21. // 启动ReceiverTracker。用于处理数据接收、数据缓存、Block生成。
22. receiverTracker.start()
23. // 启动JobGenerator。用于DStreamGraph初始化、DStream与RDD的转换、生成Job、提交执行等工作。
24. jobGenerator.start()
25. logInfo("Started JobScheduler")
26. }

3）JobScheduler中的消息处理函数processEvent。

处理三类消息：Job已开始，Job已完成，错误报告。

1. private def processEvent(event: JobSchedulerEvent) {
2. try {
3. event match {
4. case JobStarted(job, startTime) => handleJobStart(job, startTime)
5. case JobCompleted(job, completedTime) => handleJobCompletion(job, completedTime)
6. case ErrorReported(m, e) => handleError(m, e)
7. }
8. } catch {
9. case e: Throwable =>
10. reportError("Error in job scheduler", e)
11. }
12. }

4）我们再粗略地分析一下JobScheduler.start()中启动的工作。

4.1）先看JobScheduler.start()启动的第一项工作EventLoop。

EventLoop用于处理JobScheduler的各种事件。

EventLoop中有事件队列：

1. private val eventQueue: BlockingQueue[E] = new LinkedBlockingDeque[E]()

还有一个线程处理队列中的事件：

1. private val eventThread = new Thread(name) {
2. setDaemon(true)
3. override def run(): Unit = {
4. try {
5. while (!stopped.get) {
6. val event = eventQueue.take()
7. try {
8. onReceive(event)
9. } catch {
10. case NonFatal(e) => {
11. try {
12. onError(e)
13. } catch {
14. case NonFatal(e) => logError("Unexpected error in " + name, e)
15. }
16. }
17. }
18. }
19. } catch {
20. case ie: InterruptedException => // exit even if eventQueue is not empty
21. case NonFatal(e) => logError("Unexpected error in " + name, e)
22. }
23. }
24. }

这个线程中的onReceive、onError，在JobScheduler中的EventLoop实例化时已定义。

4.2）JobScheduler.start()启动的第二项工作StreamListenerBus。

- 用于异步传递StreamingListenerEvents到注册的StreamingListeners。

- 用于更新Spark UI中StreamTab的内容。

 

 

4.3）看JobScheduler.start()启动的第三项工作ReceiverTracker。

ReceiverTracker用于管理所有的输入的流，以及他们输入的数据统计。

这些信息将通过 StreamingListener监听。

ReceiverTracker的start()中，会内部实例化ReceiverTrackerEndpoint这个Rpc消息通信体。

 

 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
   }
 }

 

在ReceiverTracker启动的过程中会调用其launchReceivers方法：

 

/**
 * 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 => {
    val rcvr = nis.getReceiver()
    rcvr.setReceiverId(nis.id)
    rcvr
  })
  runDummySparkJob()
  logInfo("Starting " + receivers.length + " receivers")
  endpoint.send(StartAllReceivers(receivers))
}
 

其中调用了runDummySparkJob方法来启动Spark Streaming的框架第一个Job，其中collect这个action操作会触发Spark Job的执行。这个方法是为了确保每个Slave都注册上，避免所有Receiver都在一个节点，使后面的计算能负载均衡。

 

/**
 * 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( to , ).map(x => (x, )).reduceByKey(_ + _, ).collect()
  }
  assert(getExecutors.nonEmpty)
}

ReceiverTracker.launchReceivers()还调用了endpoint.send(StartAllReceivers(receivers))方法，Rpc消息通信体发送StartAllReceivers消息。
ReceiverTrackerEndpoint它自己接收到消息后，先根据调度策略获得Recevier在哪个Executor上运行，然后在调用startReceiver(receiver, executors)方法，来启动Receiver。

override def receive: PartialFunction[Any, Unit] = {
  // Local messages
  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startReceiver方法中，ssc.sparkContext.submitJob提交Job的时候传入startReceiverFunc这个方法，因为startReceiverFunc该方法是在Executor上执行的。而在startReceiverFunc方法中实例化ReceiverSupervisorImpl对象，该对象是对Receiver进行管理和监控。这个Job是Spark Streaming框架为我们启动的第二个Job，且一直运行。因为supervisor.awaitTermination()该方法会阻塞等待退出。

 

/**
 * Start a receiver along with its scheduled executors
 */
private def startReceiver(
    receiver: Receiver[_],
    scheduledLocations: Seq[TaskLocation]): Unit = {
  def shouldStartReceiver: Boolean = {

    // ........... 此处省略1万字 (无关代码) , 呵呵哒 .........

  // 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() == ) {
        val receiver = iterator.next()
        assert(iterator.hasNext == false)
        //实例化Receiver监控者
        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.
      }
    }

  // 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), )
    } else {
      val preferredLocations = scheduledLocations.map(_.toString).distinct
      ssc.sc.makeRDD(Seq(receiver -> preferredLocations))
    }

  receiverRDD.setName(s"Receiver $receiverId")
  ssc.sparkContext.setJobDescription(s"Streaming job running receiver $receiverId")
  ssc.sparkContext.setCallSite(Option(ssc.getStartSite()).getOrElse(Utils.getCallSite()))
  val future = ssc.sparkContext.submitJob[Receiver[_], Unit, Unit](
    receiverRDD,
   startReceiverFunc, //提交Job时候传入startReceiverFunc这个方法，因为startReceiverFunc该方法是在Executor上执行的
  Seq(), (_, _) => Unit, ())

  // 一直重启 receiver job直到 ReceiverTracker is stopped
  future.onComplete {
    case Success(_) =>
      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")
        self.send(RestartReceiver(receiver))
      }
  }(submitJobThreadPool)
  logInfo(s"Receiver ${receiver.streamId} started")
}
 

接下来看下ReceiverSupervisorImpl的启动过程，先启动所有注册上的BlockGenerator对象，然后向ReceiverTrackerEndpoint发送RegisterReceiver消息，再调用receiver的onStart方法。

 

/** Start the supervisor */
def start() {
  onStart()
  startReceiver()
}

 

其中的onStart():启动所有注册上的BlockGenerator对象

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

 

其中的startReceiver()方法中调用onReceiverStart()然后再调用receiver的onStart方法。

 

/** Start receiver */
def startReceiver(): Unit = synchronized {
  try {
    if (onReceiverStart()) {
      logInfo("Starting receiver")
      receiverState = Started
      receiver.onStart()
      logInfo("Called receiver 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))
  }
}

 

在onReceiverStart()中向ReceiverTrackerEndpoint发送RegisterReceiver消息

 

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

 

其中在Driver运行的ReceiverTrackerEndpoint对象接收到RegisterReceiver消息后，将streamId, typ, host, executorId, receiverEndpoint封装为ReceiverTrackingInfo保存到内存对象receiverTrackingInfos这个HashMap中。

 

override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
  // Remote messages
  case RegisterReceiver(streamId, typ, host, executorId, receiverEndpoint) =>
    val successful =
      registerReceiver(streamId, typ, host, executorId, receiverEndpoint, context.senderAddress)
    context.reply(successful)
 

 

registerReceiver方法源码:

/** Register a receiver */
private def registerReceiver(
    streamId: Int,
    typ: String,
    host: String,
    executorId: String,
    receiverEndpoint: RpcEndpointRef,
    senderAddress: RpcAddress
  ): Boolean = {
  if (!receiverInputStreamIds.contains(streamId)) {
    throw new SparkException("Register received for unexpected id " + streamId)
  }

    // ........... 此处省略1万字 (无关代码) , 呵呵哒 .........

  if (!isAcceptable) {
    // Refuse it since it's scheduled to a wrong executor
    false
  } else {
    val name = s"${typ}-${streamId}"
    val receiverTrackingInfo = ReceiverTrackingInfo(
      streamId,
      ReceiverState.ACTIVE,
      scheduledLocations = None,
      runningExecutor = Some(ExecutorCacheTaskLocation(host, executorId)),
      name = Some(name),
      endpoint = Some(receiverEndpoint))
    receiverTrackingInfos.put(streamId, receiverTrackingInfo)
    listenerBus.post(StreamingListenerReceiverStarted(receiverTrackingInfo.toReceiverInfo))
    logInfo("Registered receiver for stream " + streamId + " from " + senderAddress)
    true
  }
}

 

Receiver的启动，以ssc.socketTextStream("localhost", 9999)为例，创建的是SocketReceiver对象。内部启动一个线程来连接Socket Server，读取socket数据并存储。

 

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()
  }

  /** 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 {
        // ........... 此处省略1万字 (无关代码) , 呵呵哒 .........
  }
}

 

4.4）接下来看JobScheduler.start()中启动的第四项工作JobGenerator。

JobGenerator有成员RecurringTimer，用于启动消息系统和定时器。按照batchInterval时间间隔定期发送GenerateJobs消息。

 

//根据创建StreamContext时传入的batchInterval，定时发送GenerateJobs消息
private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds,
  longTime => eventLoop.post(GenerateJobs(new Time(longTime))), "JobGenerator")

JobGenerator的start()方法：
/** Start generation of jobs */
def start(): Unit = synchronized {
  if (eventLoop != null) return // generator has already been started

  // Call checkpointWriter here to initialize it before eventLoop uses it to avoid a deadlock.
  // See SPARK-10125
  checkpointWriter

  eventLoop = new EventLoop[JobGeneratorEvent]("JobGenerator") {
    override protected def onReceive(event: JobGeneratorEvent): Unit = processEvent(event)

    override protected def onError(e: Throwable): Unit = {
      jobScheduler.reportError("Error in job generator", e)
    }
  }

  // 启动消息循环处理线程
  eventLoop.start()

  if (ssc.isCheckpointPresent) {
    restart()
  } else {
    // 开启定时生成Job的定时器
    startFirstTime()
  }
}

 

JobGenerator.start()中的startFirstTime()的定义：

/** Starts the generator for the first time */
private def startFirstTime() {
  val startTime = new Time(timer.getStartTime())
  graph.start(startTime - graph.batchDuration)
  timer.start(startTime.milliseconds)
  logInfo("Started JobGenerator at " + startTime)
}

JobGenerator.start()中的processEvent()的定义：

1. /** Processes all events */
2. private def processEvent(event: JobGeneratorEvent) {
3. logDebug("Got event " + event)
4. event match {
5. case GenerateJobs(time) => generateJobs(time)
6. case ClearMetadata(time) => clearMetadata(time)
7. case DoCheckpoint(time, clearCheckpointDataLater) =>
8. doCheckpoint(time, clearCheckpointDataLater)
9. case ClearCheckpointData(time) => clearCheckpointData(time)
10. }
11. }

其中generateJobs的定义：

/** Generate jobs and perform checkpoint for the given `time`.  */
private def generateJobs(time: Time) {
  // Set the SparkEnv in this thread, so that job generation code can access the environment
  // Example: BlockRDDs are created in this thread, and it needs to access BlockManager
  // Update: This is probably redundant after threadlocal stuff in SparkEnv has been removed.
  SparkEnv.set(ssc.env)
  Try {

    // 根据特定的时间获取具体的数据
    jobScheduler.receiverTracker.allocateBlocksToBatch(time) // allocate received blocks to batch
    //调用DStreamGraph的generateJobs生成Job
    graph.generateJobs(time) // generate jobs using allocated block
  } match {
    case Success(jobs) =>
      val streamIdToInputInfos = jobScheduler.inputInfoTracker.getInfo(time)
      jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToInputInfos))
    case Failure(e) =>
      jobScheduler.reportError("Error generating jobs for time " + time, e)
  }
  eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = false))
}

 

DStreamGraph的generateJobs方法，调用输出流的generateJob方法来生成Jobs集合。

// 输出流：具体Action的输出操作
private val outputStreams = new ArrayBuffer[DStream[_]]()

def generateJobs(time: Time): Seq[Job] = {
  logDebug("Generating jobs for time " + time)
  val jobs = this.synchronized {
    outputStreams.flatMap { outputStream =>
      val jobOption = outputStream.generateJob(time)
      jobOption.foreach(_.setCallSite(outputStream.creationSite))
      jobOption
    }
  }
  logDebug("Generated " + jobs.length + " jobs for time " + time)
  jobs
}

 

来看下DStream的generateJob方法，调用getOrCompute方法来获取当Interval的时候，DStreamGraph会被BatchData实例化成为RDD，如果有RDD则封装jobFunc方法，里面包含context.sparkContext.runJob(rdd, emptyFunc)，然后返回封装后的Job。

1. /**
2. * Generate a SparkStreaming job for the given time. This is an internal method that
3. * should not be called directly. This default implementation creates a job
4. * that materializes the corresponding RDD. Subclasses of DStream may override this
5. * to generate their own jobs.
6. */
7. private[streaming] def generateJob(time: Time): Option[Job] = {
8. getOrCompute(time) match {
9. case Some(rdd) => {
10. val jobFunc = () => {
11. val emptyFunc = { (iterator: Iterator[T]) => {} }
12. context.sparkContext.runJob(rdd, emptyFunc)
13. }
14. Some(new Job(time, jobFunc))
15. }
16. case None => None
17. }
18. }

接下来看JobScheduler的submitJobSet方法，向线程池中提交JobHandler。而JobHandler实现了Runnable 接口，最终调用了job.run()这个方法。看一下Job类的定义，其中run方法调用的func为构造Job时传入的jobFunc，其包含了context.sparkContext.runJob(rdd, emptyFunc)操作，最终导致Job的提交。

def submitJobSet(jobSet: JobSet) {
  if (jobSet.jobs.isEmpty) {
    logInfo("No jobs added for time " + jobSet.time)
  } else {
    listenerBus.post(StreamingListenerBatchSubmitted(jobSet.toBatchInfo))
    jobSets.put(jobSet.time, jobSet)
    jobSet.jobs.foreach(job => jobExecutor.execute(new JobHandler(job)))
    logInfo("Added jobs for time " + jobSet.time)
  }
}

 

JobHandler实现了Runnable 接口，最终调用了job.run()这个方法:

private class JobHandler(job: Job) extends Runnable with Logging {
    import JobScheduler._

    def run() {
      try {

         //  *********** 此处省略无关代码 *******************

        // We need to assign `eventLoop` to a temp variable. Otherwise, because
        // `JobScheduler.stop(false)` may set `eventLoop` to null when this method is running, then
        // it's possible that when `post` is called, `eventLoop` happens to null.
        var _eventLoop = eventLoop
        if (_eventLoop != null) {
          _eventLoop.post(JobStarted(job, clock.getTimeMillis()))
          // Disable checks for existing output directories in jobs launched by the streaming
          // scheduler, since we may need to write output to an existing directory during checkpoint
          // recovery; see SPARK-4835 for more details.
          PairRDDFunctions.disableOutputSpecValidation.withValue(true) {
            job.run()
          }
          _eventLoop = eventLoop
          if (_eventLoop != null) {
            _eventLoop.post(JobCompleted(job, clock.getTimeMillis()))
          }
        } else {
          // JobScheduler has been stopped.
        }
      } finally {
        ssc.sc.setLocalProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY, null)
        ssc.sc.setLocalProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY, null)
      }
    }
  }
}

Job的代码片段：

1. private[streaming]
2. class Job(val time: Time, func: () => _) {
3. private var _id: String = _
4. private var _outputOpId: Int = _
5. private var isSet = false
6. private var _result: Try[_] = null
7. private var _callSite: CallSite = null
8. private var _startTime: Option[Long] = None
9. private var _endTime: Option[Long] = None
10. def run() {
11. _result = Try(func())
12. }

 

以上是主要源码的分析,累死宝宝了,......慢慢的成就感