1、我们以Socket数据来源为例，通过WordCount计算来跟踪Receiver的启动

代码如下：

objectNetworkWordCount {

  defmain(args:Array[String]) {
    if (args.length< 2) {
      System.err.println("Usage: NetworkWordCount<hostname> <port>")
      System.exit(1)
    }

    val sparkConf= newSparkConf().setAppName("NetworkWordCount").setMaster("local[2]")
    val ssc = newStreamingContext(sparkConf,Seconds(1))
    val lines= ssc.socketTextStream(args(0), args(1).toInt,StorageLevel.MEMORY_AND_DISK_SER)
    val words= lines.flatMap(_.split(""))
    val wordCounts= words.map(x => (x,1)).reduceByKey(_ + _)
    wordCounts.print()
    ssc.start()
    ssc.awaitTermination()
  }
}

2、ssc.socketTextStream调用socketStream方法，在socketStream方法中new SocketInputDStream实例，
SocketInputDStream继承自ReceiverInputDStream。SocketInputDStream实现了getReceiver方法，
在getReceiver方法中实例化了一个SocketReceiver，SocketReceiver继承自Receiver类。
在SocketReceiver中主要实现了onStart方法，在onStart方法中启动一个线程来调用receive方法，
在receiver方法中就是具体接收数据的逻辑代码，通过Socket来读取数据然后包装到Iterator中，从
的start方法。直接看scheduler.start()这行代码，调用了JobScheduler的start方法，
看到receiverTracker.start()代码调用了receiverTracker的start方法。接着看launchReceivers()方法。
代码如下：

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))
}

3.1 首先看receiverInputStreams ，他在ReceiverTracker实例化的时候声明
private val receiverInputStreams = ssc.graph.getReceiverInputStreams()
看val rcvr = nis.getReceiver()，rcvr是Receiver的一个子类，就是我们上面看的SocketReceiver，这里返回的是receivers，因为receiver可能有多个。
3.2 runDummySparkJob()从字面上看就是运行一个样本的job来测试一下应用的启动情况，看一下代码，就是运行一个简单的job测试

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)
}

3.3 看最后一行代码endpoint.send(StartAllReceivers(receivers))，发送一条消息给ReceiverTrackerEndpoint, 而ReceiverTrackerEndpoint是在ReceiverTracker的start方法中被赋值的。
3.4 看ReceiverTrackerEndpoint中的消息接收方法，代码如下

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)
  }
  val scheduledLocations = schedulingPolicy.scheduleReceivers(receivers, getExecutors)

这行代码的作用就是计算第一个receiver可以运行的Executor,接下来看关键性的一行代码
startReceiver(receiver, executors)，代码如下：

private def startReceiver(
    receiver: Receiver[_],
    scheduledLocations: Seq[TaskLocation]): Unit = {

  def shouldStartReceiver: Boolean = {
    // It's okay to start when trackerState is Initialized or Started
    !(isTrackerStopping || isTrackerStopped)
  }

  val receiverId = receiver.streamId
  if (!shouldStartReceiver) {
    onReceiverJobFinish(receiverId)
    return
  }

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

  // 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.
      }
    }
  // 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))
    }

  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, Seq(0), (_, _) => Unit, ())
  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")
}

4、具体看一下startReceiver方法都做了什么
4.1 看startReceiverFunc函数的定义，startReceiverFunc就是job中action执行的函数，首先判断iterator中有数据，然后取第一条数据（就是Receiver）,看到这样的写法，真的非常神奇，把Receiver包装成RDD的数据发送到Executor上运行。

val supervisor = new ReceiverSupervisorImpl(receiver, SparkEnv.get, serializableHadoopConf.value, checkpointDirOption)
supervisor.start() 

4.2 把receiver传入ReceiverSupervisorImpl中，调用ReceiverSupervisorImpl的start方法，然后调用startReceiver，在startReceiver中调用receiver的onStart()方法，这就是前面提到的启动数据接收的方法
4.3 定义好action的函数，再来看receiverRDD，通过ssc.sc.makeRDD(Seq(receiver), 1)或ssc.sc.makeRDD(Seq(receiver -> preferredLocations))生成RDD
4.4 最后执行submitJob将RDD[Receiver]提交到集群，需要注意一点，每一个receiver生成一个job，如果一个Receiver的job失败不会影响整个应用的执行，job失败后重新发送self.send(RestartReceiver(receiver))消息，会重新提交job，保证receiver的可靠性，这样的设计值得学习

注：以上内容如有错误，欢迎指正