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
作者：海纳百川_spark
链接：https://www.jianshu.com/p/077fc812a666