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本期内容：
1 Receiver生命周期
2 深度思考
一切不能进行实时流处理的数据都是无效的数据。在流处理时代，SparkStreaming有着强大吸引力，而且发展前景广阔，加之Spark的生态系统，Streaming可以方便调用其他的诸如SQL，MLlib等强大框架，它必将一统天下。
Spark Streaming运行时与其说是Spark Core上的一个流式处理框架，不如说是Spark Core上的一个最复杂的应用程序。如果可以掌握Spark streaming这个复杂的应用程序，那么其他的再复杂的应用程序都不在话下了。这里选择Spark Streaming作为版本定制的切入点也是大势所趋。
在SparkStreaming中，都是利用Reiceiver来接受数据的。而receiver在节点上都是通过receiverTracker来管理的。在receiverTracker中会调用start方法，来启动消息循环体ReceiverTrackerEndPoint进行通信。
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start方法会发送消息启动Receiver
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))
}
接下来会startAllReceivers，在这里通过消息循环体ReceiverTrackerEndPoint。
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)
}
在此方法中，如果发生Receiver对应的Job结束了，可是ReceiverTracker还没有停止。它将会向ReceiverTrackerEndpoint发送一个ReStartReceiver的方法来重新启动reveiver，它被封装在future.onComplete中
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)
备注：
资料来源于：DT_大数据梦工厂（Spark发行版本定制）
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Spark版本定制第9天：Receiver在Driver的精妙实现全生命周期彻底研究和思考
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原文地址：http://www.cnblogs.com/pzwxySpark/p/Spark9.html
