今天就跟大家聊聊有关如何理解Receiver启动以及启动源码分析，可能很多人都不太了解，为了让大家更加了解，小编给大家总结了以下内容，希望大家根据这篇文章可以有所收获。
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为什么要Receiver？
Receiver不断持续接收外部数据源的数据，并把数据汇报给Driver端，这样我们每隔BatchDuration会把汇报数据生成不同的Job，来执行RDD的操作。
Receiver是随着应用程序的启动而启动的。
Receiver和InputDStream是一一对应的。
RDD[Receiver]只有一个Partition，一个Receiver实例。
Spark Core并不知道RDD[Receiver]的特殊性，依然按照普通RDD对应的Job进行调度，就有可能在同样一个Executor上启动多个Receiver，会导致负载不均衡，会导致Receiver启动失败。
Receiver在Executor启动的方案：
1，启动不同Receiver采用RDD中不同Partiton的方式，不同的Partiton代表不同的Receiver，在执行层面就是不同的Task，在每个Task启动时就启动Receiver。
这种方式实现简单巧妙，但是存在弊端启动可能失败，运行过程中Receiver失败，会导致TaskRetry，如果3次失败就会导致Job失败，会导致整个Spark应用程序失败。因为Receiver的故障，导致Job失败，不能容错。
2.第二种方式就是Spark Streaming采用的方式。
在ReceiverTacker的start方法中，先实例化Rpc消息通信体ReceiverTrackerEndpoint，再调用
launchReceivers方法。
/** Start the endpoint and receiver execution thread. */
defstart(): Unit = synchronized {
if(isTrackerStarted) {
throw newSparkException("ReceiverTracker already started")
}
if(!receiverInputStreams.isEmpty) {
endpoint = ssc.env.rpcEnv.setupEndpoint(
"ReceiverTracker", newReceiverTrackerEndpoint(ssc.env.rpcEnv))
if(!skipReceiverLaunch) launchReceivers()
logInfo("ReceiverTracker started")
trackerState = Started
}
} |
在launchReceivers方法中，先对每一个ReceiverInputStream获取到对应的一个Receiver，然后发送StartAllReceivers消息。Receiver对应一个数据来源。
/**
* Get the receivers from the ReceiverInputDStreams, distributes them to the
* worker nodes as a parallel collection, and runs them.
*/
private deflaunchReceivers(): Unit = {
valreceivers = receiverInputStreams.map(nis => {
valrcvr = nis.getReceiver()
rcvr.setReceiverId(nis.id)
rcvr
})
runDummySparkJob()
logInfo("Starting "+ receivers.length + " receivers")
endpoint.send(StartAllReceivers(receivers))
} |
ReceiverTrackerEndpoint接收到StartAllReceivers消息后，先找到Receiver运行在哪些Executor上，然后调用startReceiver方法。
override defreceive: PartialFunction[Any, Unit] = {
// Local messages
caseStartAllReceivers(receivers) =>
valscheduledLocations = schedulingPolicy.scheduleReceivers(receivers, getExecutors)
for(receiver <- receivers) {
valexecutors = scheduledLocations(receiver.streamId)
updateReceiverScheduledExecutors(receiver.streamId, executors)
receiverPreferredLocations(receiver.streamId) = receiver.preferredLocation
startReceiver(receiver, executors)
} |
startReceiver方法在Driver层面自己指定了TaskLocation，而不用Spark Core来帮我们选择TaskLocation。其有以下特点:终止Receiver不需要重启Spark Job；第一次启动Receiver，不会执行第二次；为了启动Receiver而启动了一个Spark作业，一个Spark作业启动一个Receiver。每个Receiver启动触发一个Spark作业，而不是每个Receiver是在一个Spark作业的一个Task来启动。当提交启动Receiver的作业失败时发送RestartReceiver消息，来重启Receiver。
/**
* Start a receiver along with its scheduled executors
*/
private defstartReceiver(
receiver: Receiver[_],
scheduledLocations: Seq[TaskLocation]): Unit = {
defshouldStartReceiver: Boolean = {
// It's okay to start when trackerState is Initialized or Started
!(isTrackerStopping || isTrackerStopped)
}
valreceiverId = receiver.streamId
if(!shouldStartReceiver) {
onReceiverJobFinish(receiverId)
return
}
valcheckpointDirOption = Option(ssc.checkpointDir)
valserializableHadoopConf =
newSerializableConfiguration(ssc.sparkContext.hadoopConfiguration)
// Function to start the receiver on the worker node
valstartReceiverFunc: Iterator[Receiver[_]] => Unit =
(iterator: Iterator[Receiver[_]]) => {
if(!iterator.hasNext) {
throw newSparkException(
"Could not start receiver as object not found.")
}
if(TaskContext.get().attemptNumber() == 0) {
valreceiver = iterator.next()
assert(iterator.hasNext == false)
valsupervisor = newReceiverSupervisorImpl(
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
valreceiverRDD: RDD[Receiver[_]] =
if(scheduledLocations.isEmpty) {
ssc.sc.makeRDD(Seq(receiver), 1)
} else{
valpreferredLocations = 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()))
valfuture = ssc.sparkContext.submitJob[Receiver[_], Unit, Unit](
receiverRDD, startReceiverFunc, Seq(0), (_, _) => Unit, ())
// We will keep restarting the receiver job until ReceiverTracker is stopped
future.onComplete {
caseSuccess(_) =>
if(!shouldStartReceiver) {
onReceiverJobFinish(receiverId)
} else{
logInfo(s"Restarting Receiver$receiverId")
self.send(RestartReceiver(receiver))
}
caseFailure(e) =>
if(!shouldStartReceiver) {
onReceiverJobFinish(receiverId)
} else{
logError("Receiver has been stopped. Try to restart it.", e)
logInfo(s"Restarting Receiver$receiverId")
self.send(RestartReceiver(receiver))
}
}(submitJobThreadPool)
logInfo(s"Receiver${receiver.streamId} started")
} |
看完上述内容，你们对如何理解Receiver启动以及启动源码分析有进一步的了解吗？如果还想了解更多知识或者相关内容，请关注创新互联行业资讯频道，感谢大家的支持。
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