本期内容:
1、Receiver启动方式的设想
2、Receiver启动源码彻底分析
一:Receiver启动方式的设想
1. Spark Streaming通过Receiver持续不断的从外部数据源接收数据,并把数据汇报给Driver端,由此每个Batch Durations就可以根据汇报的数据生成不同的Job。
2. Receiver是在Spark Streaming应用程序启动时启动的,那么我们找Receiver在哪里启动就应该去找Spark Streaming的启动。
3. Receivers和InputDStreams是一一对应的,默认情况下一般只有一个Receiver.
如何启动Receiver?
1. 从Spark Core的角度来看,Receiver的启动Spark Core并不知道,就相当于Linux的内核之上所有的都是应用程序,因此Receiver是通过Job的方式启动的
2. 一般情况下,只有一个Receiver,但是可以创建不同的数据来源的InputDStream.
final private[streaming] class DStreamGraph extends Serializable with Logging { private val inputStreams = new ArrayBuffer[InputDStream[_]]() //数组 private val outputStreams = new ArrayBuffer[DStream[_]]()
3. 启动Receiver的时候,启动一个Job,这个Job里面有RDD的transformations操作和action的操作,这个Job只有一个partition.这个partition的特殊是里面只有一个成员, 这个成员就是启动的Receiver. 4. 这样做的问题: a) 如果有多个InputDStream,那就要启动多个Receiver,每个Receiver也就相当于分片partition,那我们启动Receiver的时候理想的情况下是在不同的机器上启动Receiver, 但是Spark Core的角度来看就是应用程序,感觉不到Receiver的特殊性,所以就会按照正常的Job启动的方式来处理,极有可能在一个Executor上启动多个Receiver. 这样的话就可能导致负载不均衡。 b) 有可能启动Receiver失败,只要集群存在Receiver就不应该失败。 c) 运行过程中,就默认的而言如果是一个partition的话,那启动的时候就是一个Task,但是此Task也很可能失败,因此以Task启动的Receiver也会挂掉。
由此,可以得出,对于Receiver失败的话,后果是非常严重的,那么Spark Streaming如何防止这些事的呢,下面就寻找Receiver的创建
这里先给出答案,后面源码会详细分析:
a) Spark使用一个Job启动一个Receiver.最大程度的保证了负载均衡。
b) Spark Streaming指定每个Receiver运行在哪些Executor上。
c) 如果Receiver启动失败,此时并不是Job失败,在内部会重新启动Receiver.
接下来我们通过代码一步一步解析Receiver是如何启动的
1、首先我们在编写具体的应用程序的时候,都会调用StreamingContext的start方法,其实这就是job启动的源头,我们先来看下start方法的源码:
def start(): Unit = synchronized { state match { case INITIALIZED => startSite.set(DStream.getCreationSite()) StreamingContext.ACTIVATION_LOCK.synchronized { StreamingContext.assertNoOtherContextIsActive() try { validate() // Start the streaming scheduler in a new thread, so that thread local properties // like call sites and job groups can be reset without affecting those of the // current thread. ThreadUtils.runInNewThread("streaming-start") { sparkContext.setCallSite(startSite.get) sparkContext.clearJobGroup() sparkContext.setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, "false") scheduler.start() //启动JobScheduler的start方法,启动子线程,一方面为了本地初始化工作,另外一方面是不要阻塞主线程。 } state = StreamingContextState.ACTIVE } catch { case NonFatal(e) => logError("Error starting the context, marking it as stopped", e) scheduler.stop(false) state = StreamingContextState.STOPPED throw e } StreamingContext.setActiveContext(this) } shutdownHookRef = ShutdownHookManager.addShutdownHook( StreamingContext.SHUTDOWN_HOOK_PRIORITY)(stopOnShutdown) // Registering Streaming Metrics at the start of the StreamingContext assert(env.metricsSystem != null) env.metricsSystem.registerSource(streamingSource) uiTab.foreach(_.attach()) logInfo("StreamingContext started") case ACTIVE => logWarning("StreamingContext has already been started") case STOPPED => throw new IllegalStateException("StreamingContext has already been stopped") } }
2、上面调用start方法的时候,会调用JobScheduler的start()方法,在该方法里面,receiverTracker启动了,源码如下:
def start(): Unit = synchronized { if (eventLoop != null) return // scheduler has already been started logDebug("Starting JobScheduler") eventLoop = new EventLoop[JobSchedulerEvent]("JobScheduler") { override protected def onReceive(event: JobSchedulerEvent): Unit = processEvent(event) override protected def onError(e: Throwable): Unit = reportError("Error in job scheduler", e) } eventLoop.start() // attach rate controllers of input streams to receive batch completion updates for { inputDStream <- ssc.graph.getInputStreams rateController <- inputDStream.rateController } ssc.addStreamingListener(rateController) listenerBus.start(ssc.sparkContext) receiverTracker = new ReceiverTracker(ssc) inputInfoTracker = new InputInfoTracker(ssc) receiverTracker.start() //启动Receiver jobGenerator.start() logInfo("Started JobScheduler") }
3、我们接着看下receiverTracker的start()方法,在start方法里启动了RPC消息通信体,为啥呢?因为receiverTracker会监控整个集群中的Receiver,Receiver转过来要向ReceiverTrackerEndpoint汇报自己的状态,接收的数据,包括生命周期等信息
/** Start the endpoint and receiver execution thread. */ def start(): Unit = synchronized { if (isTrackerStarted) { throw new SparkException("ReceiverTracker already started") } if (!receiverInputStreams.isEmpty) { //Receiver的启动是依据数据流的 endpoint = ssc.env.rpcEnv.setupEndpoint( "ReceiverTracker", new ReceiverTrackerEndpoint(ssc.env.rpcEnv)) //汇报状态信息 if (!skipReceiverLaunch) launchReceivers() //发起Receiver logInfo("ReceiverTracker started") trackerState = Started } }
4、基于ReceiverInputDStream(是在Driver端)来获得具体的Receivers实例,然后再把他们分不到Worker节点上。一个ReceiverInputDStream只产生一个Receiver
/** * 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 => { //一个输入数据来源只产生一个Receiver val rcvr = nis.getReceiver() rcvr.setReceiverId(nis.id) rcvr }) runDummySparkJob() //启动虚拟Job来分配Receiver到不同的executor上 logInfo("Starting " + receivers.length + " receivers") endpoint.send(StartAllReceivers(receivers)) }
5、其中runDummySparkJob()为了确保所有节点活着,而且避免所有的receivers集中在一个节点上。
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) }
ReceiverInputDStream中的getReceiver()方法获得receiver对象然后将它发送到worker节点上实例化receiver,然后去接收数据。
此方法必须要在子类中实现。
/** * Gets the receiver object that will be sent to the worker nodes * to receive data. This method needs to defined by any specific implementation * of a ReceiverInputDStream. */ def getReceiver(): Receiver[T]
ReceiverInputDStream是抽象类,所以getReceiver方法必须要在继承的子类中实现
private[streaming] class SocketInputDStream[T: ClassTag]( ssc_ : StreamingContext, host: String, port: Int, bytesToObjects: InputStream => Iterator[T], storageLevel: StorageLevel ) extends ReceiverInputDStream[T](ssc_) { def getReceiver(): Receiver[T] = { new SocketReceiver(host, port, bytesToObjects, storageLevel) //调用SocketReceiver } } 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() } //启动线程,调用Receiver方法 }.start() }
在receive()方法中启动socket接收数据
/** 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) //根据我们应用程序传入的host和post创建socket对象 logInfo("Connected to " + host + ":" + port) val iterator = bytesToObjects(socket.getInputStream()) //接收数据 while(!isStopped && iterator.hasNext) { store(iterator.next) //接收后的数据进行存储 } if (!isStopped()) { restart("Socket data stream had no more data") } else { logInfo("Stopped receiving") } } catch { case e: java.net.ConnectException => restart("Error connecting to " + host + ":" + port, e) case NonFatal(e) => logWarning("Error receiving data", e) restart("Error receiving data", e) } finally { if (socket != null) { socket.close() logInfo("Closed socket to " + host + ":" + port) } } } }
6、ReceiverTrackerEndpoint源码如下:
override def receive: PartialFunction[Any, Unit] = { // Local messages case StartAllReceivers(receivers) => val scheduledLocations = schedulingPolicy.scheduleReceivers(receivers, getExecutors) // receivers就是要启动的receiver,getExecutors获得集群中的Executors的列表 for (receiver <- receivers) { val executors = scheduledLocations(receiver.streamId) updateReceiverScheduledExecutors(receiver.streamId, executors) receiverPreferredLocations(receiver.streamId) = receiver.preferredLocation startReceiver(receiver, executors) //循环receivers,每次将一个receiver传入过去。 } case RestartReceiver(receiver) => // Old scheduled executors minus the ones that are not active any more val oldScheduledExecutors = getStoredScheduledExecutors(receiver.streamId) val scheduledLocations = if (oldScheduledExecutors.nonEmpty) { // Try global scheduling again oldScheduledExecutors } else { val oldReceiverInfo = receiverTrackingInfos(receiver.streamId) // Clear "scheduledLocations" to indicate we are going to do local scheduling val newReceiverInfo = oldReceiverInfo.copy( state = ReceiverState.INACTIVE, scheduledLocations = None) receiverTrackingInfos(receiver.streamId) = newReceiverInfo schedulingPolicy.rescheduleReceiver( receiver.streamId, receiver.preferredLocation, receiverTrackingInfos, getExecutors) } // Assume there is one receiver restarting at one time, so we don't need to update // receiverTrackingInfos startReceiver(receiver, scheduledLocations) case c: CleanupOldBlocks => receiverTrackingInfos.values.flatMap(_.endpoint).foreach(_.send(c)) case UpdateReceiverRateLimit(streamUID, newRate) => for (info <- receiverTrackingInfos.get(streamUID); eP <- info.endpoint) { eP.send(UpdateRateLimit(newRate)) } // Remote messages case ReportError(streamId, message, error) => reportError(streamId, message, error) }
从注释中可以看到,Spark Streaming指定receiver在那些Executors运行,而不是基于Spark Core中的Task来指定。 通过StartAllReceivers将消息发送给ReceiverTrackerEndpoint
在for循环中为每个receiver分配相应的executor。并调用startReceiver方法:
Receiver是以job的方式启动的!!! 这里你可能会有疑惑,没有RDD和来的Job呢?首先,在startReceiver方法中,会将Receiver封装成RDD
receiverRDD: RDD[Receiver[_]] = (scheduledLocations.isEmpty) { ssc..makeRDD((receiver)) } { preferredLocations = scheduledLocations.map(_.toString).distinct ssc..makeRDD((receiver -> preferredLocations)) }
封装成RDD后,将RDD提交到集群中运行
future = ssc.sparkContext.submitJob[Receiver[_]]( receiverRDDstartReceiverFunc()(__) => ())
task被发送到executor中,从RDD中取出“Receiver”然后对它执行startReceiverFunc:
// 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注册 receiver, SparkEnv.get, serializableHadoopConf.value, checkpointDirOption) supervisor.start() //启动Receiver supervisor.awaitTermination() } else { // It's restarted by TaskScheduler, but we want to reschedule it again. So exit it. } }
在函数中创建了一个ReceiverSupervisorImpl对象。它用来管理具体的Receiver。
首先它会将Receiver注册到ReceiverTracker中
override protected def onReceiverStart(): Boolean = {
val msg = RegisterReceiver(
streamId, receiver.getClass.getSimpleName, host, executorId, endpoint)
trackerEndpoint.askWithRetry[Boolean](msg)
}
如果注册成功,通过supervisor.start()来启动Receiver
/** Start the supervisor */ def start() { onStart() startReceiver() //启动Receiver }
// We will keep restarting the receiver job until 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")
回到receiverTracker的startReceiver方法中,只要Receiver对应的Job结束了(无论是正常还是异常结束),而ReceiverTracker还没有停止。
它将会向ReceiverTrackerEndpoint发送一个ReStartReceiver的方法。
// We will keep restarting the receiver job until 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")
重新为Receiver选择一个executor,并再次运行Receiver。直到ReceiverTracker启动为止。
在ReceiverTracker的receive方法中startReceiver方法第一个参数就是receiver,从实现的可以看出for循环不断取出receiver,然后调用startReceiver。由此就可以得出一个Job只启动一个Receiver. 如果Receiver启动失败,此时并不会认为是作业失败,会重新发消息给ReceiverTrackerEndpoint重新启动Receiver,这样也就确保了Receivers一定会被启动,这样就不会像Task启动Receiver的话如果失败受重试次数的影响。
简单的流程图:
原创文章,作者:奋斗,如若转载,请注明出处:https://blog.ytso.com/183940.html