一：Receiver启动的方式设想
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，在不同的机器之上启动，每个reveiver 相当于一个分片，由于Sapark core 感觉不到它的特殊性，按普通的调度，即有可能在同一个Executor之中启动多个Receiver，这种情况之下导致负载不均匀或者由于Executor运行本身的故障，task 有可能启动失败，整个job启动就失败，即receiver启动失败。
启动Receiver
1. 从Spark Core的角度来看，Receiver的启动Spark Core并不知道， Receiver是通过Job的方式启动的，运行在Executor之上的，由task运行。
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.
3.启动Receiver的时候,实其上一个receiver就是一个partition分片，由一个Job启动，这个Job里面有RDD的transformations操作和action的操作，随着定时器触发，不断的产生有数据接收，每个时间段中产生的接收数据实其上就是一个partition分片，
4. 以上设计思想产生的如下问题：
（1）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SparkCore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。（2）有可能启动Receiver失败，只要集群存在，Receiver就不应该启动失败。
（3）从运行过程中看，一个Reveiver就是一个partition的话，启动的由一个Task，如果Task启动失败，相应的Receiver也会失败。由此，可以得出，对于Receiver失败的话，后果是非常严重的，那么在SparkStreaming如何防止这些事的呢？Spark Streaming源码分析，在Spark Streaming之中就指定如下信息：
一是Spark使用一个Job启动一个Receiver.最大程度的保证了负载均衡。
二是Spark Streaming已经指定每个Receiver运行在那些Executor上，在Receiver运行之前就指定了运行的地方！
三是 如果Receiver启动失败，此时并不是Job失败，在内部会重新启动Receiver.
在StreamingContext的start方法被调用的时候，JobScheduler的start
def start(): Unit = synchronized {
state match {
caseINITIALIZED =>
startSite.set(DStream.getCreationSite())
StreamingContext.ACTIVATION_LOCK.synchronized {
StreamingContext.assertNoOtherContextIsActive()
try {
validate()
// Startthe streaming scheduler in a new thread, so that
thread local properties
// likecall 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")
//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。
scheduler.start()
}
state =StreamingContextState.ACTIVE
} catch {
caseNonFatal(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 newIllegalStateException("StreamingContext has already
been stopped")
}
}
2.而在JobScheduler的start方法中ReceiverTracker的start方法被调用，Receiver就启动了。
def start(): Unit = synchronized {
if (eventLoop !=null) return // scheduler has already been
started
logDebug("Starting JobScheduler")
eventLoop = newEventLoop[JobSchedulerEvent]("JobScheduler")
{
overrideprotected def onReceive(event: JobSchedulerEvent):
Unit = processEvent(event)
overrideprotected def onError(e: Throwable): Unit =
reportError("Error in jobscheduler", e)
}
eventLoop.start()
// attach ratecontrollers 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)
//启动receiverTracker
receiverTracker.start()
jobGenerator.start()
logInfo("Started JobScheduler")
}
3.ReceiverTracker的start方法启动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
def start(): Unit = synchronized {
if(isTrackerStarted) {
throw newSparkException("ReceiverTracker already started")
}
//Receiver的启动是依据输入数据流的。
if(!receiverInputStreams.isEmpty) {
endpoint =ssc.env.rpcEnv.setupEndpoint(
"ReceiverTracker",
newReceiverTrackerEndpoint(ssc.env.rpcEnv))
if(!skipReceiverLaunch) launchReceivers()
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
private def launchReceivers(): Unit = {
val receivers =receiverInputStreams.map(nis => {
//一个数据输入来源(receiverInputDStream)只产生一个Receiver
val rcvr =nis.getReceiver()
rcvr.setReceiverId(nis.id)
rcvr
})
runDummySparkJob()
logInfo("Starting " + receivers.length + "receivers")
//此时的endpoint就是上面代码中在ReceiverTracker的start方法中构造的ReceiverTrackerEndpoint
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。
def getReceiver(): Receiver[T] //返回的是Receiver对象
6. 根据继承关系，这里看一下SocketInputDStream中的getReceiver方法。
def getReceiver(): Receiver[T] = {
newSocketReceiver(host, port, bytesToObjects,
storageLevel)
}
}
启动后台线程，调用receive方法。
private[streaming]
class SocketReceiver[T: ClassTag](
host: String,
port: Int,
bytesToObjects:InputStream => Iterator[T],
storageLevel:StorageLevel
) extendsReceiver[T](storageLevel) with Logging {
def onStart() {
// Start thethread that receives data over a connection
newThread("Socket Receiver") {
setDaemon(true)
override defrun() { receive() }
}.start()
}
启动socket开始接收数据。
/** Create a socket connection and receive data untilreceiver is
stopped */
def receive() {
var socket:Socket = null
try {
logInfo("Connecting to " + host + ":" + port)
socket = newSocket(host, port)
logInfo("Connected to " + host + ":" + port)
val iterator= bytesToObjects(socket.getInputStream())
while(!isStopped && iterator.hasNext) {
store(iterator.next)
}
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)
caseNonFatal(e) =>
logWarning("Error receiving data", e)
restart("Error receiving data", e)
} finally {
if (socket !=null) {
socket.close()
logInfo("Closed socket to " + host + ":" + port)
}
}
}
}
7. ReceiverTrackerEndpoint源码如下：
/** RpcEndpoint to receive messages from the receivers.*/
private class ReceiverTrackerEndpoint(override valrpcEnv: RpcEnv)
extends ThreadSafeRpcEndpoint {
// TODO Removethis thread pool after
https://github.com/apache/spark/issues/7385 is merged
private valsubmitJobThreadPool =
ExecutionContext.fromExecutorService(
ThreadUtils.newDaemonCachedThreadPool("submit-job-thread-pool"))
private valwalBatchingThreadPool =
ExecutionContext.fromExecutorService(
ThreadUtils.newDaemonCachedThreadPool("wal-batching-thread-pool"))
@volatile privatevar active: Boolean = true
override defreceive: PartialFunction[Any, Unit] = {
// Localmessages
caseStartAllReceivers(receivers) =>
valscheduledLocations =
// schedulingPolicy调度策略
//receivers就是要启动的receiver
//getExecutors获得集群中的Executors的列表
// scheduleReceivers就可以确定receiver可以运行在哪些Executor上
schedulingPolicy.scheduleReceivers(receivers,getExecutors)
for (receiver<- receivers) {
//
scheduledLocations根据receiver的Id就找到了当前那些Executors可以运行Receiver
val executors= scheduledLocations(receiver.streamId)
updateReceiverScheduledExecutors(receiver.streamId,
executors)
receiverPreferredLocations(receiver.streamId)
=receiver.preferredLocation
//上述代码之后要启动的Receiver确定了，具体Receiver运行在哪些Executors上也确定了。
//循环receivers，每次将一个receiver传入过去。
startReceiver(receiver, executors)
}
//用于接收RestartReceiver消息，从新启动Receiver.
caseRestartReceiver(receiver) =>
// Oldscheduled executors minus the ones that are not active
any more
//如果Receiver失败的话，从可选列表中减去。
valoldScheduledExecutors =
//刚在调度为Receiver分配给哪个Executor的时候会有一些列可选的Executor列表
getStoredScheduledExecutors(receiver.streamId)
//从新获取Executors
valscheduledLocations = if (oldScheduledExecutors.nonEmpty)
{
// Tryglobal scheduling again
oldScheduledExecutors
} else {
//如果可选的Executor使用完了，则会重新执行rescheduleReceiver重新获取Executor.
valoldReceiverInfo =
receiverTrackingInfos(receiver.streamId)
// Clear"scheduledLocations" to indicate we are going to
do local scheduling
valnewReceiverInfo = oldReceiverInfo.copy(
state =ReceiverState.INACTIVE, scheduledLocations =
None)
receiverTrackingInfos(receiver.streamId) =
newReceiverInfo
schedulingPolicy.rescheduleReceiver(
receiver.streamId,
receiver.preferredLocation,
receiverTrackingInfos,
getExecutors)
}
// Assumethere is one receiver restarting at one time, so we
don't need to update
//receiverTrackingInfos
//重复调用startReceiver
startReceiver(receiver, scheduledLocations)
case c:CleanupOldBlocks =>
receiverTrackingInfos.values.flatMap(_.endpoint).foreach(_.send(c))
caseUpdateReceiverRateLimit(streamUID, newRate) =>
for (info<- receiverTrackingInfos.get(streamUID); eP
<- info.endpoint) {
eP.send(UpdateRateLimit(newRate))
}
// Remotemessages
caseReportError(streamId, message, error) =>
reportError(streamId, message, error)
}
8. 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。
Spark使用submitJob的方式启动Receiver,而在应用程序执行的时候会有很多Receiver，这个时候是启动一个Receiver呢，还是把所有的Receiver通过这一个Job启动？
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。
private def startReceiver(
receiver:Receiver[_],
// scheduledLocations指定的是在具体的那台物理机器上执行。
scheduledLocations: Seq[TaskLocation]): Unit = {
//判断下Receiver的状态是否正常。
defshouldStartReceiver: Boolean = {
// It's okay tostart when trackerState is Initialized or
Started
!(isTrackerStopping || isTrackerStopped)
}
val receiverId =receiver.streamId
//如果不需要启动Receiver则会调用onReceiverJobFinish()
if(!shouldStartReceiver) {
onReceiverJobFinish(receiverId)
return
}
valcheckpointDirOption = Option(ssc.checkpointDir)
valserializableHadoopConf =
newSerializableConfiguration(ssc.sparkContext.hadoopConfiguration)
//startReceiverFunc封装了在worker上启动receiver的动作。
// Function tostart 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)
// ReceiverSupervisorImpl是Receiver的监控器，同时负责数据的写等操作。
valsupervisor = new ReceiverSupervisorImpl(
receiver,SparkEnv.get, serializableHadoopConf.value,
checkpointDirOption)
supervisor.start()
supervisor.awaitTermination()
} else {
//如果你想重新启动receiver的话，你需要重新完成上面的调度，从新schedule，而不是Task重试。
// It'srestarted by TaskScheduler, but we want to
reschedule it again. So exit it.
}
}
// Create the RDDusing the scheduledLocations to run the
receiver in a Spark job
val receiverRDD:RDD[Receiver[_]] =
if(scheduledLocations.isEmpty) {
ssc.sc.makeRDD(Seq(receiver), 1)
} else {
valpreferredLocations =
scheduledLocations.map(_.toString).distinct
ssc.sc.makeRDD(Seq(receiver -> preferredLocations))
}
//receiverId可以看出，receiver只有一个
receiverRDD.setName(s"Receiver $receiverId")
ssc.sparkContext.setJobDescription(s"Streaming job running
receiver$receiverId")
ssc.sparkContext.setCallSite(Option(ssc.getStartSite()).getOrElse(Utils.getCallSite()))
//每个Receiver的启动都会触发一个Job,而不是一个作业的Task去启动所有的Receiver.
//应用程序一般会有很多Receiver,
//调用SparkContext的submitJob，为了启动Receiver，启动了Spark一个作业.
val future =ssc.sparkContext.submitJob[Receiver[_], Unit,
Unit](
receiverRDD,startReceiverFunc, Seq(0), (_, _) => Unit,
())
// We will keeprestarting the receiver job until ReceiverTracker
is stopped
future.onComplete{
case Success(_)=>
// shouldStartReceiver默认是true
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")
//RestartReceiver
self.send(RestartReceiver(receiver))
}
//使用线程池的方式提交Job，这样的好处是可以并发的启动Receiver。
}(submitJobThreadPool)
logInfo(s"Receiver ${receiver.streamId} started")
}
9. 当Receiver启动失败的话，就会调用ReceiverTrackEndpoint重新启动一个Spark
Job去启动Receiver.
/**
* This messagewill trigger ReceiverTrackerEndpoint to restart a
Spark job for the receiver.
*/
private[streaming] case class
RestartReceiver(receiver:Receiver[_])
extendsReceiverTrackerLocalMessage
11. 当Receiver关闭的话，并不需要重新启动Spark Job.
/**
* Call when areceiver is terminated. It means we won't restart
its Spark job.
*/
private def onReceiverJobFinish(receiverId: Int): Unit ={
receiverJobExitLatch.countDown()
//使用foreach将receiver从receiverTrackingInfo中去掉。
receiverTrackingInfos.remove(receiverId).foreach {
receiverTrackingInfo=>
if(receiverTrackingInfo.state == ReceiverState.ACTIVE) {
logWarning(s"Receiver $receiverId exited but didn't
deregister")
}
}
}
12.
Supervisor.start()，在子类ReceiverSupervisorImpl中并没有start方法，因此调用的是父类ReceiverSupervisor的start方法。
/** Start the supervisor */
def start() {
onStart() //具体实现是子类实现的。
startReceiver()
}
Onstart方法源码如下：
/**
* Called whensupervisor is started.
* Note that thismust be called before the receiver.onStart() is
called to ensure
* things like[[BlockGenerator]]s are started before the receiver
starts sending data.
*/
protected def onStart() { }
其具体实现是在子类的ReceiverSupervivorImpl的onstart方法
override protected def onStart() {
registeredBlockGenerators.foreach { _.start() }
}
此时的start方法调用的是BlockGenerator的start方法。
/** Start block generating and pushing threads. */
def start(): Unit = synchronized {
if (state ==Initialized) {
state = Active
blockIntervalTimer.start()
blockPushingThread.start()
logInfo("Started BlockGenerator")
} else {
throw newSparkException(
s"Cannotstart BlockGenerator as its not in the Initialized
state [state =$state]")
}
}
备注：
资料来源于：DT_大数据梦工厂（Spark发行版本定制）
更多私密内容，请关注微信公众号：DT_Spark
如果您对大数据Spark感兴趣，可以免费听由王家林老师每天晚上20：00开设的Spark永久免费公开课，地址YY房间号：68917580
网友评论