(版本定制)第9课：SparkStreaming源码解读之
本期内容：
我们提供的服务有：成都做网站、成都网站建设、微信公众号开发、网站优化、网站认证、柴桑ssl等。为1000+企事业单位解决了网站和推广的问题。提供周到的售前咨询和贴心的售后服务，是有科学管理、有技术的柴桑网站制作公司
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：
当前题目：(版本定制)第9课：SparkStreaming源码解读之
URL网址：http://shouzuofang.com/article/gegjse.html