spark streaming 的 Job创建、调度、提交

2019独角兽企业重金招聘Python工程师标准>>>

上文已经从源码分析了Receiver接收的数据交由BlockManager管理，整个数据接收流都已经运转起来了，那么让我们回到分析JobScheduler的博客中。

// JobScheduler.scala line 62def start(): Unit = synchronized {if (eventLoop != null) return // scheduler has already been startedlogDebug("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 updatesfor {inputDStream <- ssc.graph.getInputStreamsrateController <- inputDStream.rateController} ssc.addStreamingListener(rateController)listenerBus.start(ssc.sparkContext)receiverTracker = new ReceiverTracker(ssc)inputInfoTracker = new InputInfoTracker(ssc)receiverTracker.start()jobGenerator.start()logInfo("Started JobScheduler")}

前面好几篇博客都是由 receiverTracker.start() 延展开。延展完毕后，继续下一步。

// JobScheduler.scala line 83
jobGenerator.start()

jobGenerator的实例化过程，前面已经分析过。深入下源码了解到。

实例化eventLoop，此处的eventLoop与JobScheduler中的eventLoop不一样，对应的是不同的泛型。
EventLoop.start
首次启动，startFirstTime

  // JobGenerator.scala line 78/** Start generation of jobs */def start(): Unit = synchronized {if (eventLoop != null) return // generator has already been started// Call checkpointWriter here to initialize it before eventLoop uses it to avoid a deadlock.// See SPARK-10125checkpointWritereventLoop = new EventLoop[JobGeneratorEvent]("JobGenerator") {override protected def onReceive(event: JobGeneratorEvent): Unit = processEvent(event)override protected def onError(e: Throwable): Unit = {jobScheduler.reportError("Error in job generator", e)}}eventLoop.start()if (ssc.isCheckpointPresent) {restart()} else {startFirstTime()}}

// JobGenerator.scala line 189/** Starts the generator for the first time */private def startFirstTime() {val startTime = new Time(timer.getStartTime())graph.start(startTime - graph.batchDuration)timer.start(startTime.milliseconds)logInfo("Started JobGenerator at " + startTime)}

将DStreamGraph.start

将所有的outputStreams都initialize，初始化首次执行时间，依赖的DStream一并设置。
如果设置了duration，将所有的outputStreams都remember，依赖的DStream一并设置
启动前验证，主要是验证chechpoint设置是否冲突以及各种Duration
将所有的inputStreams启动；读者扫描了下目前版本1.6.0InputDStraem及其所有的子类。start方法啥都没做。结合之前的博客，inputStreams都已经交由ReceiverTracker管理了。

// DStreamGraph.scala line 39def start(time: Time) {this.synchronized {require(zeroTime == null, "DStream graph computation already started")zeroTime = timestartTime = timeoutputStreams.foreach(_.initialize(zeroTime))outputStreams.foreach(_.remember(rememberDuration))outputStreams.foreach(_.validateAtStart)inputStreams.par.foreach(_.start())}}

至此，只是做了一些简单的初始化，并没有让数据处理起来。

再回到JobGenerator。此时，将循环定时器启动，

// JobGenerator.scala line 193timer.start(startTime.milliseconds)

循环定时器启动；读者是不是很熟悉，是不是在哪见过这个循环定时器？

没错，就是BlockGenerator.scala line 105 、109 ，两个线程，其中一个是循环定时器，定时将数据放入待push队列中。

// RecurringTimer.scala line 59def start(startTime: Long): Long = synchronized {nextTime = startTimethread.start()logInfo("Started timer for " + name + " at time " + nextTime)nextTime}

具体的逻辑是在构造是传入的方法：longTime => eventLoop.post(GenerateJobs(new Time(longTime)))；

输入是Long，

方法体是eventLoop.post(GenerateJobs(new Time(longTime)))

// JobGenerator.scala line 58private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds,longTime => eventLoop.post(GenerateJobs(new Time(longTime))), "JobGenerator")

只要线程状态不是stopped，一直循环。

初始化的时候将上面的方法传进来， callback: (Long) => Unit 对应的就是 longTime => eventLoop.post(GenerateJobs(new Time(longTime)))
start的时候 thread.run启动，里面的loop方法被执行。
loop中调用的是 triggerActionForNextInterval。
triggerActionForNextInterval调用构造传入的callback，也就是上面的 longTime => eventLoop.post(GenerateJobs(new Time(longTime)))

private[streaming]
class RecurringTimer(clock: Clock, period: Long, callback: (Long) => Unit, name: String)extends Logging {
// RecurringTimer.scala line 27private val thread = new Thread("RecurringTimer - " + name) {setDaemon(true)override def run() { loop }}
// RecurringTimer.scala line 56/*** Start at the given start time.*/def start(startTime: Long): Long = synchronized {nextTime = startTimethread.start()logInfo("Started timer for " + name + " at time " + nextTime)nextTime}
// RecurringTimer.scala line 92private def triggerActionForNextInterval(): Unit = {clock.waitTillTime(nextTime)callback(nextTime)prevTime = nextTimenextTime += periodlogDebug("Callback for " + name + " called at time " + prevTime)}// RecurringTimer.scala line 100/*** Repeatedly call the callback every interval.*/private def loop() {try {while (!stopped) {triggerActionForNextInterval()}triggerActionForNextInterval()} catch {case e: InterruptedException =>}}
// ...一些代码
}

定时发送GenerateJobs 类型的事件消息，eventLoop.post中将事件消息加入到eventQueue中

// EventLoop.scala line 102def post(event: E): Unit = {eventQueue.put(event)}

同时，此EventLoop中的另一个成员变量 eventThread。会一直从队列中取事件消息，将此事件作为参数调用onReceive。而此onReceive在实例化时被override了。

// JobGenerator.scala line 86eventLoop = new EventLoop[JobGeneratorEvent]("JobGenerator") {override protected def onReceive(event: JobGeneratorEvent): Unit = processEvent(event)override protected def onError(e: Throwable): Unit = {jobScheduler.reportError("Error in job generator", e)}}eventLoop.start()

onReceive调用的是

// JobGenerator.scala line 177/** Processes all events */private def processEvent(event: JobGeneratorEvent) {logDebug("Got event " + event)event match {case GenerateJobs(time) => generateJobs(time)// 其他case class}}

GenerateJobs case class 是匹配到 generateJobs(time:Time) 来处理

获取当前时间批次ReceiverTracker收集到的所有的Blocks，若开启WAL会执行WAL
DStreamGraph生产任务
提交任务
若设置checkpoint，则checkpoint

// JobGenerator.scala line 240/** Generate jobs and perform checkpoint for the given `time`.  */private def generateJobs(time: Time) {// Set the SparkEnv in this thread, so that job generation code can access the environment// Example: BlockRDDs are created in this thread, and it needs to access BlockManager// Update: This is probably redundant after threadlocal stuff in SparkEnv has been removed.SparkEnv.set(ssc.env)Try {jobScheduler.receiverTracker.allocateBlocksToBatch(time) // allocate received blocks to batchgraph.generateJobs(time) // generate jobs using allocated block} match {case Success(jobs) =>val streamIdToInputInfos = jobScheduler.inputInfoTracker.getInfo(time)jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToInputInfos))case Failure(e) =>jobScheduler.reportError("Error generating jobs for time " + time, e)}eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = false))}

上述代码不是特别容易理解。细细拆分：咋一看以为是try{} catch{case ... }，仔细一看，是Try{}match{}

追踪下代码，原来Try是大写的，是一个伴生对象，apply接收的参数是一个方法，返回Try的实例。在scala.util.Try.scala 代码如下：

// scala.util.Try.scala line 155
object Try {/** Constructs a `Try` using the by-name parameter.  This* method will ensure any non-fatal exception is caught and a* `Failure` object is returned.*/def apply[T](r: => T): Try[T] =try Success(r) catch {case NonFatal(e) => Failure(e)}}

Try有两个子类，都是case class 。分别是Success和Failure。如图。

再返回调用处，Try中的代码块最后执行的是 graph.generateJobs(time) 。跟踪下：

返回的是outputStream.generateJob(time)。

// DStreamGraph.scala line 111def generateJobs(time: Time): Seq[Job] = {logDebug("Generating jobs for time " + time)val jobs = this.synchronized {outputStreams.flatMap { outputStream =>val jobOption = outputStream.generateJob(time)jobOption.foreach(_.setCallSite(outputStream.creationSite))jobOption}}logDebug("Generated " + jobs.length + " jobs for time " + time)jobs}

从前文可知，outputStream其实都是ForEachDStream。进入ForEachDStream，override了generateJob。

parent.getOrCompute(time) 返回一个Option[Job]。
若有rdd，则返回可能是new Job(time,jobFunc)

// ForEachDStream.scala line 46override def generateJob(time: Time): Option[Job] = {parent.getOrCompute(time) match {case Some(rdd) =>val jobFunc = () => createRDDWithLocalProperties(time, displayInnerRDDOps) {foreachFunc(rdd, time)}Some(new Job(time, jobFunc))case None => None}}

那么ForEachDStream的parent是什么呢？看下我们的案例：

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Durations, StreamingContext}object StreamingWordCountSelfScala {def main(args: Array[String]) {val sparkConf = new SparkConf().setMaster("spark://master:7077").setAppName("StreamingWordCountSelfScala")val ssc = new StreamingContext(sparkConf, Durations.seconds(5)) // 每5秒收割一次数据val lines = ssc.socketTextStream("localhost", 9999) // 监听 本地9999 socket 端口val words = lines.flatMap(_.split(" ")).map((_, 1)).reduceByKey(_ + _) // flat map 后 reducewords.print() // 打印结果ssc.start() // 启动ssc.awaitTermination()ssc.stop(true)}
}

按照前文的描述：本例中 DStream的依赖是 SocketInputDStream << FlatMappedDStream << MappedDStream << ShuffledDStream << ForEachDStream

笔者扫描了下DStream及其所有子类，发现只有DStream有 getOrCompute，没有一个子类override了此方法。如此一来，是ShuffledDStream.getorCompute

在一般情况下，是RDD不存在，执行orElse代码快，

// DStream.scala line 338/*** Get the RDD corresponding to the given time; either retrieve it from cache* or compute-and-cache it.*/private[streaming] final def getOrCompute(time: Time): Option[RDD[T]] = {// If RDD was already generated, then retrieve it from HashMap,// or else compute the RDDgeneratedRDDs.get(time).orElse {// Compute the RDD if time is valid (e.g. correct time in a sliding window)// of RDD generation, else generate nothing.if (isTimeValid(time)) {val rddOption = createRDDWithLocalProperties(time, displayInnerRDDOps = false) {// Disable checks for existing output directories in jobs launched by the streaming// scheduler, since we may need to write output to an existing directory during checkpoint// recovery; see SPARK-4835 for more details. We need to have this call here because// compute() might cause Spark jobs to be launched.PairRDDFunctions.disableOutputSpecValidation.withValue(true) {compute(time)  // line 352}}rddOption.foreach { case newRDD =>// Register the generated RDD for caching and checkpointingif (storageLevel != StorageLevel.NONE) {newRDD.persist(storageLevel)logDebug(s"Persisting RDD ${newRDD.id} for time $time to $storageLevel")}if (checkpointDuration != null && (time - zeroTime).isMultipleOf(checkpointDuration)) {newRDD.checkpoint()logInfo(s"Marking RDD ${newRDD.id} for time $time for checkpointing")}generatedRDDs.put(time, newRDD)}rddOption} else {None}}}

ShuffledDStream.compute

又调用parent.getOrCompute

// ShuffledDStream.scala line 40override def compute(validTime: Time): Option[RDD[(K, C)]] = {parent.getOrCompute(validTime) match {case Some(rdd) => Some(rdd.combineByKey[C](createCombiner, mergeValue, mergeCombiner, partitioner, mapSideCombine))case None => None}}

MappedDStream的compute，又是父类的getOrCompute，结果又调用compute，如此循环。

// MappedDStream.scala line 34override def compute(validTime: Time): Option[RDD[U]] = {parent.getOrCompute(validTime).map(_.map[U](mapFunc))}

FlatMappedDStream的compute，又是父类的getOrCompute。结果又调用compute，如此循环。

// FlatMappedDStream.scala line 34override def compute(validTime: Time): Option[RDD[U]] = {parent.getOrCompute(validTime).map(_.flatMap(flatMapFunc))}

直到DStreamshi SocketInputDStream，也就是inputStream时，compute是继承自父类。

先不考虑if中的逻辑，直接else代码块。

进入createBlockRDD

// ReceiverInputDStream.scala line 69override def compute(validTime: Time): Option[RDD[T]] = {val blockRDD = {if (validTime < graph.startTime) {// If this is called for any time before the start time of the context,// then this returns an empty RDD. This may happen when recovering from a// driver failure without any write ahead log to recover pre-failure data.new BlockRDD[T](ssc.sc, Array.empty)} else {// Otherwise, ask the tracker for all the blocks that have been allocated to this stream// for this batchval receiverTracker = ssc.scheduler.receiverTrackerval blockInfos = receiverTracker.getBlocksOfBatch(validTime).getOrElse(id, Seq.empty)// Register the input blocks information into InputInfoTrackerval inputInfo = StreamInputInfo(id, blockInfos.flatMap(_.numRecords).sum)ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo)// Create the BlockRDDcreateBlockRDD(validTime, blockInfos)}}Some(blockRDD)}

new BlockRDD[T](ssc.sc, validBlockIds) line 127，RDD实例化成功

// ReceiverInputDStream.scala line 94private[streaming] def createBlockRDD(time: Time, blockInfos: Seq[ReceivedBlockInfo]): RDD[T] = {if (blockInfos.nonEmpty) {val blockIds = blockInfos.map { _.blockId.asInstanceOf[BlockId] }.toArray// Are WAL record handles present with all the blocksval areWALRecordHandlesPresent = blockInfos.forall { _.walRecordHandleOption.nonEmpty }if (areWALRecordHandlesPresent) {// If all the blocks have WAL record handle, then create a WALBackedBlockRDDval isBlockIdValid = blockInfos.map { _.isBlockIdValid() }.toArrayval walRecordHandles = blockInfos.map { _.walRecordHandleOption.get }.toArraynew WriteAheadLogBackedBlockRDD[T](ssc.sparkContext, blockIds, walRecordHandles, isBlockIdValid)} else {// Else, create a BlockRDD. However, if there are some blocks with WAL info but not// others then that is unexpected and log a warning accordingly.if (blockInfos.find(_.walRecordHandleOption.nonEmpty).nonEmpty) {if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) {logError("Some blocks do not have Write Ahead Log information; " +"this is unexpected and data may not be recoverable after driver failures")} else {logWarning("Some blocks have Write Ahead Log information; this is unexpected")}}val validBlockIds = blockIds.filter { id =>ssc.sparkContext.env.blockManager.master.contains(id)}if (validBlockIds.size != blockIds.size) {logWarning("Some blocks could not be recovered as they were not found in memory. " +"To prevent such data loss, enabled Write Ahead Log (see programming guide " +"for more details.")}new BlockRDD[T](ssc.sc, validBlockIds) // line 127}} else {// If no block is ready now, creating WriteAheadLogBackedBlockRDD or BlockRDD// according to the configurationif (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) {new WriteAheadLogBackedBlockRDD[T](ssc.sparkContext, Array.empty, Array.empty, Array.empty)} else {new BlockRDD[T](ssc.sc, Array.empty)}}}

此BlockRDD是Spark Core的RDD的子类，且没有依赖的RDD。至此，RDD的实例化已经完成。

// BlockRDD.scala line 30
private[spark]
class BlockRDD[T: ClassTag](sc: SparkContext, @transient val blockIds: Array[BlockId])extends RDD[T](sc, Nil) // RDd.scala line 74
abstract class RDD[T: ClassTag](@transient private var _sc: SparkContext,@transient private var deps: Seq[Dependency[_]]) extends Serializable with Logging

至此，最终还原回来的RDD：

new BlockRDD[T](ssc.sc, validBlockIds).map(_.flatMap(flatMapFunc)).map(_.map[U](mapFunc)).combineByKey[C](createCombiner, mergeValue, mergeCombiner, partitioner, mapSideCombine)。

在本例中则为

new BlockRDD[T](ssc.sc, validBlockIds).map(_.flatMap(t=>t.split(" "))).map(_.map[U](t=>(t,1))).combineByKey[C](t=>t, (t1,t2)=>t1+t2, (t1,t2)=>t1+t2,partitioner, true)

而最终的print为

() => foreachFunc(new BlockRDD[T](ssc.sc, validBlockIds).map(_.flatMap(t=>t.split(" "))).map(_.map[U](t=>(t,1))).combineByKey[C](t=>t, (t1,t2)=>t1+t2, (t1,t2)=>t1+t2,partitioner, true),time)

其中foreachFunc为 DStrean.scala line 766

至此，RDD已经通过DStream实例化完成，现在再回顾下，是否可以理解DStream是RDD的模版。

不过别急，回到ForEachDStream.scala line 46 ，将上述函数作为构造参数，传入Job。

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补充下Job创建的流程图，来源于版本定制班学员博客，略有修改。