一：Spark Streaming Job生成深度思考
1. 做大数据例如Hadoop,Spark等，如果不是流处理的话，一般会有定时任务。例如10分钟触发一次，1个小时触发一次，这就是做流处理的感觉，一切不是流处理，或者与流处理无关的数据都将是没有价值的数据，以前做批处理的时候其实也是隐形的在做流处理。
2. JobGenerator构造的时候有一个核心的参数是jobScheduler, jobScheduler是整个作业的生成和提交给集群的核心，JobGenerator会基于DStream生成Job。这里面的Job就相当于Java中线程要处理的Runnable里面的业务逻辑封装。Spark的Job就是运行的一个作业。
3. Spark Streaming除了基于定时操作以外参数Job，还可以通过各种聚合操作，或者基于状态的操作。
4. 每5秒钟JobGenerator都会产生Job，此时的Job是逻辑级别的，也就是说有这个Job，并且说这个Job具体该怎么去做，此时并没有执行。具体执行的话是交给底层的RDD的action去触发，此时的action也是逻辑级别的。底层物理级别的，Spark Streaming他是基于DStream构建的依赖关系导致的Job是逻辑级别的，底层是基于RDD的逻辑级别的。
val ssc = new StreamingContext(conf, Seconds(5)) 5. Spark Streaming的触发器是以时间为单位的，storm是以事件为触发器，也就是基于一个又一个record. Spark Streaming基于时间，这个时间是Batch Duractions从逻辑级别翻译成物理级别，最后一个操作肯定是RDD的action，但是并不想一翻译立马就触发job。这个时候怎么办？
6. action触发作业，这个时候作为Runnable接口封装，他会定义一个方法，这个方法里面是基于DStream的依赖关系生成的RDD。翻译的时候是将DStream的依赖关系翻译成RDD的依赖关系，由于DStream的依赖关系最后一个是action级别的，翻译成RDD的时候，RDD的最后一个操作也应该是action级别的，如果翻译的时候直接执行的话，就直接生成了Job，就没有所谓的队列，所以会将翻译的事件放到一个函数中或者一个方法中，因此，如果这个函数没有指定的action触发作业是执行不了的。
7. Spark Streaming根据时间不断的去管理我们的生成的作业，所以这个时候我们每个作业又有action级别的操作，这个action操作是对DStream进行逻辑级别的操作，他生成每个Job放到队列的时候，他一定会被翻译为RDD的操作，那基于RDD操作的最后一个一定是action级别的，如果翻译的话直接就是触发action的话整个Spark Streaming的Job就不受管理了。因此我们既要保证他的翻译，又要保证对他的管理，把DStream之间的依赖关系转变为RDD之间的依赖关系，最后一个DStream使得action的操作，翻译成一个RDD之间的action操作，整个翻译后的内容他是一块内容，他这一块内容是放在一个函数体中的，这个函数体，他会函数的定义，这个函数由于他只是定义还没有执行，所以他里面的RDD的action不会执行，不会触发Job,当我们的JobScheduler要调度Job的时候，转过来在线程池中拿出一条线程执行刚才的封装的方法。
二：Spark Streaming Job生成源码解析
Spark 作业动态生成三大核心：
JobGenerator: 负责Job生成。
JobSheduler： 负责Job调度。
ReceiverTracker: 获取元数据。
1. JobScheduler的start方法被调用的时候，会启动JobGenerator的start方法。
/** Start generation of jobs */def start(): Unit = synchronized {//eventLoop是消息循环体，因为不断的生成Job 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-10125 checkpointWriter//匿名内部类 eventLoop = 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) } }//调用start方法。 eventLoop.start() if (ssc.isCheckpointPresent) { restart() } else { startFirstTime() }}EvenLoop: 的start方法被调用，首先会调用onstart方法。然后就启动线程。
/** * An event loop to receive events from the caller and process all events in the event thread. It * will start an exclusive event thread to process all events. * * Note: The event queue will grow indefinitely. So subclasses should make sure `onReceive` can * handle events in time to avoid the potential OOM. */private[spark] abstract class EventLoop[E](name: String) extends Logging { private val eventQueue: BlockingQueue[E] = new LinkedBlockingDeque[E]() private val stopped = new AtomicBoolean(false)//开启后台线程。 private val eventThread = new Thread(name) { setDaemon(true) override def run(): Unit = { try {//不断的从BlockQueue中拿消息。 while (!stopped.get) {//线程的start方法调用就会不断的循环队列，而我们将消息放到eventQueue中。 val event = eventQueue.take() try {// onReceive(event) } catch { case NonFatal(e) => { try { onError(e) } catch { case NonFatal(e) => logError("Unexpected error in " + name, e) } } } } } catch { case ie: InterruptedException => // exit even if eventQueue is not empty case NonFatal(e) => logError("Unexpected error in " + name, e) } } } def start(): Unit = { if (stopped.get) { throw new IllegalStateException(name + " has already been stopped") } // Call onStart before starting the event thread to make sure it happens before onReceive onStart() eventThread.start() }onReceive:不断的从消息队列中获得消息，一旦获得消息就会处理。
不要在onReceive中添加阻塞的消息，如果这样的话会不断的阻塞消息。
消息循环器一般都不会处理具体的业务逻辑，一般消息循环器发现消息以后都会将消息路由给其他的线程去处理。
/** * Invoked in the event thread when polling events from the event queue. * * Note: Should avoid calling blocking actions in `onReceive`, or the event thread will be blocked * and cannot process events in time. If you want to call some blocking actions, run them in * another thread. */protected def onReceive(event: E): Unit消息队列接收到事件后具体处理如下：
/** Processes all events */private def processEvent(event: JobGeneratorEvent) { logDebug("Got event " + event) event match { case GenerateJobs(time) => generateJobs(time) case ClearMetadata(time) => clearMetadata(time) case DoCheckpoint(time, clearCheckpointDataLater) => doCheckpoint(time, clearCheckpointDataLater) case ClearCheckpointData(time) => clearCheckpointData(time) }}基于Batch Duractions生成Job，并完成checkpoint.
Job生成的5个步骤。
/** 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 {//第一步：获取当前时间段里面的数据。根据分配的时间来分配具体要处理的数据。 jobScheduler.receiverTracker.allocateBlocksToBatch(time) // allocate received blocks to batch//第二步：生成Job，获取RDD的DAG依赖关系。在此基于DStream生成了RDD实例。 graph.generateJobs(time) // generate jobs using allocated block } match { case Success(jobs) =>//第三步：获取streamIdToInputInfos的信息。BacthDuractions要处理的数据，以及我们要处理的业务逻辑。 val streamIdToInputInfos = jobScheduler.inputInfoTracker.getInfo(time)//第四步：将生成的Job交给jobScheduler jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToInputInfos)) case Failure(e) => jobScheduler.reportError("Error generating jobs for time " + time, e) }//第五步：进行checkpoint eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = false))}此时的outputStream是整个DStream中的最后一个DStream，也就是foreachDStream.
def generateJobs(time: Time): Seq[Job] = { logDebug("Generating jobs for time " + time) val jobs = this.synchronized { outputStreams.flatMap { outputStream =>//根据最后一个DStream，然后根据时间生成Job. val jobOption = outputStream.generateJob(time) jobOption.foreach(_.setCallSite(outputStream.creationSite)) jobOption } } logDebug("Generated " + jobs.length + " jobs for time " + time) jobs}此时的JobFunc就是我们前面提到的用函数封装了Job。
generateJob基于给定的时间生成Spark Streaming 的Job，这个方法会基于我们的DStream的操作物化成了RDD，由此可以看出，DStream是逻辑级别的，RDD是物理级别的。
/** * Generate a SparkStreaming job for the given time. This is an internal method that * should not be called directly. This default implementation creates a job * that materializes the corresponding RDD. Subclasses of DStream may override this * to generate their own jobs. */private[streaming] def generateJob(time: Time): Option[Job] = { getOrCompute(time) match { case Some(rdd) => { val jobFunc = () => { val emptyFunc = { (iterator: Iterator[T]) => {} }//rdd => 就是RDD的依赖关系 context.sparkContext.runJob(rdd, emptyFunc) }//此时的 Some(new Job(time, jobFunc)) } case None => None }}Job这个类就代表了Spark业务逻辑，可能包含很多Spark Jobs.
/** * Class representing a Spark computation. It may contain multiple Spark jobs. */private[streaming]class Job(val time: Time, func: () => _) { private var _id: String = _ private var _outputOpId: Int = _ private var isSet = false private var _result: Try[_] = null private var _callSite: CallSite = null private var _startTime: Option[Long] = None private var _endTime: Option[Long] = None def run() {//调用func函数，此时这个func就是我们前面generateJob中的func _result = Try(func()) }此时put函数中的RDD是最后一个RDD，虽然触发Job是基于时间，但是也是基于DStream的action的。
/** * 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 RDD//基于时间生成RDD generatedRDDs.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) } }//然后对generated RDD进行checkpoint rddOption.foreach { case newRDD => // Register the generated RDD for caching and checkpointing if (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") }//以时间为Key,RDD为Value,此时的RDD为最后一个RDD generatedRDDs.put(time, newRDD) } rddOption } else { None } }}回到JobGenerator中的start方法。
if (ssc.isCheckpointPresent) {//如果不是第一次启动的话，就需要从checkpoint中恢复。 restart() } else {//否则的话，就是第一次启动。 startFirstTime() }}StartFirstTime的源码如下：
/** Starts the generator for the first time */private def startFirstTime() { val startTime = new Time(timer.getStartTime())//告诉DStreamGraph第一个Batch启动时间。 graph.start(startTime - graph.batchDuration)//timer启动，整个job不断生成就开始了。 timer.start(startTime.milliseconds) logInfo("Started JobGenerator at " + startTime)}这里的timer是RecurringTimer。RecurringTimer的start方法会启动内置线程thread.
private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds, longTime => eventLoop.post(GenerateJobs(new Time(longTime))), "JobGenerator")Timer.start源码如下：
/** * Start at the given start time. */def start(startTime: Long): Long = synchronized { nextTime = startTime //每次调用的 thread.start() logInfo("Started timer for " + name + " at time " + nextTime) nextTime}调用thread启动后台进程。
private val thread = new Thread("RecurringTimer - " + name) { setDaemon(true) override def run() { loop }}loop源码如下：
/** * Repeatedly call the callback every interval. */ private def loop() { try { while (!stopped) { triggerActionForNextInterval() } triggerActionForNextInterval() } catch { case e: InterruptedException => } }}tiggerActionForNextInterval源码如下：
private def triggerActionForNextInterval(): Unit = { clock.waitTillTime(nextTime) callback(nextTime) prevTime = nextTime += period logDebug("Callback for " + name + " called at time " + prevTime)}此时的callBack是RecurringTimer传入的。下面就去找callBack是谁传入的，这个时候就应该找RecurringTimer什么时候实例化的。
private[streaming]class RecurringTimer(clock: Clock, period: Long, callback: (Long) => Unit, name: String) extends Logging { private val thread = new Thread("RecurringTimer - " + name) { setDaemon(true) override def run() { loop } }在jobGenerator中，匿名函数会随着时间不断的推移反复被调用。
private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds,//匿名函数，复制给callback。 longTime => eventLoop.post(GenerateJobs(new Time(longTime))), "JobGenerator")而此时的eventLoop就是JobGenerator的start方法中eventLoop.eventLoop是一个消息循环体当收到generateJobs，就会将消息放到线程池中去执行。
至此，就知道了基于时间怎么生成作业的流程就贯通了。
Jobs: 此时的jobs就是jobs的业务逻辑，就类似于RDD之间的依赖关系，保存最后一个job，然后根据依赖关系进行回溯。
streamIdToInputInfos：基于Batch Duractions以及要处理的业务逻辑，然后就生成了JobSet.
jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToInputInfos))此时的JobSet就包含了数据以及对数据处理的业务逻辑。
/** Class representing a set of Jobs * belong to the same batch. */private[streaming]case class JobSet( time: Time, jobs: Seq[Job], streamIdToInputInfo: Map[Int, StreamInputInfo] = Map.empty) { private val incompleteJobs = new HashSet[Job]() private val submissionTime = System.currentTimeMillis() // when this jobset was submitted private var processingStartTime = -1L // when the first job of this jobset started processing private var processingEndTime = -1L // when the last job of this jobset finished processing jobs.zipWithIndex.foreach { case (job, i) => job.setOutputOpId(i) } incompleteJobs ++= jobs def handleJobStart(job: Job) { if (processingStartTime < 0) processingStartTime = System.currentTimeMillis() }submitJobSet:
def submitJobSet(jobSet: JobSet) { if (jobSet.jobs.isEmpty) { logInfo("No jobs added for time " + jobSet.time) } else { listenerBus.post(StreamingListenerBatchSubmitted(jobSet.toBatchInfo))// jobSets.put(jobSet.time, jobSet)//jobHandler jobSet.jobs.foreach(job => jobExecutor.execute(new JobHandler(job))) logInfo("Added jobs for time " + jobSet.time) }}JobHandle是一个Runnable接口，Job就是我们业务逻辑，代表的就是一系列RDD的依赖关系,job.run方法就导致了func函数的调用。
private class JobHandler(job: Job) extends Runnable with Logging { import JobScheduler._ def run() { try { val formattedTime = UIUtils.formatBatchTime( job.time.milliseconds, ssc.graph.batchDuration.milliseconds, showYYYYMMSS = false) val batchUrl = s"/streaming/batch/?id=${job.time.milliseconds}" val batchLinkText = s"[output operation ${job.outputOpId}, batch time ${formattedTime}]" ssc.sc.setJobDescription( s"""Streaming job from <a href="$batchUrl">$batchLinkText</a>""") ssc.sc.setLocalProperty(BATCH_TIME_PROPERTY_KEY, job.time.milliseconds.toString) ssc.sc.setLocalProperty(OUTPUT_OP_ID_PROPERTY_KEY, job.outputOpId.toString) // We need to assign `eventLoop` to a temp variable. Otherwise, because // `JobScheduler.stop(false)` may set `eventLoop` to null when this method is running, then // it's possible that when `post` is called, `eventLoop` happens to null. var _eventLoop = eventLoop if (_eventLoop != null) { _eventLoop.post(JobStarted(job, clock.getTimeMillis())) // 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. PairRDDFunctions.disableOutputSpecValidation.withValue(true) {// job.run() } _eventLoop = eventLoop if (_eventLoop != null) { _eventLoop.post(JobCompleted(job, clock.getTimeMillis())) } } else { // JobScheduler has been stopped. } } finally { ssc.sc.setLocalProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY, null) ssc.sc.setLocalProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY, null) } } }}此时的func就是基于DStream的业务逻辑。也就是RDD之间依赖的业务逻辑。
def run() { _result = Try(func())}总体架构如下：
本课程笔记来源于：
联系客服
