第7课：Spark Streaming源码解读之JobScheduler内幕实现和深度思考

一：JobSheduler的源码解析
1. JobScheduler是Spark Streaming整个调度的核心，相当于Spark Core上的DAGScheduler.
2. Spark Streaming为啥要设置两条线程？
setMaster指定的两条线程是指程序运行的时候至少需要两条线程。一条线程用于接收数据，需要不断的循环。而我们指定的线程数是用于作业处理的。
3. JobSheduler的启动是在StreamContext的start方法被调用的时候启动的。

def start(): Unit = synchronized {
state match {
case INITIALIZED =>
startSite.set(DStream.getCreationSite())
StreamingContext.ACTIVATION_LOCK.synchronized {
StreamingContext.assertNoOtherContextIsActive()        try {
validate()
//而这里面启动的新线程是调度方面的，因此和我们设置的线程数没有关系。
// 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()
}

4.  jobScheduler会负责逻辑层面的Job，并将其物理级别的运行在Spark之上.

/**
* This class schedules jobs to be run on Spark. It uses the JobGenerator to generate
* the jobs and runs them using a thread pool.
*/private[streaming]class JobScheduler(val ssc: StreamingContext) extends Logging {

5.  jobScheduler的start方法源码如下：

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()
jobGenerator.start()
logInfo("Started JobScheduler")
}

6.  其中processEvent的源码如下：

private def processEvent(event: JobSchedulerEvent) {  try {
event match {      case JobStarted(job, startTime) => handleJobStart(job, startTime)      case JobCompleted(job, completedTime) => handleJobCompletion(job, completedTime)      case ErrorReported(m, e) => handleError(m, e)
}
} catch {    case e: Throwable =>
reportError("Error in job scheduler", e)
}
}

7.  handleJobStart的源码如下：

private def handleJobStart(job: Job, startTime: Long) {
val jobSet = jobSets.get(job.time)
val isFirstJobOfJobSet = !jobSet.hasStarted
jobSet.handleJobStart(job)
if (isFirstJobOfJobSet) {
// "StreamingListenerBatchStarted" should be posted after calling "handleJobStart" to get the
// correct "jobSet.processingStartTime".
listenerBus.post(StreamingListenerBatchStarted(jobSet.toBatchInfo))
}
job.setStartTime(startTime)
listenerBus.post(StreamingListenerOutputOperationStarted(job.toOutputOperationInfo))
logInfo("Starting job " + job.id + " from job set of time " + jobSet.time)
}

8.  JobScheduler初始化的时候干了那些事？

此时为啥要设置并行度呢？
1) 如果Batch Duractions中有多个Output操作的话,提高并行度可以极大的提高性能。
2) 不同的Batch，线程池中有很多的线程，也可以并发运行。
将逻辑级别的Job转化为物理级别的job就是通过newDaemonFixedThreadPool线程实现的。

// Use of ConcurrentHashMap.keySet later causes an odd runtime problem due to Java 7/8 diff// https://gist.github.com/AlainODea/1375759b8720a3f9f094private val jobSets: java.util.Map[Time, JobSet] = new ConcurrentHashMap[Time, JobSet]//可以手动设置并行度private val numConcurrentJobs = ssc.conf.getInt("spark.streaming.concurrentJobs", 1)// numConcurrentJobs 默认是1private val jobExecutor =
ThreadUtils.newDaemonFixedThreadPool(numConcurrentJobs, "streaming-job-executor")//初始化JoGeneratorprivate val jobGenerator = new JobGenerator(this)val clock = jobGenerator.clock//val listenerBus = new StreamingListenerBus()// These two are created only when scheduler starts.// eventLoop not being null means the scheduler has been started and not stoppedvar receiverTracker: ReceiverTracker = null123456789101112131415161718123456789101112131415161718

print的函数源码如下：
1. DStream中的print源码如下：

/**
* Print the first ten elements of each RDD generated in this DStream. This is an output
* operator, so this DStream will be registered as an output stream and there materialized.
*/def print(): Unit = ssc.withScope {
print(10)
}

2.  实际调用的时候还是对RDD进行操作。

/**
* Print the first num elements of each RDD generated in this DStream. This is an output
* operator, so this DStream will be registered as an output stream and there materialized.
*/def print(num: Int): Unit = ssc.withScope {  def foreachFunc: (RDD[T], Time) => Unit = {
(rdd: RDD[T], time: Time) => {
val firstNum = rdd.take(num + 1)
// scalastyle:off println
println("-------------------------------------------")
println("Time: " + time)
println("-------------------------------------------")
firstNum.take(num).foreach(println)      if (firstNum.length > num) println("...")
println()
// scalastyle:on println
}
}
foreachRDD(context.sparkContext.clean(foreachFunc), displayInnerRDDOps = false)
}

3.  foreachFunc封装了RDD的操作。

/**
* Apply a function to each RDD in this DStream. This is an output operator, so
* 'this' DStream will be registered as an output stream and therefore materialized.
* @param foreachFunc foreachRDD function
* @param displayInnerRDDOps Whether the detailed callsites and scopes of the RDDs generated
*                           in the `foreachFunc` to be displayed in the UI. If `false`, then
*                           only the scopes and callsites of `foreachRDD` will override those
*                           of the RDDs on the display.
*/
private def foreachRDD(    foreachFunc: (RDD[T], Time) => Unit,    displayInnerRDDOps: Boolean): Unit = {  new ForEachDStream(this,
context.sparkContext.clean(foreachFunc, false), displayInnerRDDOps).register()
}

4.  每个BatchDuractions都会根据generateJob生成作业。

/**
* An internal DStream used to represent output operations like DStream.foreachRDD.
* @param parent        Parent DStream
* @param foreachFunc   Function to apply on each RDD generated by the parent DStream
* @param displayInnerRDDOps Whether the detailed callsites and scopes of the RDDs generated
*                           by `foreachFunc` will be displayed in the UI; only the scope and
*                           callsite of `DStream.foreachRDD` will be displayed.
*/
private[streaming]class ForEachDStream[T: ClassTag] (
parent: DStream[T],
foreachFunc: (RDD[T], Time) => Unit,
displayInnerRDDOps: Boolean
) extends DStream[Unit](parent.ssc) {

override def dependencies: List[DStream[_]] = List(parent)

override def slideDuration: Duration = parent.slideDuration

override def compute(validTime: Time): Option[RDD[Unit]] = None//每个Batch Duractions都根据generateJob生成Job
override def generateJob(time: Time): Option[Job] = {
parent.getOrCompute(time) match {
case Some(rdd) =>

val jobFunc = () => createRDDWithLocalProperties(time, displayInnerRDDOps) {
//foreachFunc基于rdd和time封装为func了，此时的foreachFunc就被job.run
//的时候调用了。
//此时的RDD就是基于时间生成的RDD，这个RDD就是DStreamGraph中的最后一个DStream决定的。然后
foreachFunc(rdd, time)
}
Some(new Job(time, jobFunc))
case None => None
}
}
}

5.  此时的foreachFunc是从哪里来的？

private[streaming]
//参数传递过来的，这个时候就要去找forEachDStream在哪里被调用。
class ForEachDStream[T: ClassTag] (    parent: DStream[T],    foreachFunc: (RDD[T], Time) => Unit,    displayInnerRDDOps: Boolean
) extends DStream[Unit](parent.ssc) {

6.   由此可以知道真正Job的生成是通过ForeachDStream通generateJob来生成的，此时是逻辑级别的，但是真正被物理级别的调用是在JobGenerator中generateJobs被调用的。

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

6.  由此可以知道真正Job的生成是通过ForeachDStream通过generateJob来生成的，此时是逻辑级别的，但是真正被物理级别的调用是在JobGenerator中generateJobs被调用的。

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

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