本篇文章为大家展示了如何进行JobScheduler内幕实现和深度思考，内容简明扼要并且容易理解，绝对能使你眼前一亮，通过这篇文章的详细介绍希望你能有所收获。
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DStream的foreachRDD方法，实例化ForEachDStream对象，并将用户定义的函数foreachFunc传入到该对象中。foreachRDD方法是输出操作，foreachFunc方法会作用到这个DStream中的每个RDD。
/**
* 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 foreachFuncforeachRDD function
* @param displayInnerRDDOpsWhether 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 defforeachRDD(
foreachFunc: (RDD[T], Time) => Unit,
displayInnerRDDOps: Boolean): Unit = {
newForEachDStream(this,
context.sparkContext.clean(foreachFunc, false), displayInnerRDDOps).register()
} |
ForEachDStream对象中重写了generateJob方法，调用父DStream的getOrCompute方法来生成RDD并封装Job，传入对该RDD的操作函数foreachFunc和time。dependencies方法定义为父DStream的集合。
/**
* 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 displayInnerRDDOpsWhether 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]
classForEachDStream[T: ClassTag] (
parent: DStream[T],
foreachFunc: (RDD[T], Time) => Unit,
displayInnerRDDOps: Boolean
) extendsDStream[Unit](parent.ssc) {
override defdependencies: List[DStream[_]] = List(parent)
override defslideDuration: Duration = parent.slideDuration
override defcompute(validTime: Time): Option[RDD[Unit]] = None
override defgenerateJob(time: Time): Option[Job] = {
parent.getOrCompute(time) match{
caseSome(rdd) =>
valjobFunc = () => createRDDWithLocalProperties(time, displayInnerRDDOps) {
foreachFunc(rdd, time)
}
Some(newJob(time, jobFunc))
caseNone => None
}
}
} |
DStreamGraph的generateJobs方法中会调用outputStream的generateJob方法，就是调用ForEachDStream的generateJob方法。
defgenerateJobs(time: Time): Seq[Job] = {
logDebug("Generating jobs for time "+ time)
valjobs = this.synchronized {
outputStreams.flatMap { outputStream =>
valjobOption = outputStream.generateJob(time)
jobOption.foreach(_.setCallSite(outputStream.creationSite))
jobOption
}
}
logDebug("Generated "+ jobs.length + " jobs for time "+ time)
jobs
} |
DStream的generateJob定义如下，其子类中只有ForEachDStream重写了generateJob方法。
/**
* 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] defgenerateJob(time: Time): Option[Job] = {
getOrCompute(time) match{
caseSome(rdd) => {
valjobFunc = () => {
valemptyFunc = { (iterator: Iterator[T]) => {} }
context.sparkContext.runJob(rdd, emptyFunc)
}
Some(newJob(time, jobFunc))
}
caseNone => None
}
} |
DStream的print方法内部还是调用foreachRDD来实现，传入了内部方法foreachFunc，来取出num+1个数后打印输出。
/**
* 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.
*/
defprint(num: Int): Unit = ssc.withScope {
defforeachFunc: (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)
} |
总结：JobScheduler是SparkStreaming 所有Job调度的中心，内部有两个重要的成员：
JobGenerator负责Job的生成，ReceiverTracker负责记录输入的数据源信息。
JobScheduler的启动会导致ReceiverTracker和JobGenerator的启动。ReceiverTracker的启动导致运行在Executor端的Receiver启动并且接收数据，ReceiverTracker会记录Receiver接收到的数据meta信息。JobGenerator的启动导致每隔BatchDuration，就调用DStreamGraph生成RDD Graph，并生成Job。JobScheduler中的线程池来提交封装的JobSet对象(时间值，Job，数据源的meta)。Job中封装了业务逻辑，导致最后一个RDD的action被触发，被DAGScheduler真正调度在Spark集群上执行该Job。
上述内容就是如何进行JobScheduler内幕实现和深度思考，你们学到知识或技能了吗？如果还想学到更多技能或者丰富自己的知识储备，欢迎关注创新互联行业资讯频道。
新闻标题：如何进行JobScheduler内幕实现和深度思考
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