如何進行JobScheduler內幕實現(xiàn)和深度思考

/**
 * 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()
}

/**
 * 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
    }
  }
}

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
}

/**
 * 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
  }
}

/**
 * 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)
}