(版本定制)第7课：Spark Streaming源码解读之JobScheduler内幕实现和深度思考
本期内容：
1、JobScheduler内幕实现
2、JobScheduler深度思考
JobScheduler是Spark Streaming的调度核心，地位相当于Spark Core上调度中心的DAG Scheduler，非常重要！
JobGenerator每隔Batch Duration时间会动态的生成JobSet提交给JobScheduler，JobScheduler接收到JobSet后，如何处理呢？
产生Job
/** 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 batch
graph.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))
}
处理产生的JobSet
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)
jobSet.jobs.foreach(job => jobExecutor.execute(new JobHandler(job)))
logInfo("Added jobs for time " + jobSet.time)
}
}
这里会为每个job生成一个新的JobHandler，交给jobExecutor运行。
这里最重要的处理逻辑是 job => jobExecutor.execute(new JobHandler(job))，也就是将每个 job 都在 jobExecutor 线程池中、用 new JobHandler 来处理
先来看JobHandler针对Job的主要处理逻辑：
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()))
}
也就是说，JobHandler除了做一些状态记录外，最主要的就是调用job.run()！这里就与我们在 DStream 生成 RDD 实例详解 里分析的对应起来了， 在ForEachDStream.generateJob(time)时，是定义了Job的运行逻辑，即定义了Job.func。而在JobHandler这里，是真正调用了Job.run()、将触发Job.func的真正执行！
def run() {
_result = Try(func())
}
参考博客：http://lqding.blog.51cto.com/9123978/1773391
备注：
资料来源于：DT_大数据梦工厂（Spark发行版本定制）
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