[spark streaming] 动态生成 Job 并提交执行
[spark streaming] 动态生成 Job 并提交执行
前言
Spark Streaming Job的生成是通过JobGenerator每隔 batchDuration 长时间动态生成的，每个batch 对应提交一个JobSet，因为针对一个batch可能有多个输出操作。
概述流程：
- 定时器定时向 eventLoop 发送生成job的请求
- 通过receiverTracker 为当前batch分配block
- 为当前batch生成对应的 Jobs
- 将Jobs封装成JobSet 提交执行
入口
在 JobGenerator 初始化的时候就创建了一个定时器：
private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds,
longTime => eventLoop.post(GenerateJobs(new Time(longTime))), "JobGenerator")每隔 batchDuration 就会向 eventLoop 发送 GenerateJobs(new Time(longTime))消息，eventLoop的事件处理方法中会调用generateJobs(time)方法：
case GenerateJobs(time) => generateJobs(time)private def generateJobs(time: Time) {
// Checkpoint all RDDs marked for checkpointing to ensure their lineages are
// truncated periodically. Otherwise, we may run into stack overflows (SPARK-6847).
ssc.sparkContext.setLocalProperty(RDD.CHECKPOINT_ALL_MARKED_ANCESTORS, "true")
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)
PythonDStream.stopStreamingContextIfPythonProcessIsDead(e)
}
eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = false))
}为当前batchTime分配Block
首先调用receiverTracker.allocateBlocksToBatch(time)方法为当前batchTime分配对应的Block，最终会调用receiverTracker的Block管理者receivedBlockTracker的allocateBlocksToBatch方法：
def allocateBlocksToBatch(batchTime: Time): Unit = synchronized {
if (lastAllocatedBatchTime == null || batchTime > lastAllocatedBatchTime) {
val streamIdToBlocks = streamIds.map { streamId =>
(streamId, getReceivedBlockQueue(streamId).dequeueAll(x => true))
}.toMap
val allocatedBlocks = AllocatedBlocks(streamIdToBlocks)
if (writeToLog(BatchAllocationEvent(batchTime, allocatedBlocks))) {
timeToAllocatedBlocks.put(batchTime, allocatedBlocks)
lastAllocatedBatchTime = batchTime
} else {
logInfo(s"Possibly processed batch $batchTime needs to be processed again in WAL recovery")
}
} else {
logInfo(s"Possibly processed batch $batchTime needs to be processed again in WAL recovery")
}
}private def getReceivedBlockQueue(streamId: Int): ReceivedBlockQueue = {
streamIdToUnallocatedBlockQueues.getOrElseUpdate(streamId, new ReceivedBlockQueue)
}可以看到是从streamIdToUnallocatedBlockQueues中获取到所有streamId对应的未分配的blocks，该队列的信息是supervisor 存储好Block后向receiverTracker上报的Block信息，详情可见 ReceiverTracker 数据产生与存储。
获取到所有streamId对应的未分配的blockInfos后，将其放入了timeToAllocatedBlocks:Map[Time, AllocatedBlocks]中，后面生成RDD的时候会用到。
为当前batchTime生成Jobs
调用DStreamGraph的generateJobs方法为当前batchTime生成job：
def generateJobs(time: Time): Seq[Job] = {
logDebug("Generating jobs for time " + time)
val jobs = this.synchronized {
outputStreams.flatMap { outputStream =>
val jobOption = outputStream.generateJob(time)
jobOption.foreach(_.setCallSite(outputStream.creationSite))
jobOption
}
}
logDebug("Generated " + jobs.length + " jobs for time " + time)
jobs
}一个outputStream就对应一个job，遍历所有的outputStreams，为其生成job：
# ForEachDStream
override def generateJob(time: Time): Option[Job] = {
parent.getOrCompute(time) match {
case Some(rdd) =>
val jobFunc = () => createRDDWithLocalProperties(time, displayInnerRDDOps) {
foreachFunc(rdd, time)
}
Some(new Job(time, jobFunc))
case None => None
}
}先获取到time对应的RDD，然后将其作为参数再调用foreachFunc方法，foreachFunc方法是通过构造器传过来的，我们来看看print()输出的情况：
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(s"Time: $time")
println("-------------------------------------------")
firstNum.take(num).foreach(println)
if (firstNum.length > num) println("...")
println()
// scalastyle:on println
}
}
foreachRDD(context.sparkContext.clean(foreachFunc), displayInnerRDDOps = false)
}这里的构造的foreachFunc方法就是最终和rdd一起提交job的执行方法，也即对rdd调用take()后并打印，真正触发action操作的是在这个func函数里，现在再来看看是怎么拿到rdd的，每个DStream都有一个generatedRDDs:Map[Time, RDD[T]]变量，来保存time对应的RDD，若获取不到则会通过compute()方法来计算，对于需要在executor上启动Receiver来接收数据的ReceiverInputDStream来说：
override def compute(validTime: Time): Option[RDD[T]] = {
val blockRDD = {
if (validTime < graph.startTime) {
// If this is called for any time before the start time of the context,
// then this returns an empty RDD. This may happen when recovering from a
// driver failure without any write ahead log to recover pre-failure data.
new BlockRDD[T](ssc.sc, Array.empty)
} else {
// Otherwise, ask the tracker for all the blocks that have been allocated to this stream
// for this batch
val receiverTracker = ssc.scheduler.receiverTracker
val blockInfos = receiverTracker.getBlocksOfBatch(validTime).getOrElse(id, Seq.empty)
// Register the input blocks information into InputInfoTracker
val inputInfo = StreamInputInfo(id, blockInfos.flatMap(_.numRecords).sum)
ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo)
// Create the BlockRDD
createBlockRDD(validTime, blockInfos)
}
}
Some(blockRDD)
}会通过receiverTracker来获取该batch对应的blocks，前面已经分析过为所有streamId分配了对应的未分配的block，并且放在了timeToAllocatedBlocks:Map[Time, AllocatedBlocks]中，这里底层就是从这个timeToAllocatedBlocks获取到的blocksInfo，然后调用了createBlockRDD(validTime, blockInfos)通过blockId创建了RDD。
最后，将通过此RDD和foreachFun构建jobFunc，并创建Job返回。
封装jobs成JobSet并提交执行
每个outputStream对应一个Job，最终就会生成一个jobs，为这个jobs创建JobSet，并通过jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToInputInfos))来提交这个JobSet：
jobSet.jobs.foreach(job => jobExecutor.execute(new JobHandler(job)))然后通过jobExecutor来执行，jobExecutor是一个线程池，并行度默认为1，可通过spark.streaming.concurrentJobs配置，即同时可执行几个批次的数据。
处理类JobHandler中调用的是Job.run()，执行的是前面构建的 jobFunc 方法。
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