SparkSteaming运行流程分析以及CheckPoint操作
本文主要通过源码来了解SparkStreaming程序从任务生成到任务完成整个执行流程以及中间伴随的checkpoint操作
注：下面源码只贴出跟分析内容有关的代码，其他省略
1 分析流程
应用程序入口：
val sparkConf = new SparkConf().setAppName("SparkStreaming")
val sc = new SparkContext(sparkConf)
val ssc = new StreamingContext(sc, Seconds(batchDuration.toLong))
ssc.start()
ssc.awaitTermination()
一旦ssc.start()调用之后，程序便真正开始运行
第一步：
SparkStreamingContext.start()进行如下主要工作：
- 调用JobScheduler.start()
- 发送StreamingListenerStreamingStarted消息
JobScheduler.start()
def start(): Unit = synchronized {
state match {
case INITIALIZED =>
StreamingContext.ACTIVATION_LOCK.synchronized {
StreamingContext.assertNoOtherContextIsActive()
try｛
...
scheduler.start()
}
state = StreamingContextState.ACTIVE
scheduler.listenerBus.post(
StreamingListenerStreamingStarted(System.currentTimeMillis()))
} catch {
...
}
StreamingContext.setActiveContext(this)
}
...
case ACTIVE =>
logWarning("StreamingContext has already been started")
case STOPPED =>
throw new IllegalStateException("StreamingContext has already been stopped")
}
}
第二步：
调用JobScheduler.start()执行以下主要操作：
- 创建EventLoop用于处理接收到的JobSchedulerEvent，processEvent就是实际的处理逻辑
- 调用jobGenerator.start()
JobScheduler.start()：
def start(): Unit = synchronized {
if (eventLoop != null) return // scheduler has already been started
logDebug("Starting JobScheduler")
//创建一个Event监听器并启动
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()
...
//启动JobGenerator
jobGenerator.start()
...
}
第三步：
JobGenerator.start()执行以下主要操作：
- 创建EventLoop[JobGeneratorEvent]用于处理JobGeneratorEvent事件
- 开始执行job的生成工作
- 创建一个timer周期地执行eventLoop.post(GenerateJobs(new Time(longTime)))
- JobGenerator.start()中的EventLoop收到GenerateJobs事件后，去执行generateJobs(time)
- generateJobs()中生成JobSet并调用jobScheduler.submitJobSet()进行提交，然后发送一个DoCheckpointEvent进行checkpoint
JobGenerator.start()
def start(): Unit = synchronized {
if (eventLoop != null) return // generator has already been started
//创建checkpointWriter用于将checkpoint信息持久化
checkpointWriter
//创建了Event监听器，用于监听JobGeneratorEvent并处理
eventLoop = new EventLoop[JobGeneratorEvent]("JobGenerator") {
override protected def onReceive(event: JobGeneratorEvent): Unit = processEvent(event)
override protected def onError(e: Throwable): Unit = {
jobScheduler.reportError("Error in job generator", e)
}
}
eventLoop.start()
if (ssc.isCheckpointPresent) {
//从checkpoint中恢复
restart()
} else {
//首次创建
startFirstTime()
}
}
首次启动会调用startFirstTime()，在该方法中主要是调用已经初始化好的RecurringTimer.start()进行周期性的发送GenerateJobs事件，这个周期是ssc.graph.batchDuration.milliseconds也就是你所设置的batchTime，JobGenerate.start()中所创建好的EventLoop收到GenerateJobs事件，就会执行processEvent(),从而执行generateJobs(time)来进行Job的生成工作
private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds,
longTime => eventLoop.post(GenerateJobs(new Time(longTime))), "JobGenerator")
private def startFirstTime() {
val startTime = new Time(timer.getStartTime())
graph.start(startTime - graph.batchDuration)
timer.start(startTime.milliseconds)
logInfo("Started JobGenerator at " + startTime)
}
private def processEvent(event: JobGeneratorEvent) {
logDebug("Got event " + event)
event match {
case GenerateJobs(time) => generateJobs(time)
case ClearMetadata(time) => clearMetadata(time)
case DoCheckpoint(time, clearCheckpointDataLater) =>
doCheckpoint(time, clearCheckpointDataLater)
case ClearCheckpointData(time) => clearCheckpointData(time)
}
}
generateJobs的主要工作：
- 生成JobSet并调用jobScheduler.submitJobSet()进行提交
- 发送一个DoCheckpointEvent进行checkpoint
private def generateJobs(time: Time) {
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))
}
第一个操作：jobScheduler.submitJobSet()中的主要操作是遍历jobSet中的job，并将其作为参数传入JobHandler对象中，并将JobHandler丢到jobExecutor中去执行。JobHandler是实现了Runnable，它的run()主要做了以下三件事
- 发送JobStarted事件
- 执行Job.run()
- 发送JobCompleted事件
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)
}
}
private class JobHandler(job: Job) extends Runnable with Logging {
import JobScheduler._
def run() {
try {
var _eventLoop = eventLoop
if (_eventLoop != null) {
_eventLoop.post(JobStarted(job, clock.getTimeMillis()))//发送JobStarted事件
SparkHadoopWriterUtils.disableOutputSpecValidation.withValue(true) {
job.run()
}
_eventLoop = eventLoop
if (_eventLoop != null) {
_eventLoop.post(JobCompleted(job, clock.getTimeMillis()))//发送JobCompleted事件
}
} else {
}
} finally {
ssc.sparkContext.setLocalProperties(oldProps)
}
}
}
第二个操作：发送DoCheckpoint事件
JobScheduler.start()中创建的EventLoop的核心内容是processEvent(event)方法，Event的类型有三种，分别是JobStarted、JobCompleted和ErrorReported，EventLoop收到DoCheckpoint事件后会执行doCheckpoint()：
//JobGenerator.processEvent()
private def processEvent(event: JobGeneratorEvent) {
logDebug("Got event " + event)
event match {
...
case DoCheckpoint(time, clearCheckpointDataLater) =>
doCheckpoint(time, clearCheckpointDataLater)
...
}
}
doCheckpoint()调用graph.updateCheckpointData进行checkpoint信息的更新，调用checkpointWriter.write对checkpoint信息进行持久化
private def doCheckpoint(time: Time, clearCheckpointDataLater: Boolean) {
if (shouldCheckpoint && (time - graph.zeroTime).isMultipleOf(ssc.checkpointDuration)) {
logInfo("Checkpointing graph for time " + time)
//将新的checkpoint写到
ssc.graph.updateCheckpointData(time)
//将checkpoint写到文件系统中
checkpointWriter.write(new Checkpoint(ssc, time), clearCheckpointDataLater)
} else if (clearCheckpointDataLater) {
markBatchFullyProcessed(time)
}
}
checkpoint的update中主要是调用DStreamGraph.updateCheckpointData：
def updateCheckpointData(time: Time) {
logInfo("Updating checkpoint data for time " + time)
this.synchronized {
outputStreams.foreach(_.updateCheckpointData(time))
}
logInfo("Updated checkpoint data for time " + time)
}
outputStreams.foreach(_.updateCheckpointData(time))则是调用了DStream中的updateCheckpointData方法，而该方法主要是调用checkpointData.update(currentTime)来进行更新，并且调用该DStream所依赖的DStream进行更新。
private[streaming] var generatedRDDs = new HashMap[Time, RDD[T]]()
private[streaming] def updateCheckpointData(currentTime: Time) {
logDebug(s"Updating checkpoint data for time $currentTime")
checkpointData.update(currentTime)
dependencies.foreach(_.updateCheckpointData(currentTime))
logDebug(s"Updated checkpoint data for time $currentTime: $checkpointData")
}
我们接下来来看看checkpointData.update(currentTime)：我们可以在DStream中看到以下的实现：
private[streaming] val checkpointData = new DStreamCheckpointData(this)
我们接着找到了：DStreamCheckpointData.update，DStreamCheckpointData有其他子类用于自定义保存的内容和逻辑
//key为指定时间，value为checkpoint file内容
@transient private var timeToCheckpointFile = new HashMap[Time, String]
// key为batchtime，value该batch中最先checkpointed RDD的time
@transient private var timeToOldestCheckpointFileTime = new HashMap[Time, Time]
protected[streaming] def currentCheckpointFiles = data.asInstanceOf[HashMap[Time, String]]
def update(time: Time) {
//从dsteam中获得要checkpoint的RDDs,generatedRDDs就是一个HashMap[Time, RDD[T]]
val checkpointFiles = dstream.generatedRDDs.filter(_._2.getCheckpointFile.isDefined)
.map(x => (x._1, x._2.getCheckpointFile.get))
logDebug("Current checkpoint files:\n" + checkpointFiles.toSeq.mkString("\n"))
// checkpoint文件添加到最后要进行序列化的HashMap中
if (!checkpointFiles.isEmpty) {
currentCheckpointFiles.clear()
currentCheckpointFiles ++= checkpointFiles
//更新checkpointfile
timeToCheckpointFile ++= currentCheckpointFiles
// key为传入的time，value为最先进行checkpoint的rdd的time
timeToOldestCheckpointFileTime(time) = currentCheckpointFiles.keys.min(Time.ordering)
}
}
第四步：任务完成
在上面generateJobs中所调用的jobScheduler.submitJobSet()中提到每个Job都会作为参数传入JobHandler,而JobHandler会丢到JobExecutor中去执行，而JobHandler的主要工作是发送JobStarted事件，执行完任务后会发送JobCompleted事件，而JobScheduler.EventLoop收到事件后会执行handleJobCompletion方法
//JobScheduler.processEvent()
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)
}
}
handleJobCompletion方法会调用jobSet.handleJobCompletion(job)，并且在最后会判断jobSet是否已经全部完成，如果是的话会执行jobGenerator.onBatchCompletion(jobSet.time)
private def handleJobCompletion(job: Job, completedTime: Long) {
val jobSet = jobSets.get(job.time)
jobSet.handleJobCompletion(job)
job.setEndTime(completedTime)
listenerBus.post(StreamingListenerOutputOperationCompleted(job.toOutputOperationInfo))
logInfo("Finished job " + job.id + " from job set of time " + jobSet.time)
if (jobSet.hasCompleted) {
listenerBus.post(StreamingListenerBatchCompleted(jobSet.toBatchInfo))
}
job.result match {
case Failure(e) =>
reportError("Error running job " + job, e)
case _ => //如果所有事件完成则会执行以下操作
if (jobSet.hasCompleted) {
jobSets.remove(jobSet.time)
jobGenerator.onBatchCompletion(jobSet.time)
logInfo("Total delay: %.3f s for time %s (execution: %.3f s)".format(
jobSet.totalDelay / 1000.0, jobSet.time.toString,
jobSet.processingDelay / 1000.0
))
}
}
}
此时到JobGenerator中找到onBatchCompletion():
def onBatchCompletion(time: Time) {
eventLoop.post(ClearMetadata(time))
}
JobGenerator.processEvent()执行clearMetadata(time)
private def processEvent(event: JobGeneratorEvent) {
logDebug("Got event " + event)
event match {
case GenerateJobs(time) => generateJobs(time)
case ClearMetadata(time) => clearMetadata(time)
case DoCheckpoint(time, clearCheckpointDataLater) =>
doCheckpoint(time, clearCheckpointDataLater)
case ClearCheckpointData(time) => clearCheckpointData(time)
}
}
clearMetadata()对原数据进行checkpoint或者删除
private def clearMetadata(time: Time) {
ssc.graph.clearMetadata(time)
// If checkpointing is enabled, then checkpoint,
// else mark batch to be fully processed
if (shouldCheckpoint) {
//如果需要进行checkpoint，发送DoCheckpoint事件
eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = true))
} else {
//将meta数据进行删除
}
}
2 总结
到这里SparkStreaming的启动、任务生成、任务结束、Checkpoint操作基本就了解完毕了，最后我们来做一个总结，SparkStreming程序的运行流程如下：
- SparkStreamingContext.start() 启动 JobScheduler
- JobScheduler的启动操作
- JobScheduler 创建了 EventLoop[JobSchedulerEvent] 来处理 JobStarted 和 JobCompleted 事件
- 启动 JobGenerator
- JobGenerator 的启动操作
- JobGenerator 创建了 EventLoop[JobGeneratorEvent] 来处理 GenerateJobs、ClearMetaData、DoCheckPoint和ClearCheckpointData 事件
- 创建RecurringTimer周期性地发送 GenerateJobs 事件用于任务的周期性创建和执行
- JobGenerator的任务生成工作
- 调用 geneateJobs() 来生成 JobSet 并通过 JobScheduler.submitJobset 进行任务的提交
- submitJobset 将 Job 作为参数传入 JobHandler ，并将 JobHandler 丢进线程池 JobExecutor 中执行
- 发送 DoCheckPoint 事件进行元数据的 checkpoint
- 调用 geneateJobs() 来生成 JobSet 并通过 JobScheduler.submitJobset 进行任务的提交
- 任务完成
- JobHandler 中任务完成之后会发送 JobCompleted 事件，JobScheduler.EventLoop 接收到该事件后会进行处理，并且判断 JobSet 全部完成之后，发送 ClearMetaData 事件，进行数据的 checkpoint 或者删除
附：RecurringTimer和EventLoop的源码，并作简单的注释
RecurringTimer的代码如下：
private[streaming]
class RecurringTimer(clock: Clock, period: Long, callback: (Long) => Unit, name: String)
extends Logging {
//创建一个thread，用来执行loop
private val thread = new Thread("RecurringTimer - " + name) {
setDaemon(true)
override def run() { loop }
}
@volatile private var prevTime = -1L
@volatile private var nextTime = -1L
@volatile private var stopped = false
def getStartTime(): Long = {
(math.floor(clock.getTimeMillis().toDouble / period) + 1).toLong * period
}
def getRestartTime(originalStartTime: Long): Long = {
val gap = clock.getTimeMillis() - originalStartTime
(math.floor(gap.toDouble / period).toLong + 1) * period + originalStartTime
}
//start方法中主要是启动thread，用于执行thread中的loop
def start(startTime: Long): Long = synchronized {
nextTime = startTime
thread.start()
logInfo("Started timer for " + name + " at time " + nextTime)
nextTime
}
def start(): Long = {
start(getStartTime())
}
def stop(interruptTimer: Boolean): Long = synchronized {
if (!stopped) {
stopped = true
if (interruptTimer) {
thread.interrupt()
}
thread.join()
logInfo("Stopped timer for " + name + " after time " + prevTime)
}
prevTime
}
private def triggerActionForNextInterval(): Unit = {
clock.waitTillTime(nextTime)
callback(nextTime)
prevTime = nextTime
nextTime += period
logDebug("Callback for " + name + " called at time " + prevTime)
}
//周期性地执行callback的内容，也就是
private def loop() {
try {
while (!stopped) {
triggerActionForNextInterval()
}
triggerActionForNextInterval()
} catch {
case e: InterruptedException =>
}
}
}
EventLoop的源码：主要功能就是创建一个线程在后台判断是否Event进来，有的话则进行相应的处理
private[spark] abstract class EventLoop[E](name: String) extends Logging {
private val eventQueue: BlockingQueue[E] = new LinkedBlockingDeque[E]()
private val stopped = new AtomicBoolean(false)
private val eventThread = new Thread(name) {
setDaemon(true)
override def run(): Unit = {
try {
while (!stopped.get) {
val event = eventQueue.take()
try {
onReceive(event)
} catch {
case NonFatal(e) =>
try {
onError(e)
} catch {
case NonFatal(e) => logError("Unexpected error in " + name, e)
}
}
}
} catch {
case ie: InterruptedException => // exit even if eventQueue is not empty
case NonFatal(e) => logError("Unexpected error in " + name, e)
}
}
}
def start(): Unit = {
if (stopped.get) {
throw new IllegalStateException(name + " has already been stopped")
}
// Call onStart before starting the event thread to make sure it happens before onReceive
onStart()
eventThread.start()
}
def stop(): Unit = {
if (stopped.compareAndSet(false, true)) {
eventThread.interrupt()
var onStopCalled = false
try {
eventThread.join()
// Call onStop after the event thread exits to make sure onReceive happens before onStop
onStopCalled = true
onStop()
} catch {
case ie: InterruptedException =>
Thread.currentThread().interrupt()
if (!onStopCalled) {
// ie is thrown from `eventThread.join()`. Otherwise, we should not call `onStop` since
// it's already called.
onStop()
}
}
} else {
// Keep quiet to allow calling `stop` multiple times.
}
}
//将event放进eventQueue中，在eventThread进行相应的处理
def post(event: E): Unit = {
eventQueue.put(event)
}
//返回eventThread是否存活
def isActive: Boolean = eventThread.isAlive
//eventThread启动前调用
protected def onStart(): Unit = {}
//在eventThred结束后调用
protected def onStop(): Unit = {}
//实现类实现Event的处理逻辑
protected def onReceive(event: E): Unit
//出错时的处理逻辑
protected def onError(e: Throwable): Unit
}
