本期内容：
一、Spark Streaming 数据清理总览
二、****Spark Streaming ****数据清理过程详解
三、****Spark Streaming ****数据清理的触发机制
Spark Streaming不像普通Spark 的应用程序，普通Spark程序运行完成后,中间数据会随着SparkContext的关闭而被销毁,而Spark Streaming一直在运行，不断计算，每一秒中在不断运行都会产生大量的中间数据，所以需要对对象及元数据需要定期清理。每个batch duration运行时不断触发job后需要清理rdd和元数据。下面我们就结合源码详细解析一下Spark Streaming程序的数据清理机制。
一、数据清理总览
Spark Streaming 运行过程中,随着时间不断产生Job,当job运行结束后,需要清理相应的数据(RDD,元数据信息,Checkpoint数据
),Job由JobGenerator定时产生,数据的清理也是有JobGenerator负责。
JobGenerator负责数据清理控制的代码位于一个消息循环体eventLoop中：
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()
其中的核心逻辑位于processEvent(event)函数中:
/** Processes all events */
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)
}
}
可以看到当JobGenerator收到ClearMetadata(time) 和 ClearCheckpointData(time)是会进行相应的数据清理,其中 clearMetadata(time)会清理RDD数据和一些元数据信息, ClearCheckpointData(time)会清理Checkpoint数据。
二、数据清理过程详解
2.1 ClearMetaData 过程详解
首先看一下clearMetaData函数的处理逻辑：
/** Clear DStream metadata for the given `time`. */
private def clearMetadata(time: Time) {
ssc.graph.clearMetadata(time)
// If checkpointing is enabled, then checkpoint,
// else mark batch to be fully processed
if (shouldCheckpoint) {
eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = true))
} else {
// If checkpointing is not enabled, then delete metadata information about
// received blocks (block data not saved in any case). Otherwise, wait for
// checkpointing of this batch to complete.
val maxRememberDuration = graph.getMaxInputStreamRememberDuration()
jobScheduler.receiverTracker.cleanupOldBlocksAndBatches(time - maxRememberDuration)
jobScheduler.inputInfoTracker.cleanup(time - maxRememberDuration)
markBatchFullyProcessed(time)
}
}
首先调用了DStreamGraph的clearMetadata方法:
def clearMetadata(time: Time) {
logDebug("Clearing metadata for time " + time)
this.synchronized {
outputStreams.foreach(_.clearMetadata(time))
}
logDebug("Cleared old metadata for time " + time)
}
这里调用了所有OutputDStream (关于DStream 的分类请参考http://blog.csdn.net/zhouzx2010/article/details/51460790)的clearMetadata方法
private[streaming] def clearMetadata(time: Time) {
val unpersistData = ssc.conf.getBoolean("spark.streaming.unpersist", true)
//获取需要清理的RDD
val oldRDDs = generatedRDDs.filter(_._1 <= (time - rememberDuration))
logDebug("Clearing references to old RDDs: [" +
oldRDDs.map(x => s"${x._1} -> ${x._2.id}").mkString(", ") + "]")
//将要清除的RDD从generatedRDDs 中清除
generatedRDDs --= oldRDDs.keys
if (unpersistData) {
logDebug(s"Unpersisting old RDDs: ${oldRDDs.values.map(_.id).mkString(", ")}")
oldRDDs.values.foreach { rdd =>
//将RDD 从persistence列表中移除
rdd.unpersist(false)
// Explicitly remove blocks of BlockRDD
rdd match {
case b: BlockRDD[_] =>
logInfo(s"Removing blocks of RDD $b of time $time")
//移除RDD的block 数据
b.removeBlocks()
case _ =>
}
}
}
logDebug(s"Cleared ${oldRDDs.size} RDDs that were older than " +
s"${time - rememberDuration}: ${oldRDDs.keys.mkString(", ")}")
//清除依赖的DStream
dependencies.foreach(_.clearMetadata(time))
}
关键的清理逻辑在代码中做了详细注释,首先清理DStream对应的RDD的元数据信息,然后清理RDD的数据,最后对DStream所依赖的DStream进行清理。
回到JobGenerator的clearMetadata函数：
if (shouldCheckpoint) {
eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = true))
} else {
// If checkpointing is not enabled, then delete metadata information about
// received blocks (block data not saved in any case). Otherwise, wait for
// checkpointing of this batch to complete.
val maxRememberDuration = graph.getMaxInputStreamRememberDuration()
jobScheduler.receiverTracker.cleanupOldBlocksAndBatches(time - maxRememberDuration)
jobScheduler.inputInfoTracker.cleanup(time - maxRememberDuration)
markBatchFullyProcessed(time)
}
调用了ReceiverTracker的 cleanupOldBlocksAndBatches方法，最后调用了clearupOldBatches方法：
def cleanupOldBatches(cleanupThreshTime: Time, waitForCompletion: Boolean): Unit = synchronized {
require(cleanupThreshTime.milliseconds < clock.getTimeMillis())
val timesToCleanup = timeToAllocatedBlocks.keys.filter { _ < cleanupThreshTime }.toSeq
logInfo(s"Deleting batches: ${timesToCleanup.mkString(" ")}")
if (writeToLog(BatchCleanupEvent(timesToCleanup))) {
//将要删除的Batch数据清除
timeToAllocatedBlocks --= timesToCleanup
//清理WAL日志
writeAheadLogOption.foreach(_.clean(cleanupThreshTime.milliseconds, waitForCompletion))
} else {
logWarning("Failed to acknowledge batch clean up in the Write Ahead Log.")
}
}
可以看到ReceiverTracker的clearupOldBatches方法清理了Receiver数据,也就是Batch数据和WAL日志数据。
最后对InputInfoTracker信息进行清理：
def cleanup(batchThreshTime: Time): Unit = synchronized {
val timesToCleanup = batchTimeToInputInfos.keys.filter(_ < batchThreshTime)
logInfo(s"remove old batch metadata: ${timesToCleanup.mkString(" ")}")
batchTimeToInputInfos --= timesToCleanup
}
这简单的清除了batchTimeToInputInfos 的输入信息。
2.2 ClearCheckPoint 过程详解
看一下clearCheckpointData的处理逻辑:****
/** Clear DStream checkpoint data for the given `time`. */
private def clearCheckpointData(time: Time) {
ssc.graph.clearCheckpointData(time)
// All the checkpoint information about which batches have been processed, etc have
// been saved to checkpoints, so its safe to delete block metadata and data WAL files
val maxRememberDuration = graph.getMaxInputStreamRememberDuration()
jobScheduler.receiverTracker.cleanupOldBlocksAndBatches(time - maxRememberDuration)
jobScheduler.inputInfoTracker.cleanup(time - maxRememberDuration)
markBatchFullyProcessed(time)
}
后面的ReceiverTraker和InputInforTracker的清理逻辑和ClearMetaData的相同,这分析DStreamGraph的clearCheckpointData方法:
def clearCheckpointData(time: Time) {
logInfo("Clearing checkpoint data for time " + time)
this.synchronized {
outputStreams.foreach(_.clearCheckpointData(time))
}
logInfo("Cleared checkpoint data for time " + time)
}
同样的调用了DStreamGraph中所有OutputDStream的clearCheckPiontData 方法:
private[streaming] def clearCheckpointData(time: Time) {
logDebug("Clearing checkpoint data")
checkpointData.cleanup(time)
dependencies.foreach(_.clearCheckpointData(time))
logDebug("Cleared checkpoint data")
}
这里的核心逻辑在checkpointData.cleanup(time)方法,这里的CheckpointData 是 DStreamCheckpointData对象, DStreamCheckpointData的clearup方法如下:
def cleanup(time: Time) {
// 获取需要清理的Checkpoint 文件 时间
timeToOldestCheckpointFileTime.remove(time) match {
case Some(lastCheckpointFileTime) =>
//获取需要删除的文件
val filesToDelete = timeToCheckpointFile.filter(_._1 < lastCheckpointFileTime)
logDebug("Files to delete:\n" + filesToDelete.mkString(","))
filesToDelete.foreach {
case (time, file) =>
try {
val path = new Path(file)
if (fileSystem == null) {
fileSystem = path.getFileSystem(dstream.ssc.sparkContext.hadoopConfiguration)
}
//
** 删除文件**
** **** **
fileSystem.delete(path, true)
timeToCheckpointFile -= time
logInfo("Deleted checkpoint file '" + file + "' for time " + time)
} catch {
case e: Exception =>
logWarning("Error deleting old checkpoint file '" + file + "' for time " + time, e)
fileSystem = null
}
}
case None =>
logDebug("Nothing to delete")
}
}
可以看到checkpoint的清理,就是删除了指定时间以前的checkpoint文件。
三、数据清理的触发
**3.1 ClearMetaData 过程的触发******
JobGenerator 生成job后,交给JobHandler执行, JobHandler的run方法中,会在job执行完后给JobScheduler 发送JobCompleted消息:
_eventLoop = eventLoop
if (_eventLoop != null) {
_eventLoop.post(JobCompleted(job, clock.getTimeMillis()))
}
JobScheduler 收到JobCompleted 消息调用 handleJobCompletion 方法,源码如下:
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)
}
}
在 JobScheduler 的handleJobCompletion方法中会调用JobGenerator的onBatchCompletion方法,我们看一下JobGenerator的 onBatchCompletion 方法的源码:
def onBatchCompletion(time: Time) {
eventLoop.post(ClearMetadata(time))
}
可以看到JobGenerator的onBatchCompletion方法给自己发送了ClearMetadata消息从而触发了ClearMetaData操作。
3.2 ****ClearCheckPoint ****过程的触发
清理CheckPoint数据发生在CheckPoint完成之后,我们先看一下CheckPointHandler的run方法:
// All done, print success
val finishTime = System.currentTimeMillis()
logInfo("Checkpoint for time " + checkpointTime + " saved to file '" + checkpointFile +
"', took " + bytes.length + " bytes and " + (finishTime - startTime) + " ms")
//调用JobGenerator的方法进行checkpoint数据清理
jobGenerator.onCheckpointCompletion(checkpointTime, clearCheckpointDataLater)
可以看到在checkpoint完成后,会调用JobGenerator的onCheckpointCompletion方法进行checkpoint数据清理,我查看JobGenerator的onCheckpointCompletion方法源码:
def onCheckpointCompletion(time: Time, clearCheckpointDataLater: Boolean) {
if (clearCheckpointDataLater) {
eventLoop.post(ClearCheckpointData(time))
}
}
可以看到JobGenerator的onCheckpointCompletion方法中首先对传进来的 clearCheckpointDataLater 参数进行判断,如果该参数为true,就会给JobGenerator的eventLoop循环体发送ClearCheckpointData消息,从而触发clearCheckpointData 方法的调用,进行Checkpoint数据的清理。
什么时候该参数会true呢？
我们回到JobGenerator的 ClearMetadata 方法：
private def clearMetadata(time: Time) {
ssc.graph.clearMetadata(time)
if (shouldCheckpoint) {
//发送DoCheckpoint消息,并进行相应的Checkpoint数据清理
eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = true))
} else {
val maxRememberDuration = graph.getMaxInputStreamRememberDuration()
jobScheduler.receiverTracker.cleanupOldBlocksAndBatches(time - maxRememberDuration)
jobScheduler.inputInfoTracker.cleanup(time - maxRememberDuration)
markBatchFullyProcessed(time)
}
}
可以看到在clearMetadata方法中,发送了DoCheckpoint消息,其中参数 clearCheckpointDataLater 为ture。Generator的eventLoop收到该消息后调用 doCheckpoint 方法：
private def doCheckpoint(time: Time, clearCheckpointDataLater: Boolean) {
if (shouldCheckpoint && (time - graph.zeroTime).isMultipleOf(ssc.checkpointDuration)) {
logInfo("Checkpointing graph for time " + time)
ssc.graph.updateCheckpointData(time)
checkpointWriter.write(new Checkpoint(ssc, time), clearCheckpointDataLater)
}
}
这里关键一步：调用了CheckpointWriter的write方法,注意此时参数 clearCheckpointDataLater 为true。我们进入该方法：
def write(checkpoint: Checkpoint, clearCheckpointDataLater: Boolean) {
try {
val bytes = Checkpoint.serialize(checkpoint, conf)
//将参数clearCheckpointDataLater传入CheckpoitWriteHandler
executor.execute(new CheckpointWriteHandler(
checkpoint.checkpointTime, bytes, clearCheckpointDataLater))
logInfo("Submitted checkpoint of time " + checkpoint.checkpointTime + " writer queue")
} catch {
case rej: RejectedExecutionException =>
logError("Could not submit checkpoint task to the thread pool executor", rej)
}
}
可以看到此时参数 clearCheckpointDataLater 传入CheckpointWriteHandler 。这样Checkpoint完成之后就会发送ClearCheckpointData消息给JobGenerator进行Checkpoint数据的清理。