SparkStreaming应用是持续不断地运行着的。如果不对内存资源进行有效管理，内存就有可能很快就耗尽。
SparkStreaming应用一定有自己的对象、数据、元数据的清理机制。
如果把SparkStreaming研究透彻了，那也就能驾驭Spark的各种应用程序。
SparkStreaming应用中的对象、数据、元数据，是我们操作DStream时产生的。
DStream：
private[streaming] vargeneratedRDDs= new HashMap[Time, RDD[T]] ()
DStream根据时间生成的RDD是放入了这个generatedRDDs中。
DStream的持久化：
/** Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER) */
def persist(): DStream[T] = persist(StorageLevel.MEMORY_ONLY_SER)
/** Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER) */
def cache(): DStream[T] = persist()
对DStream的cache就是对RDD的cache。
RDD产生、释放也应跟时钟有关的。JobGenerator：
private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds,
longTime => eventLoop.post(GenerateJobs(new Time(longTime))), "JobGenerator")
这个可以不断的发出事件。
JobScheduler的JobHandler会在需要时发出JobCompleted的消息。
JobScheduler.JobHandler.run：
...
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()))
}
} else {
// JobScheduler has been stopped.
}
...
JobScheduler.processEvent：
private def processEvent(event: JobSchedulerEvent) {
try {
event match {
case JobStarted(job, startTime) => handleJobStart(job, startTime)
caseJobCompleted(job, completedTime) =>handleJobCompletion(job, completedTime)
case ErrorReported(m, e) => handleError(m, e)
}
} catch {
case e: Throwable =>
reportError("Error in job scheduler", e)
}
}
JobCompleted事件的处理，是调用了handleJobCompletion。
JobScheduler.handleJobCompletion：
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) {
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
))
listenerBus.post(StreamingListenerBatchCompleted(jobSet.toBatchInfo))
}
job.result match {
case Failure(e) =>
reportError("Error running job " + job, e)
case _ =>
}
}
JobSet做了清理，还调用了jobGenerator.onBatchCompletion。
JobGenerator.onBatchCompletion：
/**
* Callback called when a batch has been completely processed.
*/
def onBatchCompletion(time: Time) {
eventLoop.post(ClearMetadata(time))
}
ClearMetadata消息和前面的GenerateJobs消息一样，也是在JobGenerator.processEvent里做处理。
JobGenerator.processEvent：
/** Processes all events */
private def processEvent(event: JobGeneratorEvent) {
logDebug("Got event " + event)
event match {
caseGenerateJobs(time) => generateJobs(time)
caseClearMetadata(time) =>clearMetadata(time)
case DoCheckpoint(time, clearCheckpointDataLater) =>
doCheckpoint(time, clearCheckpointDataLater)
case ClearCheckpointData(time) => clearCheckpointData(time)
}
}
其中也有清理元数据事件（ClearMetadata）对应的处理。
JobGenerator.clearMetadata：
/** Clear DStream metadata for the given `time`. */
private defclearMetadata(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)
}
}
可以看到有多项清理工作。
DStreamGraph.clearMetadata：
defclearMetadata(time: Time) {
logDebug("Clearing metadata for time " + time)
this.synchronized {
outputStreams.foreach(_.clearMetadata(time))
}
logDebug("Cleared old metadata for time " + time)
}
其中清理了ForeachDStream。
DStream.clearMetadata：
/**
* Clear metadata that are older than `rememberDuration` of this DStream.
* This is an internal method that should not be called directly. This default
* implementation clears the old generated RDDs. Subclasses of DStream may override
* this to clear their own metadata along with the generated RDDs.
*/
private[streaming] def clearMetadata(time: Time) {
val unpersistData = ssc.conf.getBoolean("spark.streaming.unpersist", true)
val oldRDDs = generatedRDDs.filter(_._1 <= (time -rememberDuration))
logDebug("Clearing references to old RDDs: [" +
oldRDDs.map(x => s"${x._1} -> ${x._2.id}").mkString(", ") + "]")
generatedRDDs --= oldRDDs.keys
if (unpersistData) {
logDebug("Unpersisting old RDDs: " + oldRDDs.values.map(_.id).mkString(", "))
oldRDDs.values.foreach { rdd =>
rdd.unpersist(false)
// Explicitly remove blocks of BlockRDD
rdd match {
case b: BlockRDD[_] =>
logInfo("Removing blocks of RDD " + b + " of time " + time)
b.removeBlocks()
case _ =>
}
}
}
logDebug("Cleared " + oldRDDs.size + " RDDs that were older than " +
(time - rememberDuration) + ": " + oldRDDs.keys.mkString(", "))
dependencies.foreach(_.clearMetadata(time))
}
spark.streaming.unpersist的配置可以用来设置是否手动清理。
想跨batch duration的话，可以设置rememberDuration。
其中把RDD清理掉了。依赖也清理掉了。
BlockRDD.removeBlocks：
/**
* Remove the data blocks that this BlockRDD is made from. NOTE: This is an
* irreversible operation, as the data in the blocks cannot be recovered back
* once removed. Use it with caution.
*/
private[spark] def removeBlocks() {
blockIds.foreach { blockId =>
sparkContext.env.blockManager.master.removeBlock(blockId)
}
_isValid = false
}
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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