Spark Streaming源码解读之数据清理内幕彻底解密-

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Spark Streaming源码解读之数据清理内幕彻底解密

作者：snail_gesture 来源：博客发布于： 2016-6-23

2015 次浏览

本篇博客的主要目的是：

1. 理清楚Spark Streaming中数据清理的流程

组织思路如下：

a) 背景

b) 如何研究Spark Streaming数据清理？

c) 源码解析

一：背景

Spark Streaming数据清理的工作无论是在实际开发中，还是自己动手实践中都是会面临的，Spark Streaming中Batch Durations中会不断的产生RDD，这样会不断的有内存对象生成，其中包含元数据和数据本身。由此Spark Streaming本身会有一套产生元数据以及数据的清理机制。

二：如何研究Spark Streaming数据清理？

操作DStream的时候会产生元数据，所以要解决RDD的数据清理工作就一定要从DStream入手。因为DStream是RDD的模板，DStream之间有依赖关系。

DStream的操作产生了RDD,接收数据也靠DStream，数据的输入，数据的计算，输出整个生命周期都是由DStream构建的。由此，DStream负责RDD的整个生命周期。因此研究的入口的是DStream。

基于Kafka数据来源，通过Direct的方式访问Kafka,DStream随着时间的进行，会不断的在自己的内存数据结构中维护一个HashMap,HashMap维护的就是时间窗口，以及时间窗口下的RDD.按照Batch Duration来存储RDD以及删除RDD.

Spark Streaming本身是一直在运行的，在自己计算的时候会不断的产生RDD，例如每秒Batch Duration都会产生RDD,除此之外可能还有累加器，广播变量。由于不断的产生这些对象，因此Spark Streaming有自己的一套对象，元数据以及数据的清理机制。

Spark Streaming对RDD的管理就相当于JVM的GC.

三：源码解析

generatedRDDs:安照Batch Duration的方式来存储RDD以及删除RDD。

// RDDs generated, marked as private[streaming] so that testsuites can access it
@transient
private[streaming] var generatedRDDs = new HashMap[Time, RDD[T]] ()

我们在实际开发中，可能手动缓存，即使不缓存的话，它在内存generatorRDD中也有对象，如何释放他们？不仅仅是RDD本身，也包括数据源(数据来源)和元数据(metada),因此释放RDD的时候这三方面都需要考虑。

释放跟时钟Click有关系，因为数据是周期性产生，所以肯定是周期性释放。

因此下一步就需要找JobGenerator

RecurringTimer: 消息循环器将消息不断的发送给EventLoop

private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds,
  longTime => eventLoop.post(GenerateJobs(new Time(longTime))), "JobGenerator")

2. eventLoop：onReceive接收到消息。

/** Start generation of jobs */
def start(): Unit = synchronized {
  if (eventLoop != null) return // generator has already been started

// Call checkpointWriter here to initialize it before eventLoop uses it to avoid a deadlock.
// See SPARK-10125
checkpointWriter

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)
}
}

3. processEvent:中就会接收到ClearMetadata和ClearCheckpointData。

/** 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)
  }
}

4. clearMetadata：清楚元数据信息。

/** 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)
}
}

5. DStreamGraph:首先会清理outputDStream，其实就是forEachDStream

def clearMetadata(time: Time) {
  logDebug("Clearing metadata for time " + time)
  this.synchronized {
    outputStreams.foreach(_.clearMetadata(time))
  }
  logDebug("Cleared old metadata for time " + time)
}

6. DStream.clearMetadata:除了清除RDD，也可以清除metadata元数据。如果想RDD跨Batch Duration的话可以设置rememberDuration时间. rememberD

/**
 * 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)
// rememberDuration记忆周期 查看下RDD是否是oldRDD
  val oldRDDs = generatedRDDs.filter(_._1 <= (time - rememberDuration))
  logDebug("Clearing references to old RDDs: [" +
    oldRDDs.map(x => s"${x._1} -> ${x._2.id}").mkString(", ") + "]")
//从generatedRDDs中将key清理掉。
  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() //清理掉RDD的数据
        case _ =>
      }
    }
  }
  logDebug("Cleared " + oldRDDs.size + " RDDs that were older than " +
    (time - rememberDuration) + ": " + oldRDDs.keys.mkString(", "))
//依赖的DStream也需要清理掉。
  dependencies.foreach(_.clearMetadata(time))
}

7. 在BlockRDD中,BlockManagerMaster根据blockId将Block删除。删除Block的操作是不可逆的。

/**
 * 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

回到上面JobGenerator中的processEvent

1. clearCheckpoint：清除缓存数据。

/** 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)
}

2. clearCheckpointData:

def clearCheckpointData(time: Time) {
  logInfo("Clearing checkpoint data for time " + time)
  this.synchronized {
    outputStreams.foreach(_.clearCheckpointData(time))
  }
  logInfo("Cleared checkpoint data for time " + time)
}

3. ClearCheckpointData: 和清除元数据信息一样，还是清除DStream依赖的缓存数据。

private[streaming] def clearCheckpointData(time: Time) {
  logDebug("Clearing checkpoint data")
  checkpointData.cleanup(time)
  dependencies.foreach(_.clearCheckpointData(time))
  logDebug("Cleared checkpoint data")
}

4. DStreamCheckpointData:清除缓存的数据

/**
 * Cleanup old checkpoint data. This gets called after a checkpoint of `time` has been
 * written to the checkpoint directory.
 */
def cleanup(time: Time) {
  // Get the time of the oldest checkpointed RDD that was written as part of the
  // checkpoint of `time`
  timeToOldestCheckpointFileTime.remove(time) match {
    case Some(lastCheckpointFileTime) =>
      // Find all the checkpointed RDDs (i.e. files) that are older than `lastCheckpointFileTime`
      // This is because checkpointed RDDs older than this are not going to be needed
      // even after master fails, as the checkpoint data of `time` does not refer to those files
      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)
            }
            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")
  }
}

至此我们也知道了清理的过程，全流程如下：

但是清理是什么时候被触发的？

1. 在最终提交Job的时候，是交给JobHandler去执行的。

private class JobHandler(job: Job) extends Runnable with Logging {
    import JobScheduler._

def run() {
try {
val formattedTime = UIUtils.formatBatchTime(
job.time.milliseconds, ssc.graph.batchDuration.milliseconds, showYYYYMMSS = false)
val batchUrl = s"/streaming/batch/?id=${job.time.milliseconds}"
val batchLinkText = s"[output operation ${job.outputOpId}, batch time ${formattedTime}]"

ssc.sc.setJobDescription(
s"""Streaming job from <a href="$batchUrl">$batchLinkText</a>""")
ssc.sc.setLocalProperty(BATCH_TIME_PROPERTY_KEY, job.time.milliseconds.toString)
ssc.sc.setLocalProperty(OUTPUT_OP_ID_PROPERTY_KEY, job.outputOpId.toString)

// We need to assign `eventLoop` to a temp variable. Otherwise, because
// `JobScheduler.stop(false)` may set `eventLoop` to null when this method is running, then
// it's possible that when `post` is called, `eventLoop` happens to null.
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) {
//当Job完成的时候，eventLoop会发消息初始化onReceive
_eventLoop.post(JobCompleted(job, clock.getTimeMillis()))
}
} else {
// JobScheduler has been stopped.
}
} finally {
ssc.sc.setLocalProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY, null)
ssc.sc.setLocalProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY, null)
}
}
}
}

2. OnReceive初始化接收到消息JobCompleted.

def start(): Unit = synchronized {
  if (eventLoop != null) return // scheduler has already been started

logDebug("Starting JobScheduler")
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()

3. 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)
  }
}

4. 调用JobGenerator的onBatchCompletion方法清楚元数据。

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 _ =>
  }
}

触发流程如下：

2015 次浏览