SparkStreaming流程梳理
根据SparkStreaming的最初设计文档（https://docs.google.com/document/d/1vTCB5qVfyxQPlHuv8rit9-zjdttlgaSrMgfCDQlCJIM/edit#)，初版的流程设计如下：
- Reciever将block分发至ReceivedBlockHandler；
- ReceivedBlockHandler将block保存在内存（无冗余）；
- Reciever将这个block传输至driver；
- Reciever标记该block为recieved；
- Driver基于block info信息创建HDFSBackedBlockRDDs；
- 基于BlockManagerMaster的block location信息进行调度；
- Checkpoint信息存储在HDFS；
而当前稳定版本(2.1.0)的实现中，在多出添加了WAL功能，变更如下：
- Reciever将block分发至ReceivedBlockHandler；
- ReceivedBlockHandler将block保存在内存(blockManager) + WAL中（无冗余）；
- Reciever将这个blockInfo传输通过trackerEndpoint 传输至driver；
- driver将该blockInfo写入WAL；
- Reciever标记该block为recieved；
- Driver基于block info信息创建HDFSBackedBlockRDDs（此处也有变更）；
- 基于BlockManagerMaster的block location信息进行调度；
- Checkpoint信息存储在HDFS；
生产阶段
ReceiverSupervisorImpl
ReceiverSupervisorImpl将搜集的内容pushAndReportBlock保存：
/** Store block and report it to driver */
def pushAndReportBlock(
receivedBlock: ReceivedBlock,
metadataOption: Option[Any],
blockIdOption: Option[StreamBlockId]
) {
// 构造blockId
val blockId = blockIdOption.getOrElse(nextBlockId)
val time = System.currentTimeMillis
// 调用下述receivedBlockHandler的storeBlock方法，将block保存至blockManager和wal
val blockStoreResult = receivedBlockHandler.storeBlock(blockId, receivedBlock)
logDebug(s"Pushed block $blockId in ${(System.currentTimeMillis - time)} ms")
val numRecords = blockStoreResult.numRecords
// 根据block信息构造blockInfo
val blockInfo = ReceivedBlockInfo(streamId, numRecords, metadataOption, blockStoreResult)
// 传输该blockInfo至driver测的trackerEndpoint
trackerEndpoint.askWithRetry[Boolean](AddBlock(blockInfo))
logDebug(s"Reported block $blockId")
}
receivedBlockHandler
receivedBlockHandler为reciever测的实现wal功能，其主要功能为：将接受到的block并行地保存在blockManger和HDFS中；
/**
* This implementation stores the block into the block manager as well as a write ahead log.
* It does this in parallel, using Scala Futures, and returns only after the block has
* been stored in both places.
*/
// 基于Scala Future特质，可以并行地将RecivedBlock存储到blockManager和HDFS
def storeBlock(blockId: StreamBlockId, block: ReceivedBlock): ReceivedBlockStoreResult = {
var numRecords = Option.empty[Long]
// Serialize the block so that it can be inserted into both
// 第一步、序列化block
val serializedBlock = block match {
case ArrayBufferBlock(arrayBuffer) =>
numRecords = Some(arrayBuffer.size.toLong)
serializerManager.dataSerialize(blockId, arrayBuffer.iterator)
case IteratorBlock(iterator) =>
val countIterator = new CountingIterator(iterator)
val serializedBlock = serializerManager.dataSerialize(blockId, countIterator)
numRecords = countIterator.count
serializedBlock
case ByteBufferBlock(byteBuffer) =>
new ChunkedByteBuffer(byteBuffer.duplicate())
case _ =>
throw new Exception(s"Could not push $blockId to block manager, unexpected block type")
}
// Store the block in block manager
// 保存在blockManager的future
val storeInBlockManagerFuture = Future {
val putSucceeded = blockManager.putBytes(
blockId,
serializedBlock,
effectiveStorageLevel,
tellMaster = true)
if (!putSucceeded) {
throw new SparkException(
s"Could not store $blockId to block manager with storage level $storageLevel")
}
}
// Store the block in write ahead log
// 保存到wal的future
val storeInWriteAheadLogFuture = Future {
// 当该函数该write函数完毕，保障该block一定成功地写入hdfs
writeAheadLog.write(serializedBlock.toByteBuffer, clock.getTimeMillis())
}
// Combine the futures, wait for both to complete, and return the write ahead log record handle
// 参考https://github.com/apache/spark/pull/3721， 该方案使用zip，可以并行地完成上述两者的执行
val combinedFuture = storeInBlockManagerFuture.zip(storeInWriteAheadLogFuture).map(_._2)
val walRecordHandle = ThreadUtils.awaitResult(combinedFuture, blockStoreTimeout)
WriteAheadLogBasedStoreResult(blockId, numRecords, walRecordHandle)
}
关于trackerEndpoint
Reciver同Driver之间通过trackerEndpoint通信，其处理上述的AddBlock信息是在ReciverTracker类中实现，其具体实现如下：
override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
// Remote messages
case RegisterReceiver(streamId, typ, host, executorId, receiverEndpoint) =>
val successful =
registerReceiver(streamId, typ, host, executorId, receiverEndpoint, context.senderAddress)
context.reply(successful)
case AddBlock(receivedBlockInfo) =>
if (WriteAheadLogUtils.isBatchingEnabled(ssc.conf, isDriver = true)) {
// 调用receivedBlockTracker.addBlock实现，具体如下
walBatchingThreadPool.execute(new Runnable {
override def run(): Unit = Utils.tryLogNonFatalError {
if (active) {
context.reply(addBlock(receivedBlockInfo))
} else {
throw new IllegalStateException("ReceiverTracker RpcEndpoint shut down.")
}
}
})
} else {
context.reply(addBlock(receivedBlockInfo))
}
case DeregisterReceiver(streamId, message, error) =>
deregisterReceiver(streamId, message, error)
context.reply(true)
// Local messages
case AllReceiverIds =>
context.reply(receiverTrackingInfos.filter(_._2.state != ReceiverState.INACTIVE).keys.toSeq)
case GetAllReceiverInfo =>
context.reply(receiverTrackingInfos.toMap)
case StopAllReceivers =>
assert(isTrackerStopping || isTrackerStopped)
stopReceivers()
context.reply(true)
}
/** Add new blocks for the given stream */
private def addBlock(receivedBlockInfo: ReceivedBlockInfo): Boolean = {
receivedBlockTracker.addBlock(receivedBlockInfo)
}
/** Add received block. This event will get written to the write ahead log (if enabled). */
// Driver测处理AddBlock事件
def addBlock(receivedBlockInfo: ReceivedBlockInfo): Boolean = {
try {
// 保存blockInfo信息，writeToLog会判定是否开启wal，
// 此处要注意： blockInfo信息和在reciever测的block不一样，一个你可以理解为block的meta信息，一个则为真实的数据
val writeResult = writeToLog(BlockAdditionEvent(receivedBlockInfo))
if (writeResult) {
synchronized {
// 同时将该blockInfo写入blockQueue，供调度使用
getReceivedBlockQueue(receivedBlockInfo.streamId) += receivedBlockInfo
}
logDebug(s"Stream ${receivedBlockInfo.streamId} received " +
s"block ${receivedBlockInfo.blockStoreResult.blockId}")
} else {
logDebug(s"Failed to acknowledge stream ${receivedBlockInfo.streamId} receiving " +
s"block ${receivedBlockInfo.blockStoreResult.blockId} in the Write Ahead Log.")
}
writeResult
} catch {
case NonFatal(e) =>
logError(s"Error adding block $receivedBlockInfo", e)
false
}
}
消费阶段
上述过程为通过reciever进行数据收集的阶段，而产生的block则是通过spark调度任务进行消费的，其消费处理逻辑如下，首先经过JobGenerator每个batchTime生成相应的DStream，然后提交任务，进行处理。
/** Processes all events */
// JobGenerator启动时，会启动一个定时的timer，根据配置的batchDuration，定时地post GenerateJobs事件，触发生成DStream的逻辑
private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds,
longTime => eventLoop.post(GenerateJobs(new Time(longTime))), "JobGenerator")
private def processEvent(event: JobGeneratorEvent) {
logDebug("Got event " + event)
event match {
// eventLoop收到GenerateJobs事件
case GenerateJobs(time) => generateJobs(time)
case ClearMetadata(time) => clearMetadata(time)
case DoCheckpoint(time, clearCheckpointDataLater) =>
doCheckpoint(time, clearCheckpointDataLater)
case ClearCheckpointData(time) => clearCheckpointData(time)
}
}
//可以看做SparkStreaming的核心调度
/** Generate jobs and perform checkpointing for the given `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 {
// 第一步、分配上述“接受到的block”到该时间点对应的batch；具体实现如下。
jobScheduler.receiverTracker.allocateBlocksToBatch(time) // allocate received blocks to batch
// 第二步，等上述分配好allocatedBlocks，调用generateJobs生成Spark定义的Job类（带time参数）
graph.generateJobs(time) // generate jobs using allocated block
} match {
case Success(jobs) =>
// 第三步、 根据time从inputInfoTracker获取这次time的metaData(这一步没弄明白，为什么不从上述分配好的time->allocatedBlocks开始任务，而要加一个inputInfoTracker)，并真正地提交任务，开始计算
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)
}
// 第四步、checkpoint该time至hdfs
eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = false))
}
/** Allocate all unallocated blocks to the given batch. */
// receiverTracker.allocateBlocksToBatch()会调用receivedBlockTracker类
def allocateBlocksToBatch(batchTime: Time): Unit = {
if (receiverInputStreams.nonEmpty) {
receivedBlockTracker.allocateBlocksToBatch(batchTime)
}
}
/**
* Allocate all unallocated blocks to the given batch.
* This event will get written to the write ahead log (if enabled).
*/
def allocateBlocksToBatch(batchTime: Time): Unit = synchronized {
if (lastAllocatedBatchTime == null || batchTime > lastAllocatedBatchTime) {
// streamId为Reciever启动时定义的streamId，调用getReceivedBlockQueue().dequeueAll()，将收集到的blockInfo返回，和streamId构成(streamId, blockInfos)的二元组
val streamIdToBlocks = streamIds.map { streamId =>
(streamId, getReceivedBlockQueue(streamId).dequeueAll(x => true))
}.toMap
// 构造成AllocateBlocks对象，方便数据传输
val allocatedBlocks = AllocatedBlocks(streamIdToBlocks)
// 在真正的任务开始前，将开始处理做的allocatedBlocks写入wal
if (writeToLog(BatchAllocationEvent(batchTime, allocatedBlocks))) {
// 如果写入成果，则开始分配任务，在time->allocatedBlocks添加该相应对，等待generateJob()使用
timeToAllocatedBlocks.put(batchTime, allocatedBlocks)
lastAllocatedBatchTime = batchTime
} else {
// 如果写入wal失败，则需要重试
logInfo(s"Possibly processed batch $batchTime needs to be processed again in WAL recovery")
}
} else {
// This situation occurs when:
// 1. WAL is ended with BatchAllocationEvent, but without BatchCleanupEvent,
// possibly processed batch job or half-processed batch job need to be processed again,
// so the batchTime will be equal to lastAllocatedBatchTime.
// 2. Slow checkpointing makes recovered batch time older than WAL recovered
// lastAllocatedBatchTime.
// This situation will only occurs in recovery time.
logInfo(s"Possibly processed batch $batchTime needs to be processed again in WAL recovery")
}
}
GenerateJob如何生成RDD?
从Spark Streaming的定义来讲，大家都熟悉Spark Streaming是一个批处理，将流转换成离散的DStream。
但这个过程却十分复杂，具体可以参考这个链接：https://github.com/lw-lin/CoolplaySpark/blob/master/Spark%20Streaming%20源码解析系列/1.2%20DStream%20生成%20RDD%20实例详解.md
后续
SparkStreaming的容错机制有点绕，名字都叫wal，其实含义有些不同，后面会有一篇文章介绍其wal容错机制，可以参考https://databricks.com/blog/2015/01/15/improved-driver-fault-tolerance-and-zero-data-loss-in-spark-streaming.html这篇文章，讲解的挺详细的；
参考：
- ReceivedBlockTracker WAL实现： https://issues.apache.org/jira/browse/SPARK-7139
- Databrick 关于一致性的文章: https://databricks.com/blog/2015/01/15/improved-driver-fault-tolerance-and-zero-data-loss-in-spark-streaming.html
- Spark Streaming WAL实现：https://issues.apache.org/jira/browse/SPARK-3129
- Spark Streaming HA 设计： https://docs.google.com/document/d/1vTCB5qVfyxQPlHuv8rit9-zjdttlgaSrMgfCDQlCJIM/edit#
- https://github.com/apache/spark/pull/3721
- https://github.com/lw-lin/CoolplaySpark/blob/master/Spark%20Streaming%20源码解析系列/1.2%20DStream%20生成%20RDD%20实例详解.md
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