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Spark Streaming概述
Spark Streaming 初始化过程
Spark Streaming Receiver启动过程分析
Spark Streaming 数据准备阶段分析（Receiver方式）
Spark Streaming 数据计算阶段分析
SparkStreaming Backpressure分析
Spark Streaming Executor DynamicAllocation 机制分析
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1、Spark Streaming数据准备流程
SparkStreaming的全过程分为两个阶段：数据准备阶段和数据计算阶段。两个阶段在功能上相互独立，仅通过数据联系在一起。本文重点从源码角度分析Spark Streaming数据准备阶段的具体流程。
Spark Streaming数据准备阶段包含对流入数据的接收、分片（按照时间片划分为数据集）以及分片数据的分发工作。其转数据的接收转化过程主要有以下几个关键步骤：
- Receiver接收外部数据流，其将接收的数据流交由BlockGenerator存储在ArrayBuffer中，在存储之前会先获取许可（由“spark.streaming.receiver.maxRate”指定，spark 1.5之后由backpressure进行自动计算，代表可以存取的最大速率，每存储一条数据获取一个许可，若未获取到许可接收将阻塞）。
- BlockGenerater中定义一Timer,其依据设置的Interval定时将ArrayBuffer中的数据取出，包装成Block,并将Block存放入blocksForPushing中（阻塞队列ArrayBlockingQueue），并将ArrayBuffer清空
- BlockGenerater中的blockPushingThread线程从阻塞队列中取出取出block信息，并以onPushBlock的方式将消息通过监听器（listener）发送给ReceiverSupervisor.
- ReceiverSupervisor收到消息后，将对消息中携带数据进行处理，其会通过调用BlockManager对数据进行存储，并将存储结果信息向ReceiverTracker汇报
- ReceiverTracker收到消息后，将信息存储在未分配Block队列（streamidToUnallocatedBlock）中，等待JobGenerator生成Job时将其指定给RDD
1过程持续进行，2-5 以BlockInterval为周期重复执行.
2、源码分析
以WordCount应用为例，程序见Spark Streaming概述
2.1 数据接收
在Receiver启动之后，其将开始接收外部数据源的数据（WordCount程序中使用的SocketReceiver是以主动接收的方式获取数据），并对数据进行存储。SocketReceiver实现代码如下：
def receive() {
try {
val iterator = bytesToObjects(socket.getInputStream())
while(!isStopped && iterator.hasNext) {
store(iterator.next())
}
......
}
/**
* Store a single item of received data to Spark's memory.
* These single items will be aggregated together into data blocks before
* being pushed into Spark's memory.
*/
def store(dataItem: T) {
supervisor.pushSingle(dataItem)
}
其中 supervisor的pushSingle（）实现如下：
/** Push a single record of received data into block generator. */
def pushSingle(data: Any) {
defaultBlockGenerator.addData(data)
}
其调用defaultBlockGenerator的addData将数据添加进currentBuffer，其中defaultBlockGenerator 即为BlockGenerator,其addData方法如下：
/**
* Push a single data item into the buffer.
*/
def addData(data: Any): Unit = {
if (state == Active) {
waitToPush()
synchronized {
if (state == Active) {
currentBuffer += data
} else {
throw new SparkException(
"Cannot add data as BlockGenerator has not been started or has been stopped")
}
}
} else {
throw new SparkException(
"Cannot add data as BlockGenerator has not been started or has been stopped")
}
}
分析上述代码，其中waitToPush（）方法，是用来控制接收速率的，与BackPressure机制相关,"SparkStreaming Backpressure分析"一章会进行详细分析。当获取到许可之后，数据将会存入currentBuffer中，并等待进行后续处理。
Receiver会不断重复上述过程，接收数据，存入currentBuffer.
2.2 数据切片
"Spark Streaming Receiver启动过程分析"提到，在启动Receiver进会创建ReceiverSupervisorImpl， ReceiverSupervisorImpl又会创建并启动BlockGenerator，用于对Receiver接收的数据流进行切片操作。其切片是以定时器的方式进行的。其时间周期由“spark.streaming.blockInterval”进行设置，默认为200ms.
BlockGenerator的start方法实现如下：
/** Start block generating and pushing threads. */
def start(): Unit = synchronized {
if (state == Initialized) {
state = Active
blockIntervalTimer.start()
blockPushingThread.start()
logInfo("Started BlockGenerator")
} else {
throw new SparkException(
s"Cannot start BlockGenerator as its not in the Initialized state [state = $state]")
}
}
其中
- blockIntervalTimer为定时器任务，其会周期性的执行计划任务
private val blockIntervalMs = conf.getTimeAsMs("spark.streaming.blockInterval", "200ms")
require(blockIntervalMs > 0, s"'spark.streaming.blockInterval' should be a positive value")
private val blockIntervalTimer =
new RecurringTimer(clock, blockIntervalMs, updateCurrentBuffer, "BlockGenerator")
- blockPushingThread为新线程，负载不断的从阻塞队列中取出打包的数据
private val blockPushingThread = new Thread() { override def run() { keepPushingBlocks() } }
2.2.1 数据流切分
RecurringTimer为定时器，其每隔blockIntervalMs时间，执行一次updateCurrentBuffer方法，将currentBuffer中的数据进行打包，并添加到阻塞队列blocksForPushing中。
/** Change the buffer to which single records are added to. */
private def updateCurrentBuffer(time: Long): Unit = {
try {
var newBlock: Block = null
synchronized {
if (currentBuffer.nonEmpty) { //如果buffer空，则不生成block.
val newBlockBuffer = currentBuffer
currentBuffer = new ArrayBuffer[Any]
val blockId = StreamBlockId(receiverId, time - blockIntervalMs)
listener.onGenerateBlock(blockId)
newBlock = new Block(blockId, newBlockBuffer)
}
}
if (newBlock != null) {
blocksForPushing.put(newBlock) // put is blocking when queue is full
}
} catch {
case ie: InterruptedException =>
logInfo("Block updating timer thread was interrupted")
case e: Exception =>
reportError("Error in block updating thread", e)
}
}
2.2.2 数据传输
blockPushingThread 线程启动会，将执行keepPushingBlocks（）方法，从阻塞队列中取出切片后的数据，并通过defaultBlockGeneratorListener转发,并等待下一步存储、分发操作。（defaultBlockGeneratorListener在ReceiverSupervisorImpl中定义）。
/** Keep pushing blocks to the BlockManager. */
private def keepPushingBlocks() {
logInfo("Started block pushing thread")
def areBlocksBeingGenerated: Boolean = synchronized {
state != StoppedGeneratingBlocks
}
try {
// While blocks are being generated, keep polling for to-be-pushed blocks and push them.
while (areBlocksBeingGenerated) {
Option(blocksForPushing.poll(10, TimeUnit.MILLISECONDS)) match {
case Some(block) => pushBlock(block)
case None =>
}
}
// At this point, state is StoppedGeneratingBlock. So drain the queue of to-be-pushed blocks.
logInfo("Pushing out the last " + blocksForPushing.size() + " blocks")
while (!blocksForPushing.isEmpty) {
val block = blocksForPushing.take()
logDebug(s"Pushing block $block")
pushBlock(block)
logInfo("Blocks left to push " + blocksForPushing.size())
}
logInfo("Stopped block pushing thread")
} catch {
case ie: InterruptedException =>
logInfo("Block pushing thread was interrupted")
case e: Exception =>
reportError("Error in block pushing thread", e)
}
}
其中pushBlock方法实现如下：
private def pushBlock(block: Block) {
listener.onPushBlock(block.id, block.buffer)
logInfo("Pushed block " + block.id)
}
2.3 Block 存储与汇报
BlockGeneratorListener 监控到onPushBlock事件后，会对传输的数据分片进行存储操作，并向ReceiverTracker汇报。
2.3.1 Block存储
BlockGeneratorListener 监控到onPushBlock事件后，经过一系列调整，最后将调用 pushAndReportBlock对数据分片进行存储，pushAndReportBlock的实现如下：
/** Store block and report it to driver */
def pushAndReportBlock(
receivedBlock: ReceivedBlock,
metadataOption: Option[Any],
blockIdOption: Option[StreamBlockId]
) {
val blockId = blockIdOption.getOrElse(nextBlockId)
val time = System.currentTimeMillis
val blockStoreResult = receivedBlockHandler.storeBlock(blockId, receivedBlock)
logDebug(s"Pushed block $blockId in ${(System.currentTimeMillis - time)} ms")
val numRecords = blockStoreResult.numRecords
val blockInfo = ReceivedBlockInfo(streamId, numRecords, metadataOption, blockStoreResult)
trackerEndpoint.askWithRetry[Boolean](AddBlock(blockInfo))
logDebug(s"Reported block $blockId")
}
其中，数据通过receivedBlockHandler存储为Block, ReceivedBlockHandler有两种实现
- WriteAheadLogBasedBlockHandler ， 开启WAL时会使用此实现
- BlockManagerBasedBlockHandler，默认情况下会使用此实现 。
BlockManagerBasedBlockHandler通过BlockManager的接口对数据在Receiver所在节点进行保存，并依据StorageLevel 设置的副本数，在其它Executor中保存副本。保存副本的方法如下所示：
/**
* Replicate block to another node. Note that this is a blocking call that returns after
* the block has been replicated.
*/
private def replicate(
blockId: BlockId,
data: ChunkedByteBuffer,
level: StorageLevel,
classTag: ClassTag[_]): Unit = {
......
var peersForReplication = blockReplicationPolicy.prioritize(
blockManagerId,
getPeers(false),
mutable.HashSet.empty,
blockId,
numPeersToReplicateTo)
......
}
其中副本策略采用，随机取样的方式进行,
/**
* Method to prioritize a bunch of candidate peers of a block. This is a basic implementation,
* that just makes sure we put blocks on different hosts, if possible
*
* @param blockManagerId Id of the current BlockManager for self identification
* @param peers A list of peers of a BlockManager
* @param peersReplicatedTo Set of peers already replicated to
* @param blockId BlockId of the block being replicated. This can be used as a source of
* randomness if needed.
* @return A prioritized list of peers. Lower the index of a peer, higher its priority
*/
override def prioritize(
blockManagerId: BlockManagerId,
peers: Seq[BlockManagerId],
peersReplicatedTo: mutable.HashSet[BlockManagerId],
blockId: BlockId,
numReplicas: Int): List[BlockManagerId] = {
val random = new Random(blockId.hashCode)
logDebug(s"Input peers : ${peers.mkString(", ")}")
val prioritizedPeers = if (peers.size > numReplicas) {
getSampleIds(peers.size, numReplicas, random).map(peers(_))
} else {
if (peers.size < numReplicas) {
logWarning(s"Expecting ${numReplicas} replicas with only ${peers.size} peer/s.")
}
random.shuffle(peers).toList
}
logDebug(s"Prioritized peers : ${prioritizedPeers.mkString(", ")}")
prioritizedPeers
}
2.3.2 Block 汇报
当Block保存完成，并且副本制作完成后，将通过trackerEndpoint向ReceiverTrack进行汇报。
trackerEndpoint.askWithRetry[Boolean](AddBlock(blockInfo))
ReceiverTrackEndpoint收到“AddBlock”信息后，将receivedBlockTracker将block信息保存入队列streamIdToUnallocatedBlockQueues中，以用于生成Job。
case AddBlock(receivedBlockInfo) =>
if (WriteAheadLogUtils.isBatchingEnabled(ssc.conf, isDriver = true)) {
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))
}
/** Add new blocks for the given stream */
private def addBlock(receivedBlockInfo: ReceivedBlockInfo): Boolean = {
receivedBlockTracker.addBlock(receivedBlockInfo)
}
其中receivedBlockTracker的addBlock实现如下：
/** Add received block. This event will get written to the write ahead log (if enabled). */
def addBlock(receivedBlockInfo: ReceivedBlockInfo): Boolean = {
try {
val writeResult = writeToLog(BlockAdditionEvent(receivedBlockInfo))
if (writeResult) {
synchronized {
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
}
}
/** Get the queue of received blocks belonging to a particular stream */
private def getReceivedBlockQueue(streamId: Int): ReceivedBlockQueue = {
streamIdToUnallocatedBlockQueues.getOrElseUpdate(streamId, new ReceivedBlockQueue)
}
至此数据准备阶段完成，保存在streamIdToUnallocatedBlockQueues中的数据信息，在下一个批次生成Job时会被取出用于封装成RDD，且注册数据信息会转移至timeToAllocatedBlocks中。