Spark Streaming应用程序有以下特点：
1. 不断持续接收数据
2. Receiver和Driver不在同一节点中
Spark Streaming应用程序接收数据、存储数据、汇报数据的metedata给Driver。数据接收的模式类似于MVC，其中Driver是Model，Receiver是View，ReceiverSupervisorImpl是Controller。Receiver的启动由ReceiverSupervisorImpl来控制，Receiver接收到数据交给ReceiverSupervisorImpl来存储。RDD中的元素必须要实现序列化，才能将RDD序列化给Executor端。Receiver就实现了Serializable接口。
ReceiverTracker的代码片段：
// Create the RDD using the scheduledLocations to run the receiver in a Spark job
val receiverRDD: RDD[Receiver[_]] =
if (scheduledLocations.isEmpty) {
ssc.sc.makeRDD(Seq(receiver), 1)
} else {
val preferredLocations = scheduledLocations.map(_.toString).distinct
ssc.sc.makeRDD(Seq(receiver -> preferredLocations))
}
Receiver的代码片段：
@DeveloperApi
abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable {
处理Receiver接收到的数据，存储数据并汇报给Driver，Receiver是一条一条的接收数据的。
/**
* Concrete implementation of [[org.apache.spark.streaming.receiver.ReceiverSupervisor]]
* which provides all the necessary functionality for handling the data received by
* the receiver. Specifically, it creates a [[org.apache.spark.streaming.receiver.BlockGenerator]]
* object that is used to divide the received data stream into blocks of data.
*/
private[streaming] class ReceiverSupervisorImpl(
receiver: Receiver[_],
env: SparkEnv,
hadoopConf: Configuration,
checkpointDirOption: Option[String]
) extends ReceiverSupervisor(receiver, env.conf) with Logging {
通过限定数据存储速度来实现限流接收数据，合并成buffer，放入block队列在ReceiverSupervisorImpl启动会调用BlockGenerator对象的start方法。
override protected def onStart() {
registeredBlockGenerators.foreach { _.start() }
...
private val registeredBlockGenerators = new mutable.ArrayBuffer[BlockGenerator]
with mutable.SynchronizedBuffer[BlockGenerator]
源码注释说明了BlockGenerator把一个Receiver接收到的数据合并到一个Block然后写入到BlockManager中。该类内部有两个线程，一个是周期性把数据生成一批对象，然后把先前的一批数据封装成Block。另一个线程时把Block写入到BlockManager中。
private val defaultBlockGenerator = createBlockGenerator(defaultBlockGeneratorListener)
BlockGenerator类继承自RateLimiter类，说明我们不能限定接收数据的速度，但是可以限定存储数据的速度，转过来就限定流动的速度。
BlockGenerator类有一个定时器（默认每200ms将接收到的数据合并成block）和一个线程(把block写入到BlockManager)，200ms会产生一个Block，即1秒钟生成5个Partition。太小则生成的数据片中数据太小，导致一个Task处理的数据少，性能差。实际经验得到不要低于50ms。
BlockGenerator代码片段：
private val blockIntervalTimer =
new RecurringTimer(clock, blockIntervalMs, updateCurrentBuffer, "BlockGenerator")
...
private val blockPushingThread = new Thread() { override def run() { keepPushingBlocks() } }
那BlockGenerator是怎么被创建的？
private val defaultBlockGenerator = createBlockGenerator(defaultBlockGeneratorListener)
...
override def createBlockGenerator(
blockGeneratorListener: BlockGeneratorListener): BlockGenerator = {
// Cleanup BlockGenerators that have already been stopped
registeredBlockGenerators --= registeredBlockGenerators.filter{ _.isStopped() }
val newBlockGenerator = new BlockGenerator(blockGeneratorListener, streamId, env.conf)
registeredBlockGenerators += newBlockGenerator
newBlockGenerator
}
BlockGenerator类中的定时器会回调updateCurrentBuffer方法。
Receiver不断的接收数据，BlockGenerator类通过一个定时器，把Receiver接收到的数据，把多条合并成Block，再放入到Block队列中。
/** Change the buffer to which single records are added to. */
private def updateCurrentBuffer(time: Long): Unit = {
try {
var newBlock: Block = null
// 不同线程都会访问currentBuffer，故需加锁
synchronized {
// 如果缓冲器不为空，则生成StreamBlockId对象，
// 调用listener的onGenerateBlock来通知Block已生成，
// 再实例化block对象。
if (currentBuffer.nonEmpty) {
val newBlockBuffer = currentBuffer
currentBuffer = new ArrayBuffer[Any]
val blockId = StreamBlockId(receiverId, time - blockIntervalMs)
listener.onGenerateBlock(blockId)
newBlock = new Block(blockId, newBlockBuffer)
}
}
// 最后，把Block对象放入
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)
}
}
该函数200ms回调一次，可以设置，但不能小于50ms。
运行在Executor端的ReceiverSupervisorImpl需要与Driver端的ReceiverTracker进行通信，传递元数据信息metedata，其中ReceiverSupervisorImpl通过RPC的名称获取到ReceiverTrcker的远程调用。
ReceiverSupervisorImpl代码片段：
/** Remote RpcEndpointRef for the ReceiverTracker */
private val trackerEndpoint = RpcUtils.makeDriverRef("ReceiverTracker", env.conf, env.rpcEnv)
在ReceiverTracker调用start方法启动的时候，会以ReceiverTracker的名称创建RPC通信体。ReceiverSupervisorImpl就是和这个RPC通信体进行消息交互的。
/** Start the endpoint and receiver execution thread. */
def start(): Unit = synchronized {
if (isTrackerStarted) {
throw new SparkException("ReceiverTracker already started")
}
if (!receiverInputStreams.isEmpty) {
endpoint = ssc.env.rpcEnv.setupEndpoint(
"ReceiverTracker", newReceiverTrackerEndpoint(ssc.env.rpcEnv))
if (!skipReceiverLaunch) launchReceivers()
logInfo("ReceiverTracker started")
trackerState = Started
}
}
在ReceiverTrackerEndpoint接收到ReceiverSupervisorImpl发送的注册消息，把其RpcEndpoint保存起来。
override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
// Remote messages
caseRegisterReceiver(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)) {
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 StopAllReceivers =>
assert(isTrackerStopping || isTrackerStopped)
stopReceivers()
context.reply(true)
}
对应的Executor端的ReceiverSupervisorImpl也会创建Rpc消息通信体，来接收来自Driver端ReceiverTacker的消息。
/** RpcEndpointRef for receiving messages from the ReceiverTracker in the driver */
private val endpoint = env.rpcEnv.setupEndpoint(
"Receiver-" + streamId + "-" + System.currentTimeMillis(), new ThreadSafeRpcEndpoint {
override val rpcEnv: RpcEnv = env.rpcEnv
override def receive: PartialFunction[Any, Unit] = {
case StopReceiver =>
logInfo("Received stop signal")
ReceiverSupervisorImpl.this.stop("Stopped by driver", None)
case CleanupOldBlocks(threshTime) =>
logDebug("Received delete old batch signal")
cleanupOldBlocks(threshTime)
case UpdateRateLimit(eps) =>
logInfo(s"Received a new rate limit: $eps.")
registeredBlockGenerators.foreach { bg =>
bg.updateRate(eps)
}
}
})
BlockGenerator类中的线程每隔10ms从队列中获取Block，写入到BlockManager中。
/** 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)
}
}
ReceiverSupervisorImpl代码片段：
/** Divides received data records into data blocks for pushing in BlockManager. */
private val defaultBlockGeneratorListener = new BlockGeneratorListener {
def onAddData(data: Any, metadata: Any): Unit = { }
def onGenerateBlock(blockId: StreamBlockId): Unit = { }
def onError(message: String, throwable: Throwable) {
reportError(message, throwable)
}
defonPushBlock(blockId: StreamBlockId, arrayBuffer: ArrayBuffer[_]) {
pushArrayBuffer(arrayBuffer, None, Some(blockId))
}
}
...
/** Store block and report it to driver */
defpushAndReportBlock(
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")
}
将数据存储在BlockManager中，并将源数据信息告诉Driver端的ReceiverTracker。
defstoreBlock(blockId: StreamBlockId, block: ReceivedBlock): ReceivedBlockStoreResult = {
var numRecords = None: Option[Long]
val putResult: Seq[(BlockId, BlockStatus)] = block match {
case ArrayBufferBlock(arrayBuffer) =>
numRecords = Some(arrayBuffer.size.toLong)
blockManager.putIterator(blockId, arrayBuffer.iterator, storageLevel,
tellMaster = true)
case IteratorBlock(iterator) =>
val countIterator = new CountingIterator(iterator)
// 把数据写入BlockManager
val putResult =blockManager.putIterator(blockId, countIterator, storageLevel,
tellMaster = true)
numRecords = countIterator.count
putResult
case ByteBufferBlock(byteBuffer) =>
blockManager.putBytes(blockId, byteBuffer, storageLevel, tellMaster = true)
case o =>
throw new SparkException(
s"Could not store $blockId to block manager, unexpected block type ${o.getClass.getName}")
}
if (!putResult.map { _._1 }.contains(blockId)) {
throw new SparkException(
s"Could not store $blockId to block manager with storage level $storageLevel")
}
BlockManagerBasedStoreResult(blockId, numRecords)
}
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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