一、数据接收原理
二、源码分析
入口包org.apache.spark.streaming.receiver下ReceiverSupervisorImpl类的onStart()方法
###
override protected def onStart() {
// 这里的blockGenerator很重要，和数据接收有关，其运行在worker的executor端负责数据接收后的一些存取工作，以及配合ReceiverTracker
// 在Executor上，启动Receiver之前，就会先启动这个Receiver相关的一个blockGenerator，该组件，在数据接收中，极其重要
blockGenerator.start()
}
ReceiverSupervisorImpl类的onStart()方法，调用了blockGenerator.start()方法，跟进去看看
###org.apache.spark.streaming.receiver/BlockGenerator.scala
def start() {
// BlockGenerator.start()方法，其实就是启动内部两个关键的后台线程，
// 一个是blockIntervalTimer，负责将currentBuffer中的原始数据，打包成一个个的block
// 另一个是blockPushingThread，负责将blocksForPushing中的block，调用pushArrayBuffer()方法
blockIntervalTimer.start()
blockPushingThread.start()
logInfo("Started BlockGenerator")
}
blockGenerator.start()方法，调用了blockIntervalTimer.start()和blockPushingThread.start()方法
先看看有关变量的定义
###org.apache.spark.streaming.receiver/BlockGenerator.scala
private val blockInterval = conf.getLong("spark.streaming.blockInterval", 200)
// blockInterval,是有一个默认值的，spark.streaming.blockInterval，默认是200ms，每隔200ms，就会调用updateCurrentBuffer函数
private val blockIntervalTimer =
new RecurringTimer(clock, blockInterval, updateCurrentBuffer, "BlockGenerator")
// blocksForPushing队列的长度，可以调节的，spark.streaming.blockQueueSize，默认10个，可大可小
private val blockQueueSize = conf.getInt("spark.streaming.blockQueueSize", 10)
// blocksForPushing队列，
private val blocksForPushing = new ArrayBlockingQueue[Block](blockQueueSize)
// blockPushingThread，后台线程，启动之后，就会调用keepPushingBlocks()方法，这个方法中，就会每隔一段时间，去blocksForPushing队列中取block
private val blockPushingThread = new Thread() { override def run() { keepPushingBlocks() } }
// 这个currentBuffer，就是用于存放原始的数据
@volatile private var currentBuffer = new ArrayBuffer[Any]
blockIntervalTimer.start()就是一个线程，这个方法就不看了
重点看下blockPushingThread.start()方法，这个线程开始运行，会调用keepPushingBlocks()方法，代码如下
###org.apache.spark.streaming.receiver/BlockGenerator.scala
private val blockPushingThread = new Thread() { override def run() { keepPushingBlocks() } }
看keepPushingBlocks()方法
###org.apache.spark.streaming.receiver/BlockGenerator.scala
private def keepPushingBlocks() {
logInfo("Started block pushing thread")
try {
while(!stopped) {
// 从blocksForPushing这个队列中，poll出来当前队列队首的block，对于阻塞队列，默认设置100ms的超时
Option(blocksForPushing.poll(100, TimeUnit.MILLISECONDS)) match {
// 如果拿到了block，调用pushBlock去推送block
case Some(block) => pushBlock(block)
case None =>
}
}
// Push out the blocks that are still left
logInfo("Pushing out the last " + blocksForPushing.size() + " blocks")
while (!blocksForPushing.isEmpty) {
logDebug("Getting block ")
val block = blocksForPushing.take()
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)
}
}
可以看到keepPushingBlocks()方法，如果拿到了block，调用pushBlock()方法
看看pushBlock()方法
###org.apache.spark.streaming.receiver/BlockGenerator.scala
private def pushBlock(block: Block) {
listener.onPushBlock(block.id, block.buffer)
logInfo("Pushed block " + block.id)
}
pushBlock()方法会调用listener.onPushBlock()方法，这个listener是BlockGeneratorListener，onPushBlock()在ReceiverSupervisorImpl类中,
看ReceiverSupervisorImpl类的onPushBlock()方法:
###org.apache.spark.streaming.receiver/ReceiverSupervisorImpl.scala
// onPushBlock就会去调用pushArrayBuffer去推送block
def onPushBlock(blockId: StreamBlockId, arrayBuffer: ArrayBuffer[_]) {
pushArrayBuffer(arrayBuffer, None, Some(blockId))
}
onPushBlock就会去调用pushArrayBuffer()方法
看pushArrayBuffer()方法:
###org.apache.spark.streaming.receiver/ReceiverSupervisorImpl.scala def pushArrayBuffer(
arrayBuffer: ArrayBuffer[_],
metadataOption: Option[Any],
blockIdOption: Option[StreamBlockId]
) {
pushAndReportBlock(ArrayBufferBlock(arrayBuffer), metadataOption, blockIdOption)
}
接着看pushAndReportBlock()方法:
###org.apache.spark.streaming.receiver/ReceiverSupervisorImpl.scala def pushAndReportBlock(
receivedBlock: ReceivedBlock,
metadataOption: Option[Any],
blockIdOption: Option[StreamBlockId]
) {
val blockId = blockIdOption.getOrElse(nextBlockId)
val numRecords = receivedBlock match {
case ArrayBufferBlock(arrayBuffer) => arrayBuffer.size
case _ => -1
} val time = System.currentTimeMillis
// 还用receivedBlockHandler，去调用storeBlock方法，存储block到BlockManager中，这里，也可以看出预写日志的机制
val blockStoreResult = receivedBlockHandler.storeBlock(blockId, receivedBlock)
logDebug(s"Pushed block $blockId in ${(System.currentTimeMillis - time)} ms") // 封装一个ReceiverBlockInfo对象，里面有一个streamId
val blockInfo = ReceivedBlockInfo(streamId, numRecords, blockStoreResult)
// 调用了ReceiverTracker的Acrot的ask方法，发送AddBlock消息
val future = trackerActor.ask(AddBlock(blockInfo))(askTimeout)
Await.result(future, askTimeout)
logDebug(s"Reported block $blockId")
}
这里主要看receivedBlockHandler.storeBlock()方法和trackerActor.ask(AddBlock(blockInfo))(askTimeout)
首先看receivedBlockHandler.storeBlock()，看看receivedBlockHandler是什么
###org.apache.spark.streaming.receiver/ReceiverSupervisorImpl.scala
private val receivedBlockHandler: ReceivedBlockHandler = {
// 如果开启了预写日志机制，spark.streaming.receiver.writeAheadLog.enable，默认false
// 如果为true，那么receivedBlockHandler就是WriteAheadLogBasedBlockHandler
// 如果没有开启预写日志机制，那么receivedBlockHandler就是BlockManagerBasedBlockHandler
if (env.conf.getBoolean("spark.streaming.receiver.writeAheadLog.enable", false)) {
if (checkpointDirOption.isEmpty) {
throw new SparkException(
"Cannot enable receiver write-ahead log without checkpoint directory set. " +
"Please use streamingContext.checkpoint() to set the checkpoint directory. " +
"See documentation for more details.")
}
new WriteAheadLogBasedBlockHandler(env.blockManager, receiver.streamId,
receiver.storageLevel, env.conf, hadoopConf, checkpointDirOption.get)
} else {
new BlockManagerBasedBlockHandler(env.blockManager, receiver.storageLevel)
}
接着分别看BlockManagerBasedBlockHandler和WriteAheadLogBasedBlockHandler的storeBlock()方法
先看WriteAheadLogBasedBlockHandler
###org.apache.spark.streaming.receiver/ReceivedBlockHandler.scala
def storeBlock(blockId: StreamBlockId, block: ReceivedBlock): ReceivedBlockStoreResult = {
// Serialize the block so that it can be inserted into both
// 先用BlockManager序列化数据
val serializedBlock = block match {
case ArrayBufferBlock(arrayBuffer) =>
blockManager.dataSerialize(blockId, arrayBuffer.iterator)
case IteratorBlock(iterator) =>
blockManager.dataSerialize(blockId, iterator)
case ByteBufferBlock(byteBuffer) =>
byteBuffer
case _ =>
throw new Exception(s"Could not push $blockId to block manager, unexpected block type")
}
// Store the block in block manager
// 将数据保存到BlockManager中去，默认的持久化策略，StorageLevel，是带_SER,_2的，会序列化，复制一份副本到其他Executor的BlockManager，以供容错
val storeInBlockManagerFuture = Future {
val putResult =
blockManager.putBytes(blockId, serializedBlock, effectiveStorageLevel, tellMaster = true)
if (!putResult.map { _._1 }.contains(blockId)) {
throw new SparkException(
s"Could not store $blockId to block manager with storage level $storageLevel")
}
}
// Store the block in write ahead log
// 将block存入预写日志，通过logManager的writeToLog()方法
val storeInWriteAheadLogFuture = Future {
logManager.writeToLog(serializedBlock)
}
// Combine the futures, wait for both to complete, and return the write ahead log segment
val combinedFuture = storeInBlockManagerFuture.zip(storeInWriteAheadLogFuture).map(_._2)
val segment = Await.result(combinedFuture, blockStoreTimeout)
WriteAheadLogBasedStoreResult(blockId, segment)
}
再看BlockManagerBasedBlockHandler
###org.apache.spark.streaming.receiver/ReceivedBlockHandler.scala // 直接将数据保存到BlockManager中，就可以了
def storeBlock(blockId: StreamBlockId, block: ReceivedBlock): ReceivedBlockStoreResult = {
val putResult: Seq[(BlockId, BlockStatus)] = block match {
case ArrayBufferBlock(arrayBuffer) =>
blockManager.putIterator(blockId, arrayBuffer.iterator, storageLevel, tellMaster = true)
case IteratorBlock(iterator) =>
blockManager.putIterator(blockId, iterator, storageLevel, tellMaster = true)
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)
}
接着看trackerActor.ask(AddBlock(blockInfo))(askTimeout)，会发一个AddBlock消息到ReceiverTracker，进入看一下：
###org.apache.spark.streaming.scheduler/ReceiverTracker.scala
private def addBlock(receivedBlockInfo: ReceivedBlockInfo): Boolean = {
receivedBlockTracker.addBlock(receivedBlockInfo)
}
接着看receivedBlockTracker的addBlock方法,除了这个方法之外，还看receivedBlockTracker的几个重要变量,
先看方法:
###org.apache.spark.streaming.scheduler/ReceivedBlockTracker.scala
def addBlock(receivedBlockInfo: ReceivedBlockInfo): Boolean = synchronized {
try {
writeToLog(BlockAdditionEvent(receivedBlockInfo))
getReceivedBlockQueue(receivedBlockInfo.streamId) += receivedBlockInfo
logDebug(s"Stream ${receivedBlockInfo.streamId} received " +
s"block ${receivedBlockInfo.blockStoreResult.blockId}")
true
} catch {
case e: Exception =>
logError(s"Error adding block $receivedBlockInfo", e)
false
}
}
再看变量
###org.apache.spark.streaming.scheduler/ReceivedBlockTracker.scala // 封装了streamId到block的映射
private val streamIdToUnallocatedBlockQueues = new mutable.HashMap[Int, ReceivedBlockQueue]
// 封装了time到block的映射
private val timeToAllocatedBlocks = new mutable.HashMap[Time, AllocatedBlocks]
// 如果开启了预写机制机制，这还有LogManager，ReceiverTracker接收到数据时，也会判断，
// 如果开启了预写日志机制，写一份到预写日志中
private val logManagerOption = createLogManager()