【Spark九十九】Spark Streaming的batch interval时间内的数据流转源码分析
编程技术 / houtizong 发布于 3年前 121
以如下代码为例（SocketInputDStream）：
Spark Streaming从Socket读取数据的代码是在SocketReceiver的receive方法中，撇开异常情况不谈(Receiver有重连机制，restart方法，默认情况下在Receiver挂了之后，间隔两秒钟重新建立Socket连接)，读取到的数据通过调用store(textRead)方法进行存储。数据的流转需要关注如下几个问题：
1. 数据存储到什么位置了
2. 数据存储的结构如何？
3. 数据什么时候被读取
4. 读取到的数据(batch interval)如何转换为RDD
1. SocketReceiver#receive
/** Create a socket connection and receive data until receiver is stopped */ def receive() { var socket: Socket = null try { logInfo("Connecting to " + host + ":" + port) socket = new Socket(host, port) logInfo("Connected to " + host + ":" + port) val iterator = bytesToObjects(socket.getInputStream()) while(!isStopped && iterator.hasNext) { store(iterator.next) } logInfo("Stopped receiving") restart("Retrying connecting to " + host + ":" + port) } catch { case e: java.net.ConnectException => restart("Error connecting to " + host + ":" + port, e) case t: Throwable => restart("Error receiving data", t) } finally { if (socket != null) { socket.close() logInfo("Closed socket to " + host + ":" + port) } } }
2. SocketReceiver#receive=>SocketReceiver#store
/** * 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) { executor.pushSingle(dataItem) }
数据存储作为Executor功能之一，store方法调用了executor中的pushSingle操作，此时的Single可以理解为一次数据读取，而dataItem就是一次读取的数据对象
3. SocketReceiver#store=>executor.pushSingle（ReceiverSupervisorImpl.pushSingle）
/** Push a single record of received data into block generator. */ def pushSingle(data: Any) { blockGenerator.addData(data) }
数据放入到了blockGenerator数据结构中了，blockGenerator，类型为BlockGenerator，顾名思义是一个block生成器，所谓的block生成器，是指Spark Streaming每隔一段时间(默认200毫秒, private val blockInterval = conf.getLong("spark.streaming.blockInterval", 200)）将接收到的数据合并成一个block，然后将这个block写入到BlockManager，继续沿着个思路分析
4. executor.pushSingle=>BlockGenerator.addData
/** * Push a single data item into the buffer. All received data items * will be periodically pushed into BlockManager. */ def addData (data: Any): Unit = synchronized { waitToPush() ///通过阻塞控制Push的速度 currentBuffer += data，将数据追加到currentBuffer中 }
当数据写入到currentBuffer中之后，似乎线索已经断了。事实上是BlockGenerator内部开启的两个线程（BlockIntervalTimer和BlockPushingThread）在背后继续处理currentBuffer
BlockIntervalTimer默认每200毫秒执行一次updateCurrentBufferer，该函数的功能是将类型为ArrayBuffer的currentBuffer合并成一个小的Block
private val blockInterval = conf.getLong("spark.streaming.blockInterval", 200) private val blockIntervalTimer = new RecurringTimer(clock, blockInterval, updateCurrentBuffer, "BlockGenerator")
BlockPushingThread是通过循环调用keepPushingBlocks将BlockIntervalTimer创建的各个Block写入到BlockManager中,
private val blockPushingThread = new Thread() { override def run() { keepPushingBlocks() } }
上面说到的两个线程的同步是通过ArrayBlockQueue实现的
private val blockQueueSize = conf.getInt("spark.streaming.blockQueueSize", 10) private val blocksForPushing = new ArrayBlockingQueue[Block](blockQueueSize)
5. BlockGenerator#updateCurrentBuffer
updateCurrentBuffer由BlockIntervalTimer线程执行
/** Change the buffer to which single records are added to. */ private def updateCurrentBuffer(time: Long): Unit = synchronized { try { val newBlockBuffer = currentBuffer currentBuffer = new ArrayBuffer[Any] //这两句对currentBuffere这样的操作，是否有线程安全问题？没有，因为currentBuffer已经标注为@volatile类型的变量 if (newBlockBuffer.size > 0) { val blockId = StreamBlockId(receiverId, time - blockInterval) //构造StreamBlockId val newBlock = new Block(blockId, newBlockBuffer) //创建出一个Block listener.onGenerateBlock(blockId) //通知谁？空实现，listener是作为BlockGenerator的构造函数传入的，这是一个所有通知时间的空实现 blocksForPushing.put(newBlock) //添加到阻塞队列中，等待BlockPushingThread读取 logDebug("Last element in " + blockId + " is " + newBlockBuffer.last) } } catch { case ie: InterruptedException => logInfo("Block updating timer thread was interrupted") case e: Exception => reportError("Error in block updating thread", e) } }
6. BlockGenerator#keepPushingBlocks
keepPushingBlocks由BlockPushingThread执行
/** Keep pushing blocks to the BlockManager. */ private def keepPushingBlocks() { logInfo("Started block pushing thread") try { while(!stopped) { //poll是阻塞队列的非阻塞方法，但是如果队列中没有元素，则等待100ms，poll是取一个元素操作 Option(blocksForPushing.poll(100, TimeUnit.MILLISECONDS)) match { 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) } }
7. BlockGenerator#pushBlock
这个方法是针对一个Block进行push，而不是一次从队列中把所有的Block取出来，一次进行push。
private def pushBlock(block: Block) { listener.onPushBlock(block.id, block.buffer) logInfo("Pushed block " + block.id) }
8. BlockGeneratorListener#onPushBlock
pushBlock是通过Observer模式，通知listener，这个liestener是BlockGenerator的构造函数传入的(其实是作为内部类，在构造时创建的实例）
/** Divides received data records into data blocks for pushing in BlockManager. */ private val blockGenerator = new BlockGenerator(new BlockGeneratorListener { def onAddData(data: Any, metadata: Any): Unit = { } def onGenerateBlock(blockId: StreamBlockId): Unit = { } def onError(message: String, throwable: Throwable) { reportError(message, throwable) } def onPushBlock(blockId: StreamBlockId, arrayBuffer: ArrayBuffer[_]) { pushArrayBuffer(arrayBuffer, None, Some(blockId)) } }, streamId, env.conf)
9. BlockGenerator#pushArrayBuffer
/** Store an ArrayBuffer of received data as a data block into Spark's memory. */ def pushArrayBuffer( arrayBuffer: ArrayBuffer[_], metadataOption: Option[Any], blockIdOption: Option[StreamBlockId] ) { pushAndReportBlock(ArrayBufferBlock(arrayBuffer), metadataOption, blockIdOption) }
10. BlockGenerator#pushAndReportBlock
/** Store block and report it to driver */ 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 val blockStoreResult = receivedBlockHandler.storeBlock(blockId, receivedBlock) logDebug(s"Pushed block $blockId in ${(System.currentTimeMillis - time)} ms") val blockInfo = ReceivedBlockInfo(streamId, numRecords, blockStoreResult) val future = trackerActor.ask(AddBlock(blockInfo))(askTimeout) Await.result(future, askTimeout) logDebug(s"Reported block $blockId") }
pushAndReportBlock做了两件事，一是Store Block，而是想Tracker汇报有Block加入
10.1 receivedBlockHandler.storeBlock（BlockManagerBasedBlockHandler#storeBlock）
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) }
其中，blockManager是BlockManager类型的变量，定义于org.apache.spark.storage包中，实现向BlockManager写入数据，具体调用putIterator，putBytes，这是Spark存储子系统的内容，此处不赘述，重要的是，在此处写入进了BlockManager
10.2 ReceiverTracker#AddBlock
通过下面两个语句，将写入到BlockManager的信息汇报给TrackActor，这是一个进程间的同步调用(ask语法）
val blockInfo = ReceivedBlockInfo(streamId, numRecords, blockStoreResult) val future = trackerActor.ask(AddBlock(blockInfo))(askTimeout) Await.result(future, askTimeout)
trackerActor对应的实体是ReceiverTracker，AddBlock消息将触发ReceiverTracker.addBlock,进而调用ReceivedBlockTracker.addBlock
/** Add new blocks for the given stream */ private def addBlock(receivedBlockInfo: ReceivedBlockInfo): Boolean = { receivedBlockTracker.addBlock(receivedBlockInfo) }
11. ReceivedBlockTracker.addBlock
/** Add received block. This event will get written to the write ahead log (if enabled). */ def addBlock(receivedBlockInfo: ReceivedBlockInfo): Boolean = synchronized { try { writeToLog(BlockAdditionEvent(receivedBlockInfo))//写WAL getReceivedBlockQueue(receivedBlockInfo.streamId) += receivedBlockInfo //getReceivedBlockQueue从Map<streamId,streamReceivedBlockQueue>中获取相应的streamReceivedBlockQueue logDebug(s"Stream ${receivedBlockInfo.streamId} received " + s"block ${receivedBlockInfo.blockStoreResult.blockId}") true } catch { case e: Exception => logError(s"Error adding block $receivedBlockInfo", e) false } }
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