SparkStreaming源码之Dstream和DstreamGraph篇
先谈DstreamGraph，
在DstreamGraph中有两个ArrayBuffer，
private val inputStreams = new ArrayBuffer[InputDStream[_]]()
private val outputStreams = new ArrayBuffer[DStream[_]]()
inputStreams的作用就是存放一个流的inputDstream，例如SocketInputDStream,他是在父类InputDStream中执行具体的存放操作
abstract class InputDStream[T: ClassTag] (ssc_ : StreamingContext)
extends DStream[T](ssc_) {
private[streaming] var lastValidTime: Time = null
//todo 将DStream放入到DstreamGraph的InputStream数组中
ssc.graph.addInputStream(this)
那么InputDStream接收的数据又是如何进行存储的呢?
/** 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) {
//todo 通过网络接受数据不断的尽心存储
store(iterator.next)
}
if (!isStopped()) {
restart("Socket data stream had no more data")
} else {
logInfo("Stopped receiving")
}
} catch {
case e: java.net.ConnectException =>
restart("Error connecting to " + host + ":" + port, e)
case NonFatal(e) =>
logWarning("Error receiving data", e)
restart("Error receiving data", e)
} finally {
if (socket != null) {
socket.close()
logInfo("Closed socket to " + host + ":" + port)
}
}
}
}
def store(dataItem: T) {
supervisor.pushSingle(dataItem)
}
经过代码追踪发现接受的数据实际上是以block的形式存放，BlockGenerator以spark.streaming.blockInterval作为时间单位来生成block块，内部有一个Timer来定时生成block块：我觉得这里的RecurringTimer做的挺好，同一个Timer根据不同的callback方法来执行不同的任务，get到了新技能,点赞！
private val blockIntervalMs = conf.getTimeAsMs("spark.streaming.blockInterval", "200ms")
require(blockIntervalMs > 0, s"'spark.streaming.blockInterval' should be a positive value")
/** 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) {
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 f
}
} catch {
case ie: InterruptedException =>
logInfo("Block updating timer thread was interrupted")
case e: Exception =>
reportError("Error in block updating thread", e)
}
}
上面说了inputStream,接下来看下outputStream,以print操作为例:
def print(num: Int): Unit = ssc.withScope {
def foreachFunc: (RDD[T], Time) => Unit = {
(rdd: RDD[T], time: Time) => {
val firstNum = rdd.take(num + 1)
// scalastyle:off println
println("-------------------------------------------")
println("Time: " + time)
println("-------------------------------------------")
firstNum.take(num).foreach(println)
if (firstNum.length > num) println("...")
println()
// scalastyle:on println
}
}
foreachRDD(context.sparkContext.clean(foreachFunc), displayInnerRDDOps = false)
}
private def foreachRDD(
foreachFunc: (RDD[T], Time) => Unit,
displayInnerRDDOps: Boolean): Unit = {
new ForEachDStream(this,
context.sparkContext.clean(foreachFunc, false), displayInnerRDDOps).register()
}
在这里将输出操作的Dstream注册进入了DstreamGraph的outputDstream中
/**
* Register this streaming as an output stream. This would ensure that RDDs of this
* DStream will be generated.
*/
private[streaming] def register(): DStream[T] = {
ssc.graph.addOutputStream(this)
this
}
还有就是Dstream中outputStreamArray中的action是如何触发job的,其实在jobGenertor中通过定时器RecurringTimer来实现的，那就再来看下这个定时器，RecurringTimer是在JobGneretor中进行实例化的
private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds,
longTime => eventLoop.post(GenerateJobs(new Time(longTime))), "JobGenerator")
来看下RecurringTimer执行的内容
private[streaming]
class RecurringTimer(clock: Clock, period: Long, callback: (Long) => Unit, name: String)
extends Logging {
private val thread = new Thread("RecurringTimer - " + name) {
setDaemon(true)
override def run() { loop }
}
private def triggerActionForNextInterval(): Unit = {
clock.waitTillTime(nextTime)
callback(nextTime)
prevTime = nextTime
nextTime += period
logDebug("Callback for " + name + " called at time " + prevTime)
}
里面的callback方法是关键，我们顺着来看下callback方法执行的内容
private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds,
longTime => eventLoop.post(GenerateJobs(new Time(longTime))), "JobGenerator")
/** Processes all events */
private def processEvent(event: JobGeneratorEvent) {
logDebug("Got event " + event)
event match {
case GenerateJobs(time) => generateJobs(time)
case ClearMetadata(time) => clearMetadata(time)
case DoCheckpoint(time, clearCheckpointDataLater) =>
doCheckpoint(time, clearCheckpointDataLater)
case ClearCheckpointData(time) => clearCheckpointData(time)
}
}
/** Generate jobs and perform checkpoint for the given `time`. */
private def generateJobs(time: Time) {
// Set the SparkEnv in this thread, so that job generation code can access the environment
// Example: BlockRDDs are created in this thread, and it needs to access BlockManager
// Update: This is probably redundant after threadlocal stuff in SparkEnv has been removed.
SparkEnv.set(ssc.env)
Try {
jobScheduler.receiverTracker.allocateBlocksToBatch(time) // allocate received blocks to batc
graph.generateJobs(time) // generate jobs using allocated block
} match {
case Success(jobs) =>
val streamIdToInputInfos = jobScheduler.inputInfoTracker.getInfo(time)
jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToInputInfos))
case Failure(e) =>
jobScheduler.reportError("Error generating jobs for time " + time, e)
}
eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = false))
}
def generateJobs(time: Time): Seq[Job] = {
logDebug("Generating jobs for time " + time)
val jobs = this.synchronized {
outputStreams.flatMap { outputStream =>
val jobOption = outputStream.generateJob(time)
jobOption.foreach(_.setCallSite(outputStream.creationSite))
jobOption
}
}
logDebug("Generated " + jobs.length + " jobs for time " + time)
jobs
}
在上面已经将要输出的DStream存放于DStreamGraph的outputStreams数组中，接下来就是具体的执行
private[streaming] def generateJob(time: Time): Option[Job] = {
getOrCompute(time) match {
case Some(rdd) => {
val jobFunc = () => {
val emptyFunc = { (iterator: Iterator[T]) => {} }
//todo 这里调用了SparkContext的runJob方法以RDD的形式执行
context.sparkContext.runJob(rdd, emptyFunc)
}
Some(new Job(time, jobFunc))
}
case None => None
}
}
再看DStream
第一：inputDStream是如何产生RDD的，还是以SocketInputDStraem为例:
/**
* Generates RDDs with blocks received by the receiver of this stream. */
override def compute(validTime: Time): Option[RDD[T]] = {
val blockRDD = {
if (validTime < graph.startTime) {
// If this is called for any time before the start time of the context,
// then this returns an empty RDD. This may happen when recovering from a
// driver failure without any write ahead log to recover pre-failure data.
new BlockRDD[T](ssc.sc, Array.empty)
} else {
// Otherwise, ask the tracker for all the blocks that have been allocated to this stream
// for this batch
val receiverTracker = ssc.scheduler.receiverTracker
val blockInfos = receiverTracker.getBlocksOfBatch(validTime).getOrElse(id, Seq.empty)
// Register the input blocks information into InputInfoTracker
val inputInfo = StreamInputInfo(id, blockInfos.flatMap(_.numRecords).sum)
ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo)
// Create the BlockRDD
createBlockRDD(validTime, blockInfos)
}
}
Some(blockRDD)
}
第二:Transform级别的DStream:例如:FlatMappedDStream,在它的comput方法中,使用parent.getOrCompute来获取父Dstream产生的RDD，然后使用父Dstream产生的RDD来执行map方法（此map方法是基于RDD的map方法）；此处可以发现SparkStreaming是对SparkCore的一层抽象，而SparkStreaming的实际执行还是基于sparkCore实体来执行的；
override def compute(validTime: Time): Option[RDD[U]] = {
parent.getOrCompute(validTime).map(_.flatMap(flatMapFunc))
}
第三:再看Action级别的DStream: 例如:print(), 在foreachFunc方法中就是基于RDD进行操作的；
/**
* Print the first num elements of each RDD generated in this DStream. This is an output
* operator, so this DStream will be registered as an output stream and there materialized
*/
def print(num: Int): Unit = ssc.withScope {
def foreachFunc: (RDD[T], Time) => Unit = {
(rdd: RDD[T], time: Time) => {
val firstNum = rdd.take(num + 1)
// scalastyle:off println
println("-------------------------------------------")
println("Time: " + time)
println("-------------------------------------------")
firstNum.take(num).foreach(println)
if (firstNum.length > num) println("...")
println()
// scalastyle:on println
}
}
foreachRDD(context.sparkContext.clean(foreachFunc), displayInnerRDDOps = false)
}
而foreachDStream中的compute方法为空,是因为foreachDStream是job中最后的Action操作，而generateJob内执行的执行发放foreachFunc中执行的还是RDD的输出操作；
private[streaming]
class ForEachDStream[T: ClassTag] (
parent: DStream[T],
foreachFunc: (RDD[T], Time) => Unit,
displayInnerRDDOps: Boolean
) extends DStream[Unit](parent.ssc) {
override def dependencies: List[DStream[_]] = List(parent)
override def slideDuration: Duration = parent.slideDuration
override def compute(validTime: Time): Option[RDD[Unit]] = None
override def generateJob(time: Time): Option[Job] = {
parent.getOrCompute(time) match {
case Some(rdd) =>
val jobFunc = () => createRDDWithLocalProperties(time, displayInnerRDDOps) {
foreachFunc(rdd, time)
}
Some(new Job(time, jobFunc))
case None => None
}
}
}
在JobGenerator中,定时器RecurringTimer不停的执行triggerActionForNextInterval的callback方法
private def triggerActionForNextInterval(): Unit = {
clock.waitTillTime(nextTime)
callback(nextTime)
prevTime = nextTime
nextTime += period
logDebug("Callback for " + name + " called at time " + prevTime)
}
callback方法具体执行的就是DStreamGraph中的generateJobs方法，
def generateJobs(time: Time): Seq[Job] = {
logDebug("Generating jobs for time " + time)
val jobs = this.synchronized {
outputStreams.flatMap { outputStream =>
val jobOption = outputStream.generateJob(time)
jobOption.foreach(_.setCallSite(outputStream.creationSite))
jobOption
}
}
logDebug("Generated " + jobs.length + " jobs for time " + time)
jobs
}
DStreamGraph中的generateJobs方法执行的是DStream的generateJob方法，在此方法中最终执行的是SparkCore的runJob方法;
private[streaming] def generateJob(time: Time): Option[Job] = {
getOrCompute(time) match {
case Some(rdd) => {
val jobFunc = () => {
val emptyFunc = { (iterator: Iterator[T]) => {} }
//todo 这里调用了SparkContext的runJob方法以RDD的形式执行
context.sparkContext.runJob(rdd, emptyFunc)
}
Some(new Job(time, jobFunc))
}
case None => None
}
}
而Dstream的generateJob方法中调用DStream的gerorcompute,在此方法中根据时间在generatedRDDs中存储对应Time的RDD数组,其他每个DStream都有一个这样的数据结构来根据Time来存储对应的RDD；
private[streaming] final def getOrCompute(time: Time): Option[RDD[T]] = {
// If RDD was already generated, then retrieve it from HashMap,
// or else compute the RDD
generatedRDDs.get(time).orElse {
// Compute the RDD if time is valid (e.g. correct time in a sliding window)
// of RDD generation, else generate nothing.
if (isTimeValid(time)) {
val rddOption = createRDDWithLocalProperties(time, displayInnerRDDOps = false) {
// Disable checks for existing output directories in jobs launched by the streaming
// scheduler, since we may need to write output to an existing directory during checkpoint
// recovery; see SPARK-4835 for more details. We need to have this call here because
// compute() might cause Spark jobs to be launched.
PairRDDFunctions.disableOutputSpecValidation.withValue(true) {
compute(time)
}
}
rddOption.foreach { case newRDD =>
// Register the generated RDD for caching and checkpointing
if (storageLevel != StorageLevel.NONE) {
newRDD.persist(storageLevel)
logDebug(s"Persisting RDD ${newRDD.id} for time $time to $storageLevel")
}
if (checkpointDuration != null && (time - zeroTime).isMultipleOf(checkpointDuration)) {
newRDD.checkpoint()
logInfo(s"Marking RDD ${newRDD.id} for time $time for checkpointing")
}
generatedRDDs.put(time, newRDD)
}
rddOption
} else {
None
}
}
}
接下来就是将基于RDD产生的Job提交给cluster进行执行……………
总结:
其实DStream只是基于RDD的一个抽象的模板,而DstreamGreaph就是生成DAG的模板，最终每个Dstream都会生成一个以time为key，RDD[T]为value的数据结 构用来存储基于模板生成的RDD，SparkStreaming最终做执行操作的还是SparkCore的RDD；
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