Spark Streaming源码解读之RDD生成全生命周期详解

本篇博客将详细探讨DStream模板下的RDD是如何被创建，然后被执行的。在开始叙述之前，先来思考几个问题，本篇文章也就是基于此问题构建的。
1. RDD是谁产生的？
2. 如何产生RDD？
带着这两个问题开启我们的探索之旅。
一：实战WordCount源码如下：

object WordCount {  def main(args:Array[String]): Unit ={    val sparkConf = new SparkConf().setMaster("Master:7077").setAppName("WordCount")    val ssc = new StreamingContext(sparkConf,Seconds(1))    val lines = ssc.socketTextStream("Master",9999)    val words = lines.flatMap(_.split(" "))    val wordCounts = words.map(x => (x,1)).reduceByKey(_+_)    wordCounts.print()    ssc.start()    ssc.awaitTermination()  }}

1. Dstream之间是有依赖关系。比如map操作，产生MappedDStream.

/** Return a new DStream by applying a function to all elements of this DStream. */def map[U: ClassTag](mapFunc: T => U): DStream[U] = ssc.withScope {  new MappedDStream(this, context.sparkContext.clean(mapFunc))}

2.  MappedDStream中的compute方法，会先获取parent Dstream.然后基于其结果进行map操作，其中mapFunc就是我们传入的业务逻辑。

private[streaming]class MappedDStream[T: ClassTag, U: ClassTag] (    parent: DStream[T],    mapFunc: T => U  ) extends DStream[U](parent.ssc) {  override def dependencies: List[DStream[_]] = List(parent)  override def slideDuration: Duration = parent.slideDuration  override def compute(validTime: Time): Option[RDD[U]] = {    parent.getOrCompute(validTime).map(_.map[U](mapFunc))  }}

3.  DStream:a)  每个DStream之间有依赖关系，除了第一个DStream是基于数据源产生，其他DStream均依赖于前面的DStream.b)  DStream基于时间产生RDD。

* DStreams internally is characterized by a few basic properties: *  - A list of other DStreams that the DStream depends on *  - A time interval at which the DStream generates an RDD *  - A function that is used to generate an RDD after each time interval */abstract class DStream[T: ClassTag] (    @transient private[streaming] var ssc: StreamingContext  ) extends Serializable with Logging {

至此，我们就知道了，RDD是DStream产生的，那么DStream是如何产生RDD的呢？

1. DStream中的generatedRDDs的HashMap中每个Time都会产生一个RDD，而每个RDD都对应着一个Job，因为此时的RDD就是整个DStream操作的时间间隔的最后一个RDD，而最后一个RDD和前面的RDD是有依赖关系。

// RDDs generated, marked as private[streaming] so that testsuites can access it@transientprivate[streaming] var generatedRDDs = new HashMap[Time, RDD[T]] ()

generatedRDDs是DStream的成员，说明DStream的实例中均有此成员，但是实质在运行的时候指抓住最后一个DStream的句柄。

generatedRDDs在哪里被实例化的？搞清楚了这里的HashMap在哪里被实例化的话，就知道RDD是怎么产生的。
1. DStream中的getOrCompute会根据时间生成RDD。

/** * Get the RDD corresponding to the given time; either retrieve it from cache * or compute-and-cache it. */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根据时间计算产生RDD          compute(time)        }      }//rddOption里面有RDD生成的逻辑，然后生成的RDD，会put到generatedRDDs中      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    }  }}

2.  在ReceiverInputDStream中compute源码如下：ReceiverInputDStream会生成计算链条中的首个RDD。后面的RDD就会依赖此RDD。

/** * 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.//如果没有输入数据会产生一系列空的RDD      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// receiverTracker会跟踪数据      val receiverTracker = ssc.scheduler.receiverTracker// blockInfos      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)// validTime是      // Create the BlockRDD      createBlockRDD(validTime, blockInfos)    }  }  Some(blockRDD)}

3.  createBlockRDD源码如下：

private[streaming] def createBlockRDD(time: Time, blockInfos: Seq[ReceivedBlockInfo]): RDD[T] = {  if (blockInfos.nonEmpty) {    val blockIds = blockInfos.map { _.blockId.asInstanceOf[BlockId] }.toArray    // Are WAL record handles present with all the blocks    val areWALRecordHandlesPresent = blockInfos.forall { _.walRecordHandleOption.nonEmpty }    if (areWALRecordHandlesPresent) {      // If all the blocks have WAL record handle, then create a WALBackedBlockRDD      val isBlockIdValid = blockInfos.map { _.isBlockIdValid() }.toArray      val walRecordHandles = blockInfos.map { _.walRecordHandleOption.get }.toArray      new WriteAheadLogBackedBlockRDD[T](        ssc.sparkContext, blockIds, walRecordHandles, isBlockIdValid)    } else {      // Else, create a BlockRDD. However, if there are some blocks with WAL info but not      // others then that is unexpected and log a warning accordingly.      if (blockInfos.find(_.walRecordHandleOption.nonEmpty).nonEmpty) {        if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) {          logError("Some blocks do not have Write Ahead Log information; " +            "this is unexpected and data may not be recoverable after driver failures")        } else {          logWarning("Some blocks have Write Ahead Log information; this is unexpected")        }      }//校验数据是否还存在，不存在就过滤掉，此时的master是BlockManager      val validBlockIds = blockIds.filter { id =>        ssc.sparkContext.env.blockManager.master.contains(id)      }      if (validBlockIds.size != blockIds.size) {        logWarning("Some blocks could not be recovered as they were not found in memory. " +          "To prevent such data loss, enabled Write Ahead Log (see programming guide " +          "for more details.")      }      new BlockRDD[T](ssc.sc, validBlockIds)    }  } else {    // If no block is ready now, creating WriteAheadLogBackedBlockRDD or BlockRDD    // according to the configuration    if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) {      new WriteAheadLogBackedBlockRDD[T](        ssc.sparkContext, Array.empty, Array.empty, Array.empty)    } else {      new BlockRDD[T](ssc.sc, Array.empty)    }  }}

4.  map算子操作，产生MappedDStream。

/** Return a new DStream by applying a function to all elements of this DStream. */def map[U: ClassTag](mapFunc: T => U): DStream[U] = ssc.withScope {  new MappedDStream(this, context.sparkContext.clean(mapFunc))}

5.  MappedDStream源码如下：除了第一个DStream产生RDD之外，其他的DStream都是从前面DStream产生的RDD开始计算，然后返回RDD，因此，对DStream的transformations操作就是对RDD进行transformations操作。

private[streaming]class MappedDStream[T: ClassTag, U: ClassTag] (    parent: DStream[T],    mapFunc: T => U  ) extends DStream[U](parent.ssc) {  override def dependencies: List[DStream[_]] = List(parent)  override def slideDuration: Duration = parent.slideDuration//parent就是父DStream  override def compute(validTime: Time): Option[RDD[U]] = {// getOrCompute是对RDD进行操作，后面的map就是对RDD进行操作//DStream里面的计算其实是对RDD进行计算，而mapFunc就是我们要操作的具体业务逻辑。    parent.getOrCompute(validTime).map(_.map[U](mapFunc))  }}

6.  forEachDStream的源码如下：

/** * An internal DStream used to represent output operations like DStream.foreachRDD. * @param parent        Parent DStream * @param foreachFunc   Function to apply on each RDD generated by the parent DStream * @param displayInnerRDDOps Whether the detailed callsites and scopes of the RDDs generated *                           by `foreachFunc` will be displayed in the UI; only the scope and *                           callsite of `DStream.foreachRDD` will be displayed. */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)        }//此时考虑jobFunc中一定有action操作//因此jobFunc被调用的时候就会触发action操作            Some(new Job(time, jobFunc))      case None => None    }  }}

7.  在上述案例中print函数源码如下，foreachFunc函数中直接对RDD进行操作。

/** * 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) => {//action操作      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)}

上述都是从逻辑方面把RDD的生成流程走了一遍，下面我们就看正在开始是在哪里触发的。

1. 在JobGenerator中generateJobs源码如下：

/** 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 batch//生成Job    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))}

2.  在DStreamGraph中我们前面分析的RDD的产生的动作正在被触发了。

def generateJobs(time: Time): Seq[Job] = {  logDebug("Generating jobs for time " + time)  val jobs = this.synchronized {//此时的outputStream就是forEachDStream    outputStreams.flatMap { outputStream =>      val jobOption = outputStream.generateJob(time)      jobOption.foreach(_.setCallSite(outputStream.creationSite))      jobOption    }  }  logDebug("Generated " + jobs.length + " jobs for time " + time)  jobs}

RDD的创建和执行流程如下：