Spark Streaming源码解读之RDD生成全生命周期详解

Posted 2022-12-03 snail_gesture

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
@transient
private[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：