RDD是怎么生成的？
RDD依靠什么生成？根据DStream来的
RDD生成的依据是什么？
Spark Streaming中RDD的执行是否和Spark Core中的RDD执行有所不同？
运行之后我们对RDD怎么处理？
ForEachDStream不一定会触发Job的执行，但是它一定会触发job的产生，和Job是否执行没有关系；
问：RDD依靠什么生成的？
下面以案例来研究RDD是依靠DStream产生的
def main(args: Array[String]): Unit = { val conf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("wordcount") val sc = new StreamingContext(conf, Duration(3000)) val streamDs: ReceiverInputDStream[String] = sc.socketTextStream("localhost", 9999)//输入的DStreamval words: DStream[String] = streamDs.flatMap(_.split(" "))//输入和输出之间的都是transformation的DStreamval word: DStream[(String, Int)] = words.map((_, 1)) val wordCount: DStream[(String, Int)] = word.reduceByKey((x, y) => { x + y }) wordCount.print()// 内部会导致Action级别的触发 print（）输出的DStreamsc.start() sc.awaitTermination() }
从代码中分析代码中此案例依次产生了如下DStream,并且它们是从后往前依赖的：
ReceiverInputDStream-》new FlatMappedDStream(this, context.sparkContext.clean(flatMapFunc))-》new MappedDStream(this, context.sparkContext.clean(mapFunc))-》new ShuffledDStream[K, V, C](self,cleanedCreateCombiner,cleanedMergeValue,cleanedMergeCombiner,partitioner, mapSideCombine)-》ForEachDStream
简化一下就是：ReceiverInputDStream-->FlatMappedDStream-->MappedDStream-->ShuffledDStream-->ForEachDStream
那么怎么证明Stream之间是相互依赖的，我们挑选一个Dstream为入口进行分析，比如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)) } MappedDStream类中 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))
MappedDStream类中的compute方法会获取parent dstream，然后基于其结果进行map操作，mapFunc就是我们需要传入的业务逻辑，这就证明了dstream的依赖关系 } }
问：DStream为什么要从后往前依赖呢？
因为DStream代表Spark Streaming业务逻辑，RDD是从后往前依赖的，DStream是lazy级别的。DStream的依赖关系必须和RDD的依赖关系保持高度一致
A DStream 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
大致意思是：
1.DStream依赖于其他DStream，除了第一个DStream，因为第一个DStream基于数据源产生，用于接收数据，所以无其他依赖；进一步证明了DStream是从后往前依赖！！
2.基于DStream怎么产生RDD？每隔BatchDuration，DStream生成一个RDD；
3.每隔BatchDuration，DStream内部函数会生成RDD
abstract class DStream[T: ClassTag] (
@transient private[streaming] var ssc: StreamingContext
) extends Serializable with Logging {
// RDDs generated, marked as private[streaming] so that testsuites can access it//DStream是RDD的模板，每隔一个batchInterval会根据DStream模板生成一个对应的RDD。然后将RDD存储到DStream中的generatedRDDs数据结构中
@transient
private[streaming] var generatedRDDs = new HashMap[Time, RDD[T]]()
}
到此，我们验证了RDD是DStream是产生的结论！
下一节我们分析DStream是到底怎么生成RDD的？
stream中RDD的生成：
//DStream是RDD的模板，每隔一个batchInterval会根据DStream模板生成一个对应的RDD。然后将RDD存储到DStream中的generatedRDDs数据结构中 @transient private[streaming] var generatedRDDs = new HashMap[Time, RDD[T]] () 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.进入DStream的getOrCompute方法：
/** * Get the RDD corresponding to the given time; either retrieve it from cache * or compute-and-cache it.先根据时间判断HashMap中是否已存在该时间对应的RDD，如果没有则调用compute得到RDD，并放入到HashMap中*/ 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/看缓存中是否有，有的话直接获取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. SparkHadoopWriterUtils.disableOutputSpecValidation.withValue(true) { compute(time)
//根据时间计算产生RDD} }//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 } } }
进入compute方法，发现其并没有具体的实现，说明在其子类中有重写并生成rdd
/** Method that generates a RDD for the given time */ def compute(validTime: Time): Option[RDD[T]]
2.进入ReceiverInputDStream的compute方法
/** * 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// 创建并返回BlockRDD，由于ReceiverInputDStream没有父依赖，所以自己生成RDD。<br> // 如果没有输入数据会产生一系列空的RDD<br> createBlockRDD(validTime, blockInfos)
createBlockRDD(validTime, blockInfos) } } Some(blockRDD) }
注意：Spark Streaming实际上在没有输入数据的时候仍然会产生RDD（空的BlockRDD），所以可以在此修改源码，提升性能。反过来仔细思考一下，流处理实际上就是时间极短的情况下完成的批处理！！
3.再进入MappedDStream的compute方法
class MappedDStream[T: ClassTag, U: ClassTag] ( parent: DStream[T], mapFunc: T => U ) extends DStream[U](parent.ssc) { <br> //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 override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration 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))
}
4.进入ForEachDStream的compute的方法：
发现其compute方法没有任何操作，但是重写了generateJob方法！
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 } } }
//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
5.从Job生成入手，JobGenerator的generateJobs方法，内部调用的DStreamGraph的generateJobs方法：
/** 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 {<br> <em>//根据特定的时间获取具体的数据</em> jobScheduler.receiverTracker.allocateBlocksToBatch(time) // allocate received blocks to batch<br><em> //调用DStreamGraph的generateJobs生成Job</em> 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)) }
DStreamGraph的generateJobs方法调用了OutputStream的generateJob方法，OutputStream就是ForEachDStream：
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 }
总结：DStream是RDD的模板，其内部generatedRDDs 保存了每个BatchDuration时间生成的RDD对象实例。DStream的依赖构成了RDD依赖关系，即从后往前计算时，只要对最后一个DStream计算即可。JobGenerator每隔BatchDuration调用DStreamGraph的generateJobs方法，调用了ForEachDStream的generateJob方法，其内部先调用父DStream的getOrCompute方法来获取RDD，然后在进行计算，从后往前推，第一个DStream是ReceiverInputDStream，其comput方法中从receiverTracker中获取对应时间段的metadata信息，然后生成BlockRDD对象，并放入到generatedRDDs中！！
转载：https://www.cnblogs.com/game-bigdata/p/5521660.html