Spark版本定制八：Spark Streaming源码解读之RDD生成全生命周期彻底研究和思考
本期内容：
1、DStream与RDD关系彻底研究
2、Streaming中RDD的生成彻底研究
一、DStream与RDD关系彻底研究
课前思考：
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？
下面以官方自带的案例来研究RDD是依靠DStream产生的：
object NetworkWordCount {
def main(args: Array[String]) {
// Create the context with a 1 second batch size
val sparkConf = new SparkConf().setAppName("NetworkWordCount")
val ssc = new StreamingContext(sparkConf, Seconds(1))
val lines = ssc.socketTextStream("Master", 9999)//输入的DStream
val words = lines.flatMap(_.split(" ")) //输入和输出之间的都是transformation的DStream
val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
wordCounts.print() // 内部会导致Action级别的触发 print（）输出的DStream
ssc.start()
ssc.awaitTermination()
}
}
从上面的红色代码中分析出此案例依次产生了如下DStream,并且它们是从后往前依赖的：
ReceiverInputDStream-->FlatMappedDStream-->MappedDStream-->ShuffledDStream-->ForEachDStream
如何证明DStream之间是相互依赖的呢，我们随便挑一个子DStream作为入口进行分析，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：
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，这就证明了它们的依赖关系！
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
上面产生的子DStream都继承自DStream，所以我们从DStream入手：
/*
* 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
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]] ()
generatedRDDs是DStream的成员，说明DStream的实例中均有此成员，但实际运行的时候，只需要知道最好一个DStream即可，因为可以从最后一个推导出之前所以的DStream！！
到此，我们验证了RDD是DStream是产生的结论！
下一节我们分析DStream是到底怎么生存RDD的？
二、Streaming中RDD的生成彻底研究
//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.直接切入主题，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. PairRDDFunctions.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
// receiverTracker跟踪数据的产生
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。
// 如果没有输入数据会产生一系列空的RDD
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) {
//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)
}
//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
}
}
}
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 {
//根据特定的时间获取具体的数据
jobScheduler.receiverTracker.allocateBlocksToBatch(time) // allocate received blocks to batch
//调用DStreamGraph的generateJobs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))
}
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！！
特别感谢王家林老师的独具一格的讲解：
王家林老师名片：
中国Spark第一人
新浪微博：http://weibo.com/ilovepains
微信公众号：DT_Spark
博客：http://blog.sina.com.cn/ilovepains
QQ：1740415547
YY课堂：每天20:00现场授课频道68917580
posted on 2016-05-23 22:25 Harvey.Sun 阅读(286) 评论(0) 编辑 收藏 举报
