(版本定制)第8课：Spark Streaming源码解读之RDD生成生命周期彻底研究和思考
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。
DStream是RDD的模板，每隔一个Batch Interval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，下面是存储结构格式。
// RDDs generated, marked as private so that testsuites can access it
@transient
private var generatedRDDs = new HashMap, RDD[T]] () 1、简单的WordCount程序
object WordCount {def main(args:Array): Unit ={
val sparkConf = new SparkConf().setMaster("Master:7077").setAppName("WordCount")
val ssc = new StreamingContext(sparkConf,Seconds(10)) // Timer触发频率
val lines = ssc.socketTextStream("Master",9999) //接收数据
val words = lines.flatMap(_.split(" "))
val wordCounts = words.map(x => (x,1)).reduceByKey(_+_)
wordCounts.print()
ssc.start()
ssc.awaitTermination()
}
}
首先我们先看看print方法，具体的代码如下：
/**
* 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)
}
首先定义了一个函数，该函数用来从RDD中取出前几条数据，并打印出结果与时间等，后面会调用foreachRDD函数。
private def foreachRDD(
foreachFunc: (RDD[T], Time) => Unit,
displayInnerRDDOps: Boolean): Unit = {
new ForEachDStream(this,context.sparkContext.clean(foreachFunc, false), displayInnerRDDOps).register()
}
/**
* Register this streaming as an output stream. This would ensure that RDDs of this
* DStream will be generated.
*/
private def register(): DStream[T] = {
ssc.graph.addOutputStream(this)
this
}
def addOutputStream(outputStream: DStream) {
this.synchronized {
outputStream.setGraph(this)
outputStreams += outputStream
}
在foreachRDD中new出了一个ForEachDStream对象，并将这个注册给DStreamGraph，ForEachDStream对象也就是DStreamGraph中的outputStreams。
当每到达一个BatchInterval时候，就会调用DStreamingGraph中的generateJobs.
def generateJobs(time: Time): Seq = {
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
} 这里就会调用outputStream的generateJob方法
private def generateJob(time: Time): Option = {
getOrCompute(time) match {
case Some(rdd) => {
val jobFunc = () => {
val emptyFunc = { (iterator: Iterator[T]) => {} }
context.sparkContext.runJob(rdd, emptyFunc)
}
Some(new Job(time, jobFunc))
}
case None => None
}
} 这里会调用getOrCompute(time)来产生新RDD，并将其存入到generatedRDDs中，整理的过程如下图：
http://s2.运维网.com/wyfs02/M01/80/65/wKiom1c_10vTnzw6AAG7UcsT23U458.png
参考博客：备注：
1、DT大数据梦工厂微信公众号DT_Spark
2、IMF晚8点大数据实战YY直播频道号：68917580
3、新浪微博: http://www.weibo.com/ilovepains
页:
[1]