DT大数据梦工厂Spark定制班笔记(008)

Spark Streaming源码解读之RDD生成全生命周期彻底研究和思考

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。

DStream.scala(86行)

private[streaming]vargeneratedRDDs =newHashMap[Time, RDD[T]]()

用于分析RDD生成的示例代码

val  lines  =  ssc.socketTextStream( "localhost" ,  9999 )
val  words  =  lines.flatMap( _ .split( " " ))
val  pairs  =  words.map(word  = > (word,  1 ))
val  wordCounts  =  pairs.reduceByKey( _  +  _ )
wordCounts.print()

DStream中Print的实现如下(DStream.scala 731-746行)

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(s"Time: $time")
      println("-------------------------------------------")
      firstNum.take(num).foreach(println)
      if (firstNum.length > num) println("...")
      println()
      // scalastyle:on println
    }
  }
  foreachRDD(context.sparkContext.clean(foreachFunc), displayInnerRDDOps = false)
}

其中foreachRDD的实现如下(DStream.scala 650-655行)

private def foreachRDD(
    foreachFunc: (RDD[T], Time) => Unit,
    displayInnerRDDOps: Boolean): Unit = {
  new ForEachDStream(this,
    context.sparkContext.clean(foreachFunc, false), displayInnerRDDOps).register()
}

核心部分是实例化ForEachDStream并将其注册（register）到DStreamGraph中。

ForEachDStream是DStream的子类，并且实现了generateJob函数（ForEachStream.scala 47-56行）。

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
  }
}

回顾第5讲的内容，generateJob会在DStreamGraph启动是被调用，从而触发一系列追溯DStream，生成RDD的操作。

详情如下图所示 （图来自http://lqding.blog.51cto.com/9123978/1773398 感谢作者！）