源码目录

1. 程序入口

 // 初始化 StreamingContext
 SparkConf conf = new SparkConf().setAppName("SparkStreaming_demo")
       .set("spark.streaming.stopGracefullyOnShutdown", "true");
 JavaStreamingContext streamingContext = new JavaStreamingContext(conf, Durations.seconds(30));

 // 构建处理链
 Map kafkaParams = new HashMap();
 kafkaParams.put("bootstrap.servers", BOOTSTRAP_SERVERS);
 kafkaParams.put("key.deserializer", StringDeserializer.class);
 kafkaParams.put("value.deserializer", StringDeserializer.class);
 kafkaParams.put("group.id", "exampleGroup");
 kafkaParams.put("auto.offset.reset", "latest");
 kafkaParams.put("enable.auto.commit", false);

 Collection topics = Arrays.asList("exampleTopic");

 JavaInputDStream> inputDStream = KafkaUtils.createDirectStream(streamingContext,
                LocationStrategies.PreferConsistent(), ConsumerStrategies.Subscribe(topics, kafkaParams));

 JavaDStream transformDStream = inputDStream.transform(new Function>, JavaRDD>() {
      @Override
      public JavaRDD call(JavaRDD> v1) throws Exception {
         JavaRDD tempRdd = v1.map(new Function, String>() {
             @Override
             public String call(ConsumerRecord v1) throws Exception {
                 return v1.value();
             }
         });
         return tempRdd;
     }
 });

 JavaDStream filterDStream = transformDStream.filter(new Function() {
     @Override
     public Boolean call(String v1) throws Exception {
         return StringUtils.isNotBlank(v1);
     }
 });

 filterDStream.foreachRDD(new VoidFunction>() {
     @Override
     public void call(JavaRDD javaRDD) throws Exception {
         javaRDD.saveAsTextFile("/home/example/result/");
     }
 });
 
 // 启动运行
 streamingContext.start();
 streamingContext.awaitTermination();

本文主要看SparkStreaming链式处理过程的构建。

2. 进入源码

2.1 创建 InputDStream

进入 org.apache.spark.streaming.kafka010.KafkaUtils.scala

  def createDirectStream[K, V](
      jssc: JavaStreamingContext,
      locationStrategy: LocationStrategy,
      consumerStrategy: ConsumerStrategy[K, V]
    ): JavaInputDStream[ConsumerRecord[K, V]] = {
    new JavaInputDStream(
      createDirectStream[K, V](
        jssc.ssc, locationStrategy, consumerStrategy))
  }


  def createDirectStream[K, V](
      ssc: StreamingContext,
      locationStrategy: LocationStrategy,
      consumerStrategy: ConsumerStrategy[K, V]
    ): InputDStream[ConsumerRecord[K, V]] = {
    new DirectKafkaInputDStream[K, V](ssc, locationStrategy, consumerStrategy)
  }

createDirectStream时会直接 new DirectKafkaInputDStream 出来。

进入org.apache.spark.streaming.kafka010.DirectKafkaInputDStream.scala

private[spark] class DirectKafkaInputDStream[K, V](
    _ssc: StreamingContext,
    locationStrategy: LocationStrategy,
    consumerStrategy: ConsumerStrategy[K, V]
  ) extends InputDStream[ConsumerRecord[K, V]](_ssc) with Logging with CanCommitOffsets {

  override protected[streaming] val rateController: Option[RateController] = {
    if (RateController.isBackPressureEnabled(ssc.conf)) {
      Some(new DirectKafkaRateController(id,
        RateEstimator.create(ssc.conf, context.graph.batchDuration)))
    } else {
      None
    }
  }

  private val maxRateLimitPerPartition: Int = context.sparkContext.getConf.getInt(
    "spark.streaming.kafka.maxRatePerPartition", 0)

  ......

}

在创建DirectKafkaInputDStream 时，会：

创建 rateController，此时会读取配置spark.streaming.backpressure.enabled看反压机制是否打开，如果打开了则构建一个新的速率推算器作为 rateController；
读取配置spark.streaming.kafka.maxRatePerPartition看是否配置了各分区最大消费速率，如果配置了则配合上面的 rateController 一起来控制 offset 的变化，从而控制每个批次拉取的数据量；
等等

DirectKafkaInputDStream 是 InputDStream 的子类，因此会先构造 InputDStream。

进入org.apache.spark.streaming.dstream.InputDStream.scala

abstract class InputDStream[T: ClassTag](_ssc: StreamingContext)
  extends DStream[T](_ssc) {

  ssc.graph.addInputStream(this)

  val id = ssc.getNewInputStreamId()

  ......

}

此处将构造的 DirectKafkaInputDStream 加入到之前初始化完毕的 StreamingContext 的 DStreamGraph 的 inputStreams 中。

2.2 transform 操作

进入org.apache.spark.streaming.api.java.AbstractJavaDStreamLike.scala

  def transform[U](transformFunc: JFunction[R, JavaRDD[U]]): JavaDStream[U] = {
    implicit val cm: ClassTag[U] = fakeClassTag

    def scalaTransform (in: RDD[T]): RDD[U] =
      transformFunc.call(wrapRDD(in)).rdd
    dstream.transform(scalaTransform(_))
  }

再进入org.apache.spark.streaming.dstream.DStream.scala

  def transform[U: ClassTag](transformFunc: RDD[T] => RDD[U]): DStream[U] = ssc.withScope {
    // because the DStream is reachable from the outer object here, and because
    // DStreams can't be serialized with closures, we can't proactively check
    // it for serializability and so we pass the optional false to SparkContext.clean
    val cleanedF = context.sparkContext.clean(transformFunc, false)
    transform((r: RDD[T], _: Time) => cleanedF(r))
  }


  def transform[U: ClassTag](transformFunc: (RDD[T], Time) => RDD[U]): DStream[U] = ssc.withScope {
    // because the DStream is reachable from the outer object here, and because
    // DStreams can't be serialized with closures, we can't proactively check
    // it for serializability and so we pass the optional false to SparkContext.clean
    val cleanedF = context.sparkContext.clean(transformFunc, false)
    val realTransformFunc = (rdds: Seq[RDD[_]], time: Time) => {
      assert(rdds.length == 1)
      cleanedF(rdds.head.asInstanceOf[RDD[T]], time)
    }
    new TransformedDStream[U](Seq(this), realTransformFunc)
  }

transform 操作最终会创建 TransformedDStream。

进入org.apache.spark.streaming.dstream.TransformedDStream.scala

private[streaming]
class TransformedDStream[U: ClassTag] (
    parents: Seq[DStream[_]],
    transformFunc: (Seq[RDD[_]], Time) => RDD[U]
  ) extends DStream[U](parents.head.ssc) {

  override def dependencies: List[DStream[_]] = parents.toList

  override def slideDuration: Duration = parents.head.slideDuration

  ......
}

创建 TransformedDStream 时会设置 dependencies，即：
dependencies = parents.toList，而 parents 即为上面new TransformedDStream[U](Seq(this), realTransformFunc)中的 Seq(this)，而 this 就是 inputDStream.transform中调用 transform 操作的 inputDStream。

至此，TransformedDStream 通过内部声明的 dependencies 与上一链的 DirectKafkaInputDStream 链接在了一起。

2.3 filter 操作

与 transform 操作类似，filter 操作会创建 FilteredDStream，在 FilteredDStream 内部同样使用 dependencies 将自身与上一链的 TransformedDStream 链接在了一起。

进入org.apache.spark.streaming.dstream.FilteredDStream.scala

private[streaming]
class FilteredDStream[T: ClassTag](
    parent: DStream[T],
    filterFunc: T => Boolean
  ) extends DStream[T](parent.ssc) {

  override def dependencies: List[DStream[_]] = List(parent)

  override def slideDuration: Duration = parent.slideDuration

  ......
}

2.4 foreachRDD 操作

进入org.apache.spark.streaming.dstream.DStream.scala

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

foreachRDD 操作中：

创建 ForEachDStream
调用 register() 注册

2.4.1 创建 ForEachDStream

进入org.apache.spark.streaming.dstream.ForEachDStream.scala

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

  ......
}

与前面类似，在 ForEachDStream 内部同样使用 dependencies 将自身与上一链的 FilteredDStream 链接在了一起。

2.4.2 调用`register()`注册

进入org.apache.spark.streaming.dstream.DStream.scala

  private[streaming] def register(): DStream[T] = {
    ssc.graph.addOutputStream(this)
    this
  }

此处将刚刚创建的 ForEachDStream 加入 DStreamGraph 的 outputStreams 中。

注意：
SparkStreaming 所有的 Output 操作都会生成 ForEachDStream。
即所有的 Output 操作都会注册到 DStreamGraph 的 outputStreams 中。

例如：print 操作最后调用的就是 foreachRDD

  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)
  }

3. 总结

创建 InputDStream，并加入到 DStreamGraph 的 inputStreams 中。
SparkStreaming的每个算子都会产生相应的 DStream（例如 FilteredDStream、ForEachDStream等）。
这些 DStream 内部使用 dependencies 来链接上一链的 DStream。
利用一个一个 DStream 的链接，构建出了 SparkStreaming 的处理链。
所有的 Output 操作都调用 foreachRDD 算子来运算，foreachRDD 算子将产生 ForEachDStream，并将 ForEachDStream 加入到 DStreamGraph 的 outputStreams 中。

SparkStreaming源码：构建处理链

1. 程序入口

2. 进入源码

2.1 创建 InputDStream

2.2 transform 操作

2.3 filter 操作

2.4 foreachRDD 操作

2.4.1 创建 ForEachDStream

2.4.2 调用`register()`注册

3. 总结

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SparkStreaming源码：构建处理链

1. 程序入口

2. 进入源码

2.1 创建 InputDStream

2.2 transform 操作

2.3 filter 操作

2.4 foreachRDD 操作

2.4.1 创建 ForEachDStream

2.4.2 调用register()注册

3. 总结

你可能感兴趣的:(SparkStreaming源码：构建处理链)

2.4.2 调用`register()`注册