spark streaming源码分析4 DStream相关API

时间：2021-08-31 20:55:26

 博客地址: http://blog.csdn.net/yueqian_zhu/
一、InputDStream创建的操作（StreamingContext.scala） 1、给定Receiver作为参数，创建ReceiverInputDStream,T为receiver接收到的数据类型 
def receiverStream[T: ClassTag](receiver: Receiver[T]): ReceiverInputDStream[T] = {
    withNamedScope("receiver stream") {
      new PluggableInputDStream[T](this, receiver)
    }
  }
 2、根据参数生成akka actorstream接收数据 
def actorStream[T: ClassTag](
      props: Props,
      name: String,
      storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2,
      supervisorStrategy: SupervisorStrategy = ActorSupervisorStrategy.defaultStrategy
    ): ReceiverInputDStream[T] = withNamedScope("actor stream") {
    receiverStream(new ActorReceiver[T](props, name, storageLevel, supervisorStrategy))
  }
3、TCP socket
socketStream：converter是从socket输入流转换成元素T的迭代器的方法
def socketStream[T: ClassTag](
      hostname: String,
      port: Int,
      converter: (InputStream) => Iterator[T],
      storageLevel: StorageLevel
    ): ReceiverInputDStream[T] = {
    new SocketInputDStream[T](this, hostname, port, converter, storageLevel)
  }
socketTextStream：storageLevel默认是MEMORY_AND_DISK_SER_2,converter是从inputstream中按行读取转换成迭代器的固定方法
def socketTextStream(
      hostname: String,
      port: Int,
      storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
    ): ReceiverInputDStream[String] = withNamedScope("socket text stream") {
    socketStream[String](hostname, port, SocketReceiver.bytesToLines, storageLevel)
  }
4、fileStream:filter：文件过滤器，newFileOnly：只读取新的文件。还有其他一些使用默认参数的方法。
def fileStream[
    K: ClassTag,
    V: ClassTag,
    F <: NewInputFormat[K, V]: ClassTag
  ] (directory: String,
     filter: Path => Boolean,
     newFilesOnly: Boolean,
     conf: Configuration): InputDStream[(K, V)] = {
    new FileInputDStream[K, V, F](this, directory, filter, newFilesOnly, Option(conf))
  }
一个以固定格式读取文件作为输入的接口
def textFileStream(directory: String): DStream[String] = withNamedScope("text file stream") {    fileStream[LongWritable, Text, TextInputFormat](directory).map(_._2.toString)  }
与receiverInputDStream不同，它是以文件作为输入，所以不需要receiver去读取。而是直接根据path生成hadoopRDD，再将所有的RDD Union起来。也就是说，在一个batchDuration时间间隔内，就将这个间隔内新的file组合成一个RDD。
5、将多个DStream 联合，返回UnionDStream。compute方法就是将多个DStream中的Rdd union
/**
   * Create a unified DStream from multiple DStreams of the same type and same slide duration.
   */
  def union[T: ClassTag](streams: Seq[DStream[T]]): DStream[T] = withScope {
    new UnionDStream[T](streams.toArray)
  }
6、transform:将dstreams中得到的所有rdds转换成一个RDD
/**
   * Create a new DStream in which each RDD is generated by applying a function on RDDs of
   * the DStreams.
   */
  def transform[T: ClassTag](
      dstreams: Seq[DStream[_]],
      transformFunc: (Seq[RDD[_]], Time) => RDD[T]
    ): DStream[T] = withScope {
    new TransformedDStream[T](dstreams, sparkContext.clean(transformFunc))
  }
二、DStream操作(DStream.scala)


与RDD不同的是，DStream是以一个outputStream作为一个job。


那outputStream是如何产生的呢？在调用foreachRDD方法时通过注册将一个DStream在DStreamGraph中标记为outputStream。


那有哪些API会注册outputStream呢？


foreachRDD/print


saveAsNewAPIHadoopFiles/saveAsTextFiles


1、map/flatMap/filter/mapPartitions


与RDD类似，分别生成MappedDstream/FlatMappedDStream/FilteredDStream等,真正运算时根据receiverInputDStream的compute方法产生BlockRDD，再在这个RDD上赋予map的方法参数执行操作。


2、重新分区


方法最终是将BlockRDD进行重新分区


/**
   * Return a new DStream with an increased or decreased level of parallelism. Each RDD in the
   * returned DStream has exactly numPartitions partitions.
   */
  def repartition(numPartitions: Int): DStream[T] = ssc.withScope {
    this.transform(_.repartition(numPartitions))
  }
3、reduce：这个方法将DStream的每个RDD都执行reduceFunc方法，并最终每个RDD只有一个分区,返回的还是一个DStream[T]


区别：RDD.scala的reduce方法是提交runJob的，返回一个确切的值。


/**
   * Return a new DStream in which each RDD has a single element generated by reducing each RDD
   * of this DStream.
   */
  def reduce(reduceFunc: (T, T) => T): DStream[T] = ssc.withScope {
    this.map(x => (null, x)).reduceByKey(reduceFunc, 1).map(_._2)
  }
4、count:这个方法是将DStream中的每个RDD进行计数，返回一个包含技术的DStream
/**   * Return a new DStream in which each RDD has a single element generated by counting each RDD   * of this DStream.   */  def count(): DStream[Long] = ssc.withScope {    this.map(_ => (null, 1L))        .transform(_.union(context.sparkContext.makeRDD(Seq((null, 0L)), 1)))        .reduceByKey(_ + _)        .map(_._2)  }
5、countByValue:类似count方法，只是该方法是按value值计数的
def countByValue(numPartitions: Int = ssc.sc.defaultParallelism)(implicit ord: Ordering[T] = null)      : DStream[(T, Long)] = ssc.withScope {    this.map(x => (x, 1L)).reduceByKey((x: Long, y: Long) => x + y, numPartitions)  }
6、foreachRDD：foreachFunc是在一个RDD进行自定义的任何操作


def foreachRDD(foreachFunc: RDD[T] => Unit): Unit = ssc.withScope {
    val cleanedF = context.sparkContext.clean(foreachFunc, false)
    this.foreachRDD((r: RDD[T], t: Time) => cleanedF(r))
  }
def foreachRDD(foreachFunc: (RDD[T], Time) => Unit): Unit = 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    new ForEachDStream(this, context.sparkContext.clean(foreachFunc, false)).register()  }
7、transform：在最终生成的RDD上执行transformFunc方法定义的转换操作


def transform[U: ClassTag](transformFunc: RDD[T] => RDD[U]): DStream[U]
def transform[U: ClassTag](transformFunc: (RDD[T], Time) => RDD[U]): DStream[U]
8、transformWith:将自身DStream生成的RDD与other生成的RDD一起，执行transformWith方法。
def transformWith[U: ClassTag, V: ClassTag](      other: DStream[U], transformFunc: (RDD[T], RDD[U]) => RDD[V]    ): DStream[V]
def transformWith[U: ClassTag, V: ClassTag](      other: DStream[U], transformFunc: (RDD[T], RDD[U], Time) => RDD[V]    ): DStream[V]
9、union联合


def union(that: DStream[T]): DStream[T] = ssc.withScope {
    new UnionDStream[T](Array(this, that))
  }
10、saveAsObjectFiles/saveAsTextFiles


保存为文件


三、K/V类型RDD转换操作
1、groupByKey
def groupByKey(): DStream[(K, Iterable[V])] = ssc.withScope {    groupByKey(defaultPartitioner())  }
def groupByKey(numPartitions: Int): DStream[(K, Iterable[V])] = ssc.withScope {    groupByKey(defaultPartitioner(numPartitions))  }
def groupByKey(partitioner: Partitioner): DStream[(K, Iterable[V])] = ssc.withScope {    val createCombiner = (v: V) => ArrayBuffer[V](v)    val mergeValue = (c: ArrayBuffer[V], v: V) => (c += v)    val mergeCombiner = (c1: ArrayBuffer[V], c2: ArrayBuffer[V]) => (c1 ++ c2)    combineByKey(createCombiner, mergeValue, mergeCombiner, partitioner)      .asInstanceOf[DStream[(K, Iterable[V])]]  }
2、reduceByKey
def reduceByKey(reduceFunc: (V, V) => V): DStream[(K, V)]
def reduceByKey(      reduceFunc: (V, V) => V,      numPartitions: Int): DStream[(K, V)]
def reduceByKey(      reduceFunc: (V, V) => V,      partitioner: Partitioner): DStream[(K, V)]
3、combineByKey
def combineByKey[C: ClassTag](      createCombiner: V => C,      mergeValue: (C, V) => C,      mergeCombiner: (C, C) => C,      partitioner: Partitioner,      mapSideCombine: Boolean = true): DStream[(K, C)] = ssc.withScope {    val cleanedCreateCombiner = sparkContext.clean(createCombiner)    val cleanedMergeValue = sparkContext.clean(mergeValue)    val cleanedMergeCombiner = sparkContext.clean(mergeCombiner)    new ShuffledDStream[K, V, C](      self,      cleanedCreateCombiner,      cleanedMergeValue,      cleanedMergeCombiner,      partitioner,      mapSideCombine)  }
4、mapValues/flatMapValues
与RDD的操作类似，不解释


5、join


内部调用transformWith，transformWith的参数就是将两个参数RDD作join操作。


def join[W: ClassTag](
      other: DStream[(K, W)],
      partitioner: Partitioner
    ): DStream[(K, (V, W))] = ssc.withScope {
    self.transformWith(
      other,
      (rdd1: RDD[(K, V)], rdd2: RDD[(K, W)]) => rdd1.join(rdd2, partitioner)
    )
  }
6、saveAsNewAPIHadoopFiles


保存到文件




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