19转换&窗口

转换&窗口

转换

transform

不带状态，RDD之间没有联系

可以把Dstream的操作转换为 RDD操作

适用于 需要使用RDD的算子而Dstream中没有

类似于 forEachRDD(rdd=>{}),但是foreachRDD是行动算子(常用于把数据写出到外部系统)，transform为转换算子。

def main(args: Array[String]): Unit = {
    //创建StreamingContext

    val conf = new SparkConf().setMaster("local[2]").setAppName("streamWordCount")
    val sc = new StreamingContext(conf,Seconds(3))
    //从数据源创建一个流
    val stream = sc.socketTextStream("node1",9999)
    //转换为rdd
    //
    val value = stream.transform(rdd => {
      rdd.flatMap(_.split(" ")).map((_, 1)).reduceByKey(_ + _)
    })
    value.print()
    //启动
    sc.start()
    //阻值主线程退出
    sc.awaitTermination()
  }

updateStateBykey

带状态,实现所有周期结果累计

必须有checkpoint

def main(args: Array[String]): Unit = {
  //创建StreamingContext

  val conf = new SparkConf().setMaster("local[2]").setAppName("streamWordCount")
  val sc = new StreamingContext(conf,Seconds(3))
  //从数据源创建一个流
  val stream = sc.socketTextStream("node1",9999)
  //需要checkpoint才能带状态
  sc.checkpoint("check")
  //带状态updateStateByKey
  //seq :本批次rdd value集合
  //option:历史数据
  stream.flatMap(_.split(" ")).map((_,1)).updateStateByKey((seq:Seq[Int],opt:Option[Int])=>{
    Some(seq.sum+opt.getOrElse(0))
  }).print
  //启动
  sc.start()
  //阻值主线程退出
  sc.awaitTermination()
}

Window

reduceByKeyAndWindow

Spark Streaming 也提供了窗口计算, 允许执行转换操作作用在一个窗口内的数据.

窗口在 DStream 上每滑动一次, 落在窗口内的那些 RDD会结合在一起, 然后在上面操作产生新的 RDD, 组成了 window DStream.

窗口时间和步长必须是一个批次的整数倍

def reduceByKeyAndWindow(
      reduceFunc: (V, V) => V,
      windowDuration: Duration
    ): DStream[(K, V)] 

 def reduceByKeyAndWindow(
      reduceFunc: (V, V) => V,
      windowDuration: Duration,
      slideDuration: Duration
    ): DStream[(K, V)]

def reduceByKeyAndWindow(
      reduceFunc: (V, V) => V,
      windowDuration: Duration,
      slideDuration: Duration,
      numPartitions: Int
    ): DStream[(K, V)] 

 def reduceByKeyAndWindow(
      reduceFunc: (V, V) => V,
      windowDuration: Duration,
      slideDuration: Duration,
      partitioner: Partitioner
    ): DStream[(K, V)]

def main(args: Array[String]): Unit = {
  //创建StreamingContext

  val conf = new SparkConf().setMaster("local[2]").setAppName("streamWordCount")
  val sc = new StreamingContext(conf,Seconds(3))
  //从数据源创建一个流
  val stream = sc.socketTextStream("node1",9999)
  

  val value = stream.flatMap(_.split(" ")).map((_, 1)).reduceByKeyAndWindow(_ + _, Seconds(12),Seconds(6))
  value.print()
  //启动
  sc.start()
  //阻值主线程退出
  sc.awaitTermination()
}

优化计算 reduceByKeyAndWindow

减少窗口滑动过程中的重复计算，上一个窗口和新的窗口会有重叠部分, 重叠部分的值可以不用重复计算了. 第一个参数就是新的值, 第二个参数是旧的值. newWindow = oldWindow+(newIn-oldOut)

要有checkPoint

def reduceByKeyAndWindow(
    reduceFunc: (V, V) => V,
    invReduceFunc: (V, V) => V,
    windowDuration: Duration,
    slideDuration: Duration = self.slideDuration,
    numPartitions: Int = ssc.sc.defaultParallelism,
    filterFunc: ((K, V)) => Boolean = null
  ): DStream[(K, V)] 

def reduceByKeyAndWindow(
      reduceFunc: (V, V) => V,
      invReduceFunc: (V, V) => V,
      windowDuration: Duration,
      slideDuration: Duration,
      partitioner: Partitioner,
      filterFunc: ((K, V)) => Boolean
    ): DStream[(K, V)]

val value = stream
     .flatMap(_.split(" "))
     .map((_, 1))
     .reduceByKeyAndWindow(_ + _, _-_,Seconds(12),Seconds(6))
  .print

window

基于对源 DStream 窗化的批次进行计算返回一个新的 Dstream

def window(windowDuration: Duration, slideDuration: Duration): DStream[T] 

countByWindow

返回一个滑动窗口计数流中的元素的个数。

def countByWindow(
    windowDuration: Duration,
    slideDuration: Duration): DStream[Long]

countByValueAndWindow

对(K,V)对的DStream调用，返回(K,Long)对的新DStream，其中每个key的的对象的v是其在滑动窗口中频率

def countByValueAndWindow(
    windowDuration: Duration,
    slideDuration: Duration,
    numPartitions: Int = ssc.sc.defaultParallelism)
    (implicit ord: Ordering[T] = null)
    : DStream[(T, Long)]