SparkStreaming之窗口函数

WindowOperations（窗口操作）

         Spark还提供了窗口的计算，它允许你使用一个滑动窗口应用在数据变换中。下图说明了该滑动窗口。

如图所示，每个时间窗口在一个个DStream中划过，每个DSteam中的RDD进入Window中进行合并，操作时生成为

窗口化DSteam的RDD。在上图中，该操作被应用在过去的3个时间单位的数据，和划过了2个时间单位。这说明任

何窗口操作都需要指定2个参数。

1. window length（窗口长度）：窗口的持续时间（上图为3个时间单位）
2. sliding interval （滑动间隔）- 窗口操作的时间间隔（上图为2个时间单位）。

上面的2个参数的大小，必须是接受产生一个DStream时间的倍数

让我们用一个例子来说明窗口操作。比如说，你想用以前的WordCount的例子，来计算最近30s的数据的中的单词

数，10S接受为一个DStream。为此，我们要用reduceByKey操作来计算最近30s数据中每一个DSteam中关于

（word，1）的pair操作。它可以用reduceByKeyAndWindow操作来实现。一些常见的窗口操作如下。所有这些操作

都需要两个参数— window length（窗口长度）和sliding interval（滑动间隔）。

————————-实验数据———————————————————————-

spark
Streaming
better
than
storm
you
need
it
yes
do
it

（每秒在其中随机抽取一个，作为Socket端的输入），socket端的数据模拟和实验函数等程序见附录百度云链接

———————————————–window操作————————————————————————-

  
  
//输入:窗口长度（隐：输入的滑动窗口长度为形成Dstream的时间）
//输出：返回一个DStream,這个DStream包含這个滑动窗口下的全部元素
def window(windowDuration: Duration): DStream[T] = window(windowDuration, this.slideDuration)

//输入:窗口长度和滑动窗口长度
//输出：返回一个DStream,這个DStream包含這个滑动窗口下的全部元素
def window(windowDuration: Duration, slideDuration: Duration): DStream[T] = ssc.withScope {
new WindowedDStream( this, windowDuration, slideDuration)
}
  
  

  
  
import org.apache.log4j.{Level, Logger}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}

object windowOnStreaming {
def main(args: Array[String]) {
/**
* this is test of Streaming operations-----window
*/
Logger.getLogger( "org.apache.spark").setLevel(Level.ERROR)
Logger.getLogger( "org.eclipse.jetty.Server").setLevel(Level.OFF)

val conf = new SparkConf().setAppName( "the Window operation of SparK Streaming").setMaster( "local[2]")
val sc = new SparkContext(conf)
val ssc = new StreamingContext(sc,Seconds( 2))


//set the Checkpoint directory
ssc.checkpoint( "/Res")

//get the socket Streaming data
val socketStreaming = ssc.socketTextStream( "master", 9999)

val data = socketStreaming.map(x =>(x, 1))
//def window(windowDuration: Duration): DStream[T]
val getedData1 = data.window(Seconds( 6))
println( "windowDuration only : ")
getedData1.print()
//same as
// def window(windowDuration: Duration, slideDuration: Duration): DStream[T]
//val getedData2 = data.window(Seconds(9),Seconds(3))
//println("Duration and SlideDuration : ")
//getedData2.print()

ssc.start()
ssc.awaitTermination()
}

}
  
  

——————–reduceByKeyAndWindow操作——————————–

  
  
/**通过对每个滑动过来的窗口应用一个reduceByKey的操作，返回一个DSream，有点像
* `DStream.reduceByKey(),但是只是這个函数只是应用在滑动过来的窗口，hash分区是采用spark集群
* 默认的分区树
* @param reduceFunc 从左到右的reduce 函数
* @param windowDuration 窗口时间
* 滑动窗口默认是1个batch interval
* 分区数是是RDD默认（depend on spark集群core）
*/
def reduceByKeyAndWindow(
reduceFunc: (V, V) => V,
windowDuration: Duration
): DStream[(K, V)] = ssc.withScope {
reduceByKeyAndWindow(reduceFunc, windowDuration, self.slideDuration, defaultPartitioner())
}

/**通过对每个滑动过来的窗口应用一个reduceByKey的操作，返回一个DSream，有点像
* `DStream.reduceByKey(),但是只是這个函数只是应用在滑动过来的窗口，hash分区是采用spark集群
* 默认的分区树
* @param reduceFunc 从左到右的reduce 函数
* @param windowDuration 窗口时间
* @param slideDuration 滑动时间
*/
def reduceByKeyAndWindow(
reduceFunc: (V, V) => V,
windowDuration: Duration,
slideDuration: Duration
): DStream[(K, V)] = ssc.withScope {
reduceByKeyAndWindow(reduceFunc, windowDuration, slideDuration, defaultPartitioner())
}


/**通过对每个滑动过来的窗口应用一个reduceByKey的操作，返回一个DSream，有点像
* `DStream.reduceByKey(),但是只是這个函数只是应用在滑动过来的窗口，hash分区是采用spark集群
* 默认的分区树
* @param reduceFunc 从左到右的reduce 函数
* @param windowDuration 窗口时间
* @param slideDuration 滑动时间

* @param numPartitions 每个RDD的分区数.
*/
def reduceByKeyAndWindow(
reduceFunc: (V, V) => V,
windowDuration: Duration,
slideDuration: Duration,
numPartitions: Int
): DStream[(K, V)] = ssc.withScope {
reduceByKeyAndWindow(reduceFunc, windowDuration, slideDuration,
defaultPartitioner(numPartitions))
}

/**
/**通过对每个滑动过来的窗口应用一个reduceByKey的操作，返回一个DSream，有点像
* `DStream.reduceByKey(),但是只是這个函数只是应用在滑动过来的窗口，hash分区是采用spark集群
* 默认的分区树
* @param reduceFunc 从左到右的reduce 函数
* @param windowDuration 窗口时间
* @param slideDuration 滑动时间

* @param numPartitions 每个RDD的分区数.
* @param partitioner 设置每个partition的分区数
*/
def reduceByKeyAndWindow(
reduceFunc: (V, V) => V,
windowDuration: Duration,
slideDuration: Duration,
partitioner: Partitioner
): DStream[(K, V)] = ssc.withScope {
self.reduceByKey(reduceFunc, partitioner)
.window(windowDuration, slideDuration)
.reduceByKey(reduceFunc, partitioner)
}

/**
*通过对每个滑动过来的窗口应用一个reduceByKey的操作.同时对old RDDs进行了invReduceFunc操作
* hash分区是采用spark集群，默认的分区树
* @param reduceFunc从左到右的reduce 函数
* @param invReduceFunc inverse reduce function; such that for all y, invertible x:
* `invReduceFunc(reduceFunc(x, y), x) = y`
* @param windowDuration窗口时间
* @param slideDuration 滑动时间
* @param filterFunc 来赛选一定条件的 key-value 对的
*/
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 =
): DStream[(K, V)] = ssc.withScope {
reduceByKeyAndWindow(
reduceFunc, invReduceFunc, windowDuration,
slideDuration, defaultPartitioner(numPartitions), filterFunc
)
}

/**
*通过对每个滑动过来的窗口应用一个reduceByKey的操作.同时对old RDDs进行了invReduceFunc操作
* hash分区是采用spark集群，默认的分区树
* @param reduceFunc从左到右的reduce 函数
* @param invReduceFunc inverse reduce function; such that for all y, invertible x:
* `invReduceFunc(reduceFunc(x, y), x) = y`
* @param windowDuration窗口时间
* @param slideDuration 滑动时间
* @param partitioner 每个RDD的分区数.
* @param filterFunc 来赛选一定条件的 key-value 对的
*/
def reduceByKeyAndWindow(
reduceFunc: (V, V) => V,
invReduceFunc: (V, V) => V,
windowDuration: Duration,
slideDuration: Duration,
partitioner: Partitioner,
filterFunc: ((K, V)) => Boolean
): DStream[(K, V)] = ssc.withScope {

val cleanedReduceFunc = ssc.sc.clean(reduceFunc)
val cleanedInvReduceFunc = ssc.sc.clean(invReduceFunc)
val cleanedFilterFunc = if (filterFunc != ) Some(ssc.sc.clean(filterFunc)) else None
new ReducedWindowedDStream[K, V](
self, cleanedReduceFunc, cleanedInvReduceFunc, cleanedFilterFunc,
windowDuration, slideDuration, partitioner
)
}
  
  

  
  
import org.apache.log4j.{Level, Logger}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}


object reduceByWindowOnStreaming {

def main(args: Array[String]) {
/**
* this is test of Streaming operations-----reduceByKeyAndWindow
*/
Logger.getLogger( "org.apache.spark").setLevel(Level.ERROR)
Logger.getLogger( "org.eclipse.jetty.Server").setLevel(Level.OFF)

val conf = new SparkConf().setAppName( "the reduceByWindow operation of SparK Streaming").setMaster( "local[2]")
val sc = new SparkContext(conf)
val ssc = new StreamingContext(sc,Seconds( 2))

//set the Checkpoint directory
ssc.checkpoint( "/Res")

//get the socket Streaming data
val socketStreaming = ssc.socketTextStream( "master", 9999)

val data = socketStreaming.map(x =>(x, 1))
//def reduceByKeyAndWindow(reduceFunc: (V, V) => V, windowDuration: Duration ): DStream[(K, V)]
//val getedData1 = data.reduceByKeyAndWindow(_+_,Seconds(6))

val getedData2 = data.reduceByKeyAndWindow(_+_,
(a,b) => a+b* 0
,Seconds( 6),Seconds( 2))

val getedData1 = data.reduceByKeyAndWindow(_+_,_-_,Seconds( 9),Seconds( 6))

println( "reduceByKeyAndWindow : ")
getedData1.print()

ssc.start()
ssc.awaitTermination()


}
}
  
  

這里出现了invReduceFunc函数這个函数有点特别，一不注意就会出错，现在通过分析源码中的

ReducedWindowedDStream這个类内部来进行说明：

——————reduceByWindow操作—————————

  
  
/输入：reduceFunc、窗口长度、滑动长度
//输出：（a,b）为从几个从左到右一次取得两个元素
//（，a,b）进入reduceFunc,
def reduceByWindow(
reduceFunc: (T, T) => T,
windowDuration: Duration,
slideDuration: Duration
): DStream[T] = ssc.withScope {
this.reduce(reduceFunc).window(windowDuration, slideDuration).reduce(reduceFunc)
}
/**
*输入reduceFunc，invReduceFunc，窗口长度、滑动长度
*/
def reduceByWindow(
reduceFunc: (T, T) => T,
invReduceFunc: (T, T) => T,
windowDuration: Duration,
slideDuration: Duration
): DStream[T] = ssc.withScope {
this.map(( 1, _))
.reduceByKeyAndWindow(reduceFunc, invReduceFunc, windowDuration, slideDuration, 1)
.map(_._2)
}
  
  

  
  
import org.apache.log4j.{Level, Logger}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}

/**
* Created by root on 6/23/16.
*/
object reduceByWindow {
def main(args: Array[String]) {
/**
* this is test of Streaming operations-----reduceByWindow
*/
Logger.getLogger( "org.apache.spark").setLevel(Level.ERROR)
Logger.getLogger( "org.eclipse.jetty.Server").setLevel(Level.OFF)

val conf = new SparkConf().setAppName( "the reduceByWindow operation of SparK Streaming").setMaster( "local[2]")
val sc = new SparkContext(conf)
val ssc = new StreamingContext(sc,Seconds( 2))
//set the Checkpoint directory
ssc.checkpoint( "/Res")

//get the socket Streaming data
val socketStreaming = ssc.socketTextStream( "master", 9999)

//val data = socketStreaming.reduceByWindow(_+_,Seconds(6),Seconds(2))
val data = socketStreaming.reduceByWindow(_+_,_+_,Seconds( 6),Seconds( 2))


println( "reduceByWindow: count the number of elements")
data.print()


ssc.start()
ssc.awaitTermination()

}
}
  
  

———————————————–countByWindow操作———————————

  
  
/**
* 输入 窗口长度和滑动长度，返回窗口内的元素数量
* @param windowDuration 窗口长度
* @param slideDuration 滑动长度
*/
def countByWindow(
windowDuration: Duration,
slideDuration: Duration): DStream[Long] = ssc.withScope {
this.map(_ => 1L).reduceByWindow(_ + _, _ - _, windowDuration, slideDuration)
//窗口下的DStream进行map操作，把每个元素变为1之后进行reduceByWindow操作
}
  
  

  
  
import org.apache.log4j.{Level, Logger}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}

/
* Created by root on 6/23/16.
/
object countByWindow {
def main(args: Array[String]) {

/
this is test of Streaming operations-----countByWindow
*/
Logger.getLogger( "org.apache.spark").setLevel(Level.ERROR)
Logger.getLogger( "org.eclipse.jetty.Server").setLevel(Level.OFF)

val conf = new SparkConf().setAppName( "the reduceByWindow operation of SparK Streaming").setMaster( "local[2]")
val sc = new SparkContext(conf)
val ssc = new StreamingContext(sc,Seconds( 2))
//set the Checkpoint directory
ssc.checkpoint( "/Res")

//get the socket Streaming data
val socketStreaming = ssc.socketTextStream( "master", 9999)

val data = socketStreaming.countByWindow(Seconds( 6),Seconds( 2))


println( "countByWindow: count the number of elements")
data.print()


ssc.start()
ssc.awaitTermination()


}
}
  
  

——————————– countByValueAndWindow————-

  
  
/
输入 窗口长度、滑动时间、RDD分区数（默认分区是等于并行度）
@param windowDuration width of the window; must be a multiple of this DStream's
* batching interval
* @param slideDuration sliding interval of the window (i.e., the interval after which
* the new DStream will generate RDDs); must be a multiple of this
* DStream's batching interval
* @param numPartitions number of partitions of each RDD in the new DStream.
/
def countByValueAndWindow(
windowDuration: Duration,
slideDuration: Duration,
numPartitions: Int = ssc.sc.defaultParallelism)
(implicit ord: Ordering[T] = )
: DStream[ (T, Long)] = ssc.withScope {
this.map((_, 1L)).reduceByKeyAndWindow(
(x: Long, y: Long) => x + y,
(x: Long, y: Long) => x - y,
windowDuration,
slideDuration,
numPartitions,
(x: (T, Long)) => x._2 != 0L
)
}
  
  

  
  
import org.apache.log4j.{Level, Logger}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}

/
Created by root on 6/23/16.
/
object countByValueAndWindow {
def main(args: Array[String]) {
/*
* this is test of Streaming operations-----countByValueAndWindow
*/
Logger.getLogger( "org.apache.spark").setLevel(Level.ERROR)
Logger.getLogger( "org.eclipse.jetty.Server").setLevel(Level.OFF)

val conf = new SparkConf().setAppName( "the reduceByWindow operation of SparK Streaming").setMaster( "local[2]")
val sc = new SparkContext(conf)
val ssc = new StreamingContext(sc,Seconds( 2))
//set the Checkpoint directory
ssc.checkpoint( "/Res")

//get the socket Streaming data
val socketStreaming = ssc.socketTextStream( "master", 9999)

val data = socketStreaming.countByValueAndWindow(Seconds( 6),Seconds( 2))


println( "countByWindow: count the number of elements")
data.print()


ssc.start()
ssc.awaitTermination()
}

}
  
  

附录

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