作者：周志湖
本节主要内容
- Window Operation
- 入门案例
1. Window Operation
Spark Streaming提供窗口操作（Window Operation），如下图所示：
上图中，红色实线表示窗口当前的滑动位置，虚线表示前一次窗口位置，窗口每滑动一次，落在该窗口中的RDD被一起同时处理，生成一个窗口DStream（windowed DStream），窗口操作需要设置两个参数：
（1）窗口长度（window length），即窗口的持续时间，上图中的窗口长度为3
（2）滑动间隔（sliding interval），窗口操作执行的时间间隔，上图中的滑动间隔为2
这两个参数必须是原始DStream 批处理间隔（batch interval）的整数倍（上图中的原始DStream的batch interval为1）
2. 入门案例
- WindowWordCount——reduceByKeyAndWindow方法使用
import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._
object WindowWordCount {
def main(args: Array[String]) {
//传入的参数为localhost 9999 30 10
if (args.length != 4) {
System.err.println("Usage: WindowWorldCount <hostname> <port> <windowDuration> <slideDuration>")
System.exit(1)
}
StreamingExamples.setStreamingLogLevels()
val conf = new SparkConf().setAppName("WindowWordCount").setMaster("local[4]")
val sc = new SparkContext(conf)
// 创建StreamingContext，batch interval为5秒
val ssc = new StreamingContext(sc, Seconds(5))
//Socket为数据源
val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_ONLY_SER)
val words = lines.flatMap(_.split(" "))
// windows操作，对窗口中的单词进行计数
val wordCounts = words.map(x => (x , 1)).reduceByKeyAndWindow((a:Int,b:Int) => (a + b), Seconds(args(2).toInt), Seconds(args(3).toInt))
wordCounts.print()
ssc.start()
ssc.awaitTermination()
}
}通过下列代码启动netcat server
root@sparkmaster:~# nc -lk 9999再运行WindowWordCount
输入下列语句
root@sparkmaster:~# nc -lk 9999
Spark is a fast and general cluster computing system for Big Data. It provides观察执行情况：
-------------------------------------------
Time: 1448778805000 ms（10秒，第一个滑动窗口时间）
-------------------------------------------
(provides,1)
(is,1)
(general,1)
(Big,1)
(fast,1)
(cluster,1)
(Data.,1)
(computing,1)
(Spark,1)
(a,1)
...
-------------------------------------------
Time: 1448778815000 ms（10秒后，第二个滑动窗口时间）
-------------------------------------------
(provides,1)
(is,1)
(general,1)
(Big,1)
(fast,1)
(cluster,1)
(Data.,1)
(computing,1)
(Spark,1)
(a,1)
...
-------------------------------------------
Time: 1448778825000 ms（10秒后，第三个滑动窗口时间）
-------------------------------------------
(provides,1)
(is,1)
(general,1)
(Big,1)
(fast,1)
(cluster,1)
(Data.,1)
(computing,1)
(Spark,1)
(a,1)
...
-------------------------------------------
Time: 1448778835000 ms（再经10秒后，超出window length窗口长度，不在计数范围内）
-------------------------------------------
-------------------------------------------
Time: 1448778845000 ms
-------------------------------------------同样的语句输入两次
root@sparkmaster:~# nc -lk 9999
Spark is a fast and general cluster computing system for Big Data. It provides
Spark is a fast and general cluster computing system for Big Data. It provides
Spark is a fast and general cluster computing system for Big Data. It provides观察执行结果如下：
Time: 1448779205000 ms
-------------------------------------------
(provides,2)
(is,2)
(general,2)
(Big,2)
(fast,2)
(cluster,2)
(Data.,2)
(computing,2)
(Spark,2)
(a,2)
...再输入一次
root@sparkmaster:~# nc -lk 9999
Spark is a fast and general cluster computing system for Big Data. It provides
Spark is a fast and general cluster computing system for Big Data. It provides
Spark is a fast and general cluster computing system for Big Data. It provides
Spark is a fast and general cluster computing system for Big Data. It provides计算结果如下：
-------------------------------------------
Time: 1448779215000 ms
-------------------------------------------
(provides,3)
(is,3)
(general,3)
(Big,3)
(fast,3)
(cluster,3)
(Data.,3)
(computing,3)
(Spark,3)
(a,3)
...再输入一次
root@sparkmaster:~# nc -lk 9999
Spark is a fast and general cluster computing system for Big Data. It provides
Spark is a fast and general cluster computing system for Big Data. It provides
Spark is a fast and general cluster computing system for Big Data. It provides
Spark is a fast and general cluster computing system for Big Data. It provides
Spark is a fast and general cluster computing system for Big Data. It provides计算结果如下：
-------------------------------------------
Time: 1448779225000 ms
-------------------------------------------
(provides,4)
(is,4)
(general,4)
(Big,4)
(fast,4)
(cluster,4)
(Data.,4)
(computing,4)
(Spark,4)
(a,4)
...
-------------------------------------------
Time: 1448779235000 ms
-------------------------------------------
(provides,2)
(is,2)
(general,2)
(Big,2)
(fast,2)
(cluster,2)
(Data.,2)
(computing,2)
(Spark,2)
(a,2)
...
-------------------------------------------
Time: 1448779245000 ms
-------------------------------------------
(provides,1)
(is,1)
(general,1)
(Big,1)
(fast,1)
(cluster,1)
(Data.,1)
(computing,1)
(Spark,1)
(a,1)
...
-------------------------------------------
Time: 1448779255000 ms
-------------------------------------------
-------------------------------------------
Time: 1448779265000 ms
-------------------------------------------2 WindowWordCount——countByWindow方法使用
import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._
object WindowWordCount {
def main(args: Array[String]) {
if (args.length != 4) {
System.err.println("Usage: WindowWorldCount <hostname> <port> <windowDuration> <slideDuration>")
System.exit(1)
}
StreamingExamples.setStreamingLogLevels()
val conf = new SparkConf().setAppName("WindowWordCount").setMaster("local[2]")
val sc = new SparkContext(conf)
// 创建StreamingContext
val ssc = new StreamingContext(sc, Seconds(5))
// 定义checkpoint目录为当前目录
ssc.checkpoint(".")
val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_ONLY_SER)
val words = lines.flatMap(_.split(" "))
//countByWindowcountByWindow方法计算基于滑动窗口的DStream中的元素的数量。
val countByWindow=words.countByWindow(Seconds(args(2).toInt), Seconds(args(3).toInt))
countByWindow.print()
ssc.start()
ssc.awaitTermination()
}
}启动
root@sparkmaster:~# nc -lk 9999然后运行WindowWordCount
输入
root@sparkmaster:~# nc -lk 9999
Spark is a fast and general cluster computing system for Big Data察看运行结果：
-------------------------------------------
Time: 1448780625000 ms
-------------------------------------------
0
-------------------------------------------
Time: 1448780635000 ms
-------------------------------------------
12
-------------------------------------------
Time: 1448780645000 ms
-------------------------------------------
12
-------------------------------------------
Time: 1448780655000 ms
-------------------------------------------
12
-------------------------------------------
Time: 1448780665000 ms
-------------------------------------------
0
-------------------------------------------
Time: 1448780675000 ms
-------------------------------------------
03 WindowWordCount——reduceByWindow方法使用
//reduceByWindow方法基于滑动窗口对源DStream中的元素进行聚合操作，返回包含单元素的一个新的DStream。
val reduceByWindow=words.map(x=>1).reduceByWindow(_+_,_-_Seconds(args(2).toInt), Seconds(args(3).toInt))上面的例子其实是countByWindow的实现，可以在countByWindow源码实现中得到验证
def countByWindow(
windowDuration: Duration,
slideDuration: Duration): DStream[Long] = ssc.withScope {
this.map(_ => 1L).reduceByWindow(_ + _, _ - _, windowDuration, slideDuration)
}而reduceByWindow又是通过reduceByKeyAndWindow方法来实现的，具体代码如下
def reduceByWindow(
reduceFunc: (T, T) => T,
invReduceFunc: (T, T) => T,
windowDuration: Duration,
slideDuration: Duration
): DStream[T] = ssc.withScope {
this.map(x => (1, x))
.reduceByKeyAndWindow(reduceFunc, invReduceFunc, windowDuration, slideDuration, 1)
.map(_._2)
}与前面的例子中的reduceByKeyAndWindow方法不同的是这里的reduceByKeyAndWindow方法多了一个invReduceFunc参数，方法完整源码如下：
/**
* Return a new DStream by applying incremental `reduceByKey` over a sliding window.
* The reduced value of over a new window is calculated using the old window's reduced value :
* 1. reduce the new values that entered the window (e.g., adding new counts)
*
* 2. "inverse reduce" the old values that left the window (e.g., subtracting old counts)
*
* This is more efficient than reduceByKeyAndWindow without "inverse reduce" function.
* However, it is applicable to only "invertible reduce functions".
* Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
* @param reduceFunc associative reduce function
* @param invReduceFunc inverse reduce function
* @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 filterFunc Optional function to filter expired key-value pairs;
* only pairs that satisfy the function are retained
*/
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)] = ssc.withScope {
reduceByKeyAndWindow(
reduceFunc, invReduceFunc, windowDuration,
slideDuration, defaultPartitioner(numPartitions), filterFunc
)
}具体来讲，下面两个方法得到的结果是一样的，只是效率不同，后面的方法方式效率更高：
//以过去5秒钟为一个输入窗口，每1秒统计一下WordCount，本方法会将过去5秒钟的每一秒钟的WordCount都进行统计
//然后进行叠加，得出这个窗口中的单词统计。 这种方式被称为叠加方式，如下图左边所示
val wordCounts = words.map(x => (x, 1)).reduceByKeyAndWindow(_ + _, Seconds(5s)，seconds(1))与
//计算t+4秒这个时刻过去5秒窗口的WordCount，可以将t+3时刻过去5秒的统计量加上[t+3，t+4]的统计量
//再减去[t-2，t-1]的统计量，这种方法可以复用中间三秒的统计量，提高统计的效率。 这种方式被称为增量方式，如下图的右边所示
val wordCounts = words.map(x => (x, 1)).reduceByKeyAndWindow(_ + _, _ - _, Seconds(5s)，seconds(1))
DStream支持的全部Window操作方法如下：
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