13.window滑动窗口以及热点搜索词滑动统计案例实战

window滑动窗口

Spark Streaming提供了滑动窗口操作的支持，从而让我们可以对一个滑动窗口内的数据执行计算操作。每次掉落在窗口内的RDD的数据，会被聚合起来执行计算操作，然后生成的RDD，会作为window DStream的一个RDD。比如下图中，就是对每三秒钟的数据执行一次滑动窗口计算，这3秒内的3个RDD会被聚合起来进行处理，然后过了两秒钟，又会对最近三秒内的数据执行滑动窗口计算。所以每个滑动窗口操作，都必须指定两个参数，窗口长度以及滑动间隔，而且这两个参数值都必须是batch间隔的整数倍。（Spark Streaming对滑动窗口的支持，是比Storm更加完善和强大的）

Transform

意义

window

对每个滑动窗口的数据执行自定义的计算

countByWindow

对每个滑动窗口的数据执行count操作

reduceByWindow

对每个滑动窗口的数据执行reduce操作

reduceByKeyAndWindow

对每个滑动窗口的数据执行reduceByKey操作

countByValueAndWindow

对每个滑动窗口的数据执行countByValue操作

案例：热点搜索词滑动统计，每隔10秒钟，统计最近60秒钟的搜索词的搜索频次，并打印出排名最靠前的3个搜索词以及出现次数

java版本：

package cn.spark.study.streaming;import java.util.List;import org.apache.spark.SparkConf;import org.apache.spark.api.java.JavaPairRDD;import org.apache.spark.api.java.function.Function;import org.apache.spark.api.java.function.Function2;import org.apache.spark.api.java.function.PairFunction;import org.apache.spark.streaming.Durations;import org.apache.spark.streaming.api.java.JavaDStream;import org.apache.spark.streaming.api.java.JavaPairDStream;import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;import org.apache.spark.streaming.api.java.JavaStreamingContext;import scala.Tuple2;/** * 基于滑动窗口的热点搜索词实时统计 * @author Administrator * */public class WindowHotWord {public static void main(String[] args) {SparkConf conf = new SparkConf().setMaster("local[2]").setAppName("WindowHotWord");  JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(1));// 说明一下，这里的搜索日志的格式// leo hello// tom worldJavaReceiverInputDStream<String> searchLogsDStream = jssc.socketTextStream("spark1", 9999);// 将搜索日志给转换成，只有一个搜索词，即可JavaDStream<String> searchWordsDStream = searchLogsDStream.map(new Function<String, String>() {private static final long serialVersionUID = 1L;@Overridepublic String call(String searchLog) throws Exception {return searchLog.split(" ")[1];}});// 将搜索词映射为(searchWord, 1)的tuple格式JavaPairDStream<String, Integer> searchWordPairDStream = searchWordsDStream.mapToPair(new PairFunction<String, String, Integer>() {private static final long serialVersionUID = 1L;@Overridepublic Tuple2<String, Integer> call(String searchWord)throws Exception {return new Tuple2<String, Integer>(searchWord, 1);}});// 针对(searchWord, 1)的tuple格式的DStream，执行reduceByKeyAndWindow，滑动窗口操作// 第二个参数，是窗口长度，这里是60秒// 第三个参数，是滑动间隔，这里是10秒// 也就是说，每隔10秒钟，将最近60秒的数据，作为一个窗口，进行内部的RDD的聚合，然后统一对一个RDD进行后续// 计算// 所以说，这里的意思，就是，之前的searchWordPairDStream为止，其实，都是不会立即进行计算的// 而是只是放在那里// 然后，等待我们的滑动间隔到了以后，10秒钟到了，会将之前60秒的RDD，因为一个batch间隔是，5秒，所以之前// 60秒，就有12个RDD，给聚合起来，然后，统一执行redcueByKey操作// 所以这里的reduceByKeyAndWindow，是针对每个窗口执行计算的，而不是针对某个DStream中的RDDJavaPairDStream<String, Integer> searchWordCountsDStream = searchWordPairDStream.reduceByKeyAndWindow(new Function2<Integer, Integer, Integer>() {private static final long serialVersionUID = 1L;@Overridepublic Integer call(Integer v1, Integer v2) throws Exception {return v1 + v2;}}, Durations.seconds(60), Durations.seconds(10));// 到这里为止，就已经可以做到，每隔10秒钟，出来，之前60秒的收集到的单词的统计次数// 执行transform操作，因为，一个窗口，就是一个60秒钟的数据，会变成一个RDD，然后，对这一个RDD// 根据每个搜索词出现的频率进行排序，然后获取排名前3的热点搜索词JavaPairDStream<String, Integer> finalDStream = searchWordCountsDStream.transformToPair(new Function<JavaPairRDD<String,Integer>, JavaPairRDD<String,Integer>>() {private static final long serialVersionUID = 1L;@Overridepublic JavaPairRDD<String, Integer> call(JavaPairRDD<String, Integer> searchWordCountsRDD) throws Exception {// 执行搜索词和出现频率的反转JavaPairRDD<Integer, String> countSearchWordsRDD = searchWordCountsRDD.mapToPair(new PairFunction<Tuple2<String,Integer>, Integer, String>() {private static final long serialVersionUID = 1L;@Overridepublic Tuple2<Integer, String> call(Tuple2<String, Integer> tuple)throws Exception {return new Tuple2<Integer, String>(tuple._2, tuple._1);}});// 然后执行降序排序JavaPairRDD<Integer, String> sortedCountSearchWordsRDD = countSearchWordsRDD.sortByKey(false);// 然后再次执行反转，变成(searchWord, count)的这种格式JavaPairRDD<String, Integer> sortedSearchWordCountsRDD = sortedCountSearchWordsRDD.mapToPair(new PairFunction<Tuple2<Integer,String>, String, Integer>() {private static final long serialVersionUID = 1L;@Overridepublic Tuple2<String, Integer> call(Tuple2<Integer, String> tuple)throws Exception {return new Tuple2<String, Integer>(tuple._2, tuple._1);}});// 然后用take()，获取排名前3的热点搜索词List<Tuple2<String, Integer>> hogSearchWordCounts = sortedSearchWordCountsRDD.take(3);for(Tuple2<String, Integer> wordCount : hogSearchWordCounts) {System.out.println(wordCount._1 + ": " + wordCount._2);  }return searchWordCountsRDD;}  });// 这个无关紧要，只是为了触发job的执行，所以必须有output操作finalDStream.print();jssc.start();jssc.awaitTermination();jssc.close();}}

scala版本：

package cn.spark.study.streamingimport org.apache.spark.SparkConfimport org.apache.spark.streaming.StreamingContextimport org.apache.spark.streaming.Seconds/** * @author Administrator */object WindowHotWord {    def main(args: Array[String]): Unit = {    val conf = new SparkConf()        .setMaster("local[2]")          .setAppName("WindowHotWord")    val ssc = new StreamingContext(conf, Seconds(1))        val searchLogsDStream = ssc.socketTextStream("spark1", 9999)      val searchWordsDStream = searchLogsDStream.map { _.split(" ")(1) }      val searchWordPairsDStream = searchWordsDStream.map { searchWord => (searchWord, 1) }      val searchWordCountsDSteram = searchWordPairsDStream.reduceByKeyAndWindow(        (v1: Int, v2: Int) => v1 + v2,         Seconds(60),         Seconds(10))              val finalDStream = searchWordCountsDSteram.transform(searchWordCountsRDD => {      val countSearchWordsRDD = searchWordCountsRDD.map(tuple => (tuple._2, tuple._1))        val sortedCountSearchWordsRDD = countSearchWordsRDD.sortByKey(false)        val sortedSearchWordCountsRDD = sortedCountSearchWordsRDD.map(tuple => (tuple._1, tuple._2))            val top3SearchWordCounts = sortedSearchWordCountsRDD.take(3)      for(tuple <- top3SearchWordCounts) {        println(tuple)      }            searchWordCountsRDD    })        finalDStream.print()        ssc.start()    ssc.awaitTermination()  }}

运行步骤：
1.启动nc 
nc -lk 9999
2.本地运行，直接在IDE中运行程序

运行结果：