reduceByKeyAndWindow实现基于滑动窗口的热点搜索词实时统计(Java版本)

package gh.spark.SparkStreaming;

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

 * 每隔5秒钟，统计最近20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

 *

 */

public class WindowDemo {
public static void main(String[] args) throws Exception {
SparkConf conf=new SparkConf()
              .setAppName("WindowDemo").setMaster("local[2]");
JavaStreamingContext jsc=new JavaStreamingContext(conf,Durations.seconds(5));

//从nc服务中读取输入的数据
JavaReceiverInputDStream<String> socketTextStream = 
jsc.socketTextStream("tgmaster", 9999);

/**
* 搜索的日志格式：name words，比如：张三  hello
* 我们通过map算子将搜索词取出
*/
JavaDStream<String> mapDStream = socketTextStream.map(new Function<String, String>() {
private static final long serialVersionUID = 1L;

public String call(String log) throws Exception {
// TODO Auto-generated method stub
return log.split(" ")[1];
}
});

// 将搜索词映射为(searchWord, 1)的tuple格式
JavaPairDStream<String, Integer> mapToPairDStream = mapDStream.mapToPair(new PairFunction<String, String, Integer>() {
private static final long serialVersionUID = 1L;

public Tuple2<String, Integer> call(String searchWord) throws Exception {
// TODO Auto-generated method stub
return new Tuple2<String, Integer>(searchWord,1);
}
});

/**
* 对滑动窗口进行reduceByKeyAndWindow操作
* 其中，窗口长度是20秒，滑动时间间隔是5秒
*/
JavaPairDStream<String, Integer> reduceByKeyAndWindowDStream = mapToPairDStream.reduceByKeyAndWindow(new Function2<Integer, Integer, Integer>() {

public Integer call(Integer v1, Integer v2) throws Exception {
// TODO Auto-generated method stub
return v1+v2;
}
}, Durations.seconds(20), Durations.seconds(5));

/**
* 获取前3名的搜索词
*/
JavaPairDStream<String, Integer> resultDStream = reduceByKeyAndWindowDStream.transformToPair(new Function<JavaPairRDD<String,Integer>, JavaPairRDD<String,Integer>>() {

private static final long serialVersionUID = 1L;

public JavaPairRDD<String, Integer> call(
JavaPairRDD<String, Integer> wordsRDD) throws Exception {

//通过mapToPair算子，将key与value互换位置 
JavaPairRDD<Integer, String> mapToPairRDD = wordsRDD.mapToPair(new PairFunction<Tuple2<String,Integer>, Integer, String>() {

private static final long serialVersionUID = 1L;

public Tuple2<Integer, String> call(
Tuple2<String, Integer> tuple) throws Exception {
//将key与value互换位置 
return new Tuple2<Integer, String>(tuple._2,tuple._1);
}
});

//根据key值进行降序排列
JavaPairRDD<Integer, String> sortByKeyRDD = mapToPairRDD.sortByKey(false);

// 然后再次执行反转，变成(searchWord, count)的这种格式
JavaPairRDD<String, Integer> wordcountRDD = sortByKeyRDD.mapToPair(new PairFunction<Tuple2<Integer,String>, String, Integer>() {

private static final long serialVersionUID = 1L;

public Tuple2<String, Integer> call(
Tuple2<Integer, String> tuple) throws Exception {

return new Tuple2<String, Integer>(tuple._2,tuple._1);
}
});

//获取降序排列之后的前3名
List<Tuple2<String, Integer>> result = wordcountRDD.take(3);
//遍历输出结果
for (Tuple2<String, Integer> info : result) {
System.out.println(info._1+"  "+info._2);
}

return wordsRDD;
}
});

resultDStream.print();

jsc.start();
jsc.awaitTermination();
jsc.close();
}

}