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 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 mapDStream = socketTextStream.map(new Function() {
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 mapToPairDStream = mapDStream.mapToPair(new PairFunction() {
private static final long serialVersionUID = 1L;


public Tuple2 call(String searchWord) throws Exception {
// TODO Auto-generated method stub
return new Tuple2(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 reduceByKeyAndWindowDStream = mapToPairDStream.reduceByKeyAndWindow(new Function2() {

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 resultDStream = reduceByKeyAndWindowDStream.transformToPair(new Function, JavaPairRDD>() {


private static final long serialVersionUID = 1L;


public JavaPairRDD call(
JavaPairRDD wordsRDD) throws Exception {

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


private static final long serialVersionUID = 1L;


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

//根据key值进行降序排列
JavaPairRDD sortByKeyRDD = mapToPairRDD.sortByKey(false);

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


private static final long serialVersionUID = 1L;


public Tuple2 call(
Tuple2 tuple) throws Exception {

return new Tuple2(tuple._2,tuple._1);
}
});

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

return wordsRDD;
}
});

resultDStream.print();

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