package com.bjsxt.spark.actions;

import java.util.Arrays;

import java.util.Map;

import java.util.Map.Entry;

import org.apache.spark.SparkConf;

import org.apache.spark.api.java.JavaPairRDD;

import org.apache.spark.api.java.JavaRDD;

import org.apache.spark.api.java.JavaSparkContext;

import scala.Tuple2;

/**

* countByValue

* 根据数据集每个元素相同的内容来计数。返回相同内容的元素对应的条数。

*

* @author root

*

*/

public class Operator_countByValue {

public static void main(String[] args) {

SparkConf conf = new SparkConf();

conf.setMaster("local").setAppName("countByKey");

JavaSparkContext sc = new JavaSparkContext(conf);

JavaPairRDD<Integer, String> parallelizePairs = sc.parallelizePairs(Arrays.asList(

new Tuple2<Integer,String>(1,"a"),

new Tuple2<Integer,String>(2,"b"),

new Tuple2<Integer,String>(2,"c"),

new Tuple2<Integer,String>(3,"c"),

new Tuple2<Integer,String>(4,"d"),

new Tuple2<Integer,String>(4,"d")

));

Map<Tuple2<Integer, String>, Long> countByValue = parallelizePairs.countByValue();

for(Entry<Tuple2<Integer, String>, Long> entry : countByValue.entrySet()){

System.out.println("key:"+entry.getKey()+",value:"+entry.getValue());

}

}

}