MapReduce自定义Reducer实现（第二步）

package com.imooc.bigdata.hadoop.mapreduce.wordcount;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;
import java.util.Iterator;

/*
 *  Mapper的输出是Reducer的输入
 *  Mapper<LongWritable, Text, Text, IntWritable>
 *  Reducer<Text, IntWritable, Text, IntWritable>
 *
 * (hello, 1)  (world, 1)
 * (hello, 1)  (world, 1)
 * (hello, 1)  (world, 1)
 * (welcome, 1)
 *
 *  map的输出到reduce端，是按照相同的key分发到一个reduce上执行的
 *  reduce1：(hello, 1)(hello, 1)(hello, 1)   -->(hello, <1,1,1>)
 *  reduce2：(world, 1)(world, 1)(world, 1)   -->(world, <1,1,1>)
 *  reduce3：(welcome, 1)   -->(welcome, <1>)
 *
 *  Reducer和Mapper中使用的设计模式是：模板
 */

public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable>{


    //实现一个reduce方法
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {

        int count = 0;
        Iterator<IntWritable> iterator = values.iterator();

        //<1,1,1>
        while (iterator.hasNext()){
            IntWritable value = iterator.next();
            count += value.get();
        }

        //将结果输出
        context.write(key,new IntWritable(count));
    }
}