Combiner 合并 知识点 案例

一、概述

1、Combiner是MR程序中Mapper和Reducer之外的一种组件

2、Combiner继承Reducer

3、Combiner在每个Map Task的节点上运行， Reducer接收全局的Mapper结果

4、Combiner对每个Map Task的输出进行局部汇总，减少网络传输

5、并不是所有的运算，都可以使用局部汇总，如求平均值

二、自定义Combiner类

1、继承Reducer，重写reduce方法

2、在driver中设置job的Combiner驱动

3、Combiner的输入kv 与 Mapper的输出 kv 一致， Combiner的输出kv 与 Reducer的输入 kv一致

4、reduce()的作用是局部统计Map Task的输出结果与Mapper的语法相似

三、WordCount

1、Mapper

package com.wordcount;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
    Text k = new Text();
    IntWritable v = new IntWritable(1);
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        // 1. 读取行
        String line = value.toString();
        // 2. 切割
        String[] words = line.split("\s");
        // 3. 循环写入
        for (String word : words) {
            k.set(word);
            context.write(k, v);
        }
    }
}

2、Combiner

package com.wordcount;

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

import java.io.IOException;

public class WordCountCombiner extends Reducer<Text, IntWritable,Text,IntWritable> {
    IntWritable v = new IntWritable();
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int sum = 0;
        // 1.累加
        for (IntWritable value : values) {
            sum += value.get();
        }
        v.set(sum);
        // 2.写入
        context.write(key, v);
    }
}

3、Reducer

package com.wordcount;

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

import java.io.IOException;

public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    IntWritable v = new IntWritable();
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        // 1. 累加
        int sum = 0;
        for (IntWritable value : values) {
            sum += value.get();
        }
        v.set(sum);
        // 2. 写入
        context.write(key, v);
    }
}

4、Driver

package com.wordcount;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

public class WordCountDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        args = new String[]{"E:\a\inputFile\test.txt", "E:\a\output3"};
        // 1.job
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        // 2.设置jar
        job.setJarByClass(WordCountDriver.class);
        // 3.关联mapper和reducer
        job.setMapperClass(WordCountMapper.class);
        job.setReducerClass(WordCountReducer.class);
        // 4.设置mapper输出的 k, v
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        // 5.设置输出结果的k, v
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        // 7.设置 Combiner 类
        job.setCombinerClass(WordCountCombiner.class);
        // 6.设置文件的输入输出值
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        // 7.提交任务
        boolean wait = job.waitForCompletion(true);
        System.exit(wait? 0: 1);
    }
}

 注意：

因为Combiner和Reducer的代码逻辑一样

因此在Driver中添加下面内容即可

job.setCombinerClass(WordCountReducer.class);