004简单介绍WordCount，统计文本单词次数

MapReduce简介

1. MapReduce是一种分布式计算模型,主要解决海量数据的计算问题。
2. MR有两个阶段组成：Map和Reduce，用户只需实现map()和reduce()两个函数，即可实现分布式计算。

MapReduce的原理图

1. MR执行的流程

             2.MR原理图

• 根据代码简单了解MR。

package com.lj.MR;

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 WCMapper extends Mapper<LongWritable, Text, Text, IntWritable>  {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //super.map(key, value, context);
        String[] arr = value.toString().split(" ");
        Text keyOut = new Text();
        IntWritable valueOut = new IntWritable();
        for(String s :arr){
            keyOut.set(s);
            valueOut.set(1);
            try {
                context.write(keyOut,valueOut);
            } catch (InterruptedException e) {
                e.printStackTrace();
            }
        }
    }
}

package com.lj.MR;

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

import java.io.IOException;

public class WCReducce extends Reducer<Text, IntWritable, Text, IntWritable> {
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        //super.reduce(key, values, context);
        int count = 0;
        for(IntWritable iw:values){
             count = count + iw.get();
        }
        context.write(key,new IntWritable(count));
    }
}

package com.lj.MR;

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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.log4j.BasicConfigurator;

public class WCApp {
    public static void main(String[] args) {
        BasicConfigurator.configure();

        Configuration conf = new Configuration();        //此处为本地测试
        // conf.set("fs.defaultFS","file：///D://ItTools");
        try {
            //单例模式
            Job job = Job.getInstance(conf);
            //任务作业名字
            job.setJobName("WCApp");
            //搜索类
            job.setJarByClass(WCApp.class);
            //设置输入格式
            job.setInputFormatClass(TextInputFormat.class);

            FileInputFormat.addInputPath(job, new Path(args[0]));
            FileOutputFormat.setOutputPath(job, new Path(args[1]));

            job.setMapperClass(WCMapper.class);
            job.setReducerClass(WCReducce.class);

            job.setNumReduceTasks(1);
            job.setMapOutputKeyClass(Text.class);
            job.setMapOutputValueClass(IntWritable.class);

            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(IntWritable.class);
            job.waitForCompletion(false);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

• 代码简单解析：

根据执行流程图我们不难发现，首先我们从Mapper下手，然后着手Reducer,而Reducer的key(in),value(in)，肯定是Mapper的key(out),value(out)，否则我们不难发现，一定会类型不匹配，直接报错。

MAP：就是将原本文字转换成（k,v），其中k就是word，v就是单词的出现的次数

Shuffle：将相同的k排列一起

Reduce：将相同的k的v相加

原文地址：https://www.cnblogs.com/sirlijun/p/9588965.html