hadoop学习笔记（九）：MapReduce程序的编写

一、MapReduce主要继承两个父类：

Map

protected void map(KEY key,VALUE value,Context context) throws IOException,InterruptedException{
}

Reduce

1 protected void reduce(KEY key,Iterable<VALUE> values,Context context) throws IOException,InterruptedException{
2 }

二、使用代码实现WordCount：

package com.laowang.mapreduce;

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

import java.io.IOException;
import java.util.StringTokenizer;

public class MR {
    /**
     * @author laowang
     * @version v1.0.0
     * @apiNote Mapper
     * @since 2018/4/27 10:44
     * <p>
     * KEYIN, VALUEIN, KEYOUT, VALUEOUT  输入key类型，输入value类型，输出KEY类型，输出value类型
     */
    static class MyMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
        private final static IntWritable ONE = new IntWritable(1);
        private Text word = new Text();

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            //获取每一行的数据
            String lineStr = value.toString();
            //以 空格、/t、/n、/r、/f 分割
            StringTokenizer stringTokenizer = new StringTokenizer(lineStr);
            //遍历
            while (stringTokenizer.hasMoreTokens()) {
                //获取截取后的每一个字符串
                String wordValue = stringTokenizer.nextToken();
                //拼接到word里面去
                word.set(wordValue);
                //写入到输出中
                context.write(word, ONE);
            }
        }
    }

    /**
     * @author laowang
     * @version v1.0.0
     * @apiNote Reducer
     * @since 2018/4/27 10:44
     * <p>
     * KEYIN, VALUEIN, KEYOUT, VALUEOUT  输入key类型，输入value类型，输出KEY类型，输出value类型
     */
    static class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
        private IntWritable result = new IntWritable();
        @Override
        protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable value : values) {
                sum += value.get();
            }
            result.set(sum);
            context.write(key, result);
        }
    }

    /**
     * @author laowang
     * @version v1.0.0
     * @apiNote Client
     * @since 2018/4/27 10:47
     */
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        //获取配置信息
        Configuration configuration = new Configuration();
        //创建job
        Job job = new Job(configuration,"wc");
        //设置JOB运行的类
        job.setJarByClass(MR.class);
        //设置Mapper和Reducer
        job.setMapperClass(MyMapper.class);
        job.setReducerClass(MyReducer.class);
        //设置输入和输出路径
        FileInputFormat.addInputPath(job,new Path(args[0]));
        FileOutputFormat.setOutputPath(job,new Path(args[1]));
        //设置输出key和value的类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        //提交job
        boolean b = job.waitForCompletion(true);
        //结束程序
        System.exit(b ? 0 : 1);
    }
}