WordCount

 Map过程需要继承org.apache.hadoop.mapreduce包中Mapper类，并重写其 map方法。通过在map方法中添加两句把key值和value值输出到控制台的代码，可以发现map方法中value值存储的是文本文件中的一行（以回 车符为行结束标记），而key值为该行的首字母相对于文本文件的首地址的偏移量。然后StringTokenizer类将每一行拆分成为一个个的单词，并 将作为map方法的结果输出，其余的工作都交有MapReduce框架处理。

       Reduce过程需要继承org.apache.hadoop.mapreduce包中Reducer类，并重写其reduce方法。Map过程输出中key为单个单词，而values是对应单词的计数值所组成的列表，Map的输出就是Reduce的输入，所以reduce方法只要遍历values并求和，即可得到某个单词的总次数。

       在MapReduce中，由Job对象负责管理和运行一个计算任务，并通过Job的一些方法对任务的参数进行相关的设置。此处设置了使用 TokenizerMapper完成Map过程中的处理和使用IntSumReducer完成Combine和Reduce过程中的处理。还设置了Map 过程和Reduce过程的输出类型：key的类型为Text，value的类型为IntWritable。任务的输出和输入路径则由命令行参数指定，并由FileInputFormat和FileOutputFormat分别设定。完成相应任务的参数设定后，即可调用job.waitForCompletion()方法执行任务。

源代码如下：

package com.hadoop.test1;

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

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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;

public class WordCount {
    public static class TokenizerMapper extends
            Mapper {
        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();

        protected void map(Object key, Text value, Context context)
                throws IOException, InterruptedException {
            StringTokenizer tokenizer = new StringTokenizer(value.toString());
            while (tokenizer.hasMoreTokens()) {
                word.set(tokenizer.nextToken());
                context.write(word, one);
            }
        }
    }

    public static class IntSumReducer extends
            Reducer {
        private IntWritable result = new IntWritable();

        protected void reduce(Text key, Iterator values,
                Context context) throws IOException, InterruptedException {
            int sum = 0;
            while (values.hasNext()) {
                sum += values.next().get();
            }
            result.set(sum);
            context.write(key, result);
        }
    }

    public static void main(String[] args) throws IOException,
            InterruptedException, ClassNotFoundException {

        // section 1
        Configuration conf = new Configuration();
        String[] otherArgs = new GenericOptionsParser(conf, args)
                .getRemainingArgs();
        if (otherArgs.length != 2) {
            System.err.println("Usage : wordcount ");
            System.exit(2);
        }
        Job job = new Job(conf, "wordcount");
        job.setJarByClass(WordCount.class);

        // section2
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        // section3
        job.setMapperClass(TokenizerMapper.class);
        job.setCombinerClass(IntSumReducer.class);

        // section4
        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));

        // section5
        System.exit(job.waitForCompletion(true) ? 0 : 1);

    }
}