编写一个简单的MapReduce程序大体上需要如下3步：

1）实现Mapper，处理输入的对，输出中间结果；

2）实现Reducer，对中间结果进行运算，输出最终结果；

3）在main方法里定义运行作业，定义一个job，在这里控制job如何运行等。

本文将通过一个实例（字数统计）演示MapReduce基本编程。

0 导入Hadoop的jar包

导入hadoop目录下和lib目录下的jar包

1 编写Mapper类

package cn.com.yz.mapreduce;

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

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

public class WordCountMapper extends Mapper<Object, Text, Text, IntWritable> {
	// --------------------------------------------------------------------
	private final static IntWritable one = new IntWritable(1); 	// initial word number is 1
	private Text word = new Text(); // word
	// --------------------------------------------------------------------
	public void map(Object key, Text value, Context context)
			throws IOException, InterruptedException {
		StringTokenizer itr = new StringTokenizer(value.toString());
		while (itr.hasMoreTokens()) {
			word.set(itr.nextToken());
			context.write(word, one);
		} // end while
	} // end map()
} // end class WordCountMapper

2 编写Reduce类

       Reducer抽象类的四个形式参数类型指定了reduce函数的输入和输出类型。在本例子中，输入键是单词，输入值是单词出现的次数，将单词出现的次数进行叠加，输出单词和单词总数。

package cn.com.yz.mapreduce;

import java.io.IOException;

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

public class WordCountReducer extends
		Reducer<Text, IntWritable, Text, IntWritable> {
	// --------------------------------------------------------------------
	private IntWritable result = new IntWritable();
	// --------------------------------------------------------------------
	public void reduce(Text key, Iterable<IntWritable> values, Context context)
			throws IOException, InterruptedException {
		int sum = 0;
		
		for (IntWritable val : values) {
			sum += val.get();
		} //end for
		result.set(sum);
		context.write(key, result);
	} //end reduce()
} //end class WordCountReducer

3 编写Main方法

package cn.com.yz.mapreduce;

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 org.apache.hadoop.util.GenericOptionsParser;

public class WordCount {

	public static void main(String[] args) throws Exception {
		Configuration conf = new Configuration();

		String[] otherArgs = new GenericOptionsParser(conf, args)
				.getRemainingArgs();
		if (otherArgs.length != 2) {
			System.err.println("Usage: wordcount <in> <out>");
			System.exit(2);
		}// end if

		// set job 
		Job job = new Job(conf, "word count");
		job.setJarByClass(WordCount.class);
		job.setMapperClass(WordCountMapper.class);
		job.setCombinerClass(WordCountReducer.class);
		job.setReducerClass(WordCountReducer.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);

		// set input and output path
		FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
		FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
        
		//submit job and wait for fininshing
		System.exit(job.waitForCompletion(true) ? 0 : 1);
	}// end main()
} // end class WordCount

      Hadoop的复杂在于job的配置有着复杂的属性参数，如文件分割策略、排序策略、map输出内存缓冲区的大小、工作线程数量等，深入理解掌握这些参数才能使自己的MapReduce程序在集群环境中运行的最优。