package com.freud.mapreduce;

import java.io.IOException;

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;

/**
 * Entrance of the word count.
 * 
 * @author freud
 * 
 */
public class WordCount {

	public static void main(String[] args) throws IOException,
			InterruptedException, ClassNotFoundException {
		// Get the hadoop configuration.
		Configuration conf = new Configuration();
		// Check the arguments.
		String[] otherArgs = new GenericOptionsParser(conf, args)
				.getRemainingArgs();

		/**
		 * Error if the argument length is not 2
		 */
		if (otherArgs.length != 2) {
			System.out.println("Usage:wordcount <in> <out>");
			System.exit(2);
		}

		// Instance a new job to run this program.
		Job job = new Job(conf, "Word count");
		job.setJarByClass(WordCount.class);
		job.setMapperClass(WordCountMapper.class);
		job.setReducerClass(WordCountReduce.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);

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

		// Run and exit
		System.exit(job.waitForCompletion(true) ? 0 : 1);
	}
}

package com.freud.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;

/**
 * 
 * Get the text line by line and then separate with " \t\n\r\f".
 * 
 * @author freud
 * 
 */
public class WordCountMapper extends Mapper<Object, Text, Text, IntWritable> {

	@Override
	protected void map(Object key, Text value, Context context)
			throws IOException, InterruptedException {

		// Get the line value
		String valueStr = value.toString();
		// Separate the data。
		StringTokenizer st = new StringTokenizer(valueStr);

		// Write the map into context
		while (st.hasMoreElements()) {
			context.write(new Text(st.nextToken()), new IntWritable(1));
		}
	}

}

package com.freud.mapreduce;

import java.io.IOException;

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

/**
 * Reducer class responsible for merge the same data in map.
 * 
 * @author Freud
 * 
 */
public class WordCountReduce extends
		Reducer<Text, IntWritable, Text, IntWritable> {

	@Override
	protected void reduce(Text key, Iterable<IntWritable> value, Context context)
			throws IOException, InterruptedException {
		// Count
		int sum = 0;
		while (value.iterator().hasNext()) {
			sum += value.iterator().next().get();
		}
		// after merge the same data then set the map back.
		context.write(key, new IntWritable(sum));
	}
}

./hadoop jar Hadoop-Test-0.0.1-SNAPSHOT.jar com.freud.mapreduce.WordCount /home/hadoop-test/hadoop/input /home/hadoop-test/hadoop/output