mapreduce 实现数子排序

设计思路：

　　使用mapreduce的默认排序，按照key值进行排序的，如果key为封装int的IntWritable类型，那么MapReduce按照数字大小对key排序，如果key为封装为String的Text类型，那么MapReduce按照字典顺序对字符串排序。

　　首先map阶段将输入的数字作为key, 并记录相同key出现的次数，在reduce阶段将输入的key作为输出的value,如果相同值存在多个，循环便利输出。

源数据：file1

　　file2

　　file3

　　源代码

package com.duking.hadoop;

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

public class DataSort {

	public static class Map extends
			Mapper<Object, Text, IntWritable, IntWritable> {

		private static IntWritable data = new IntWritable();

		// 实现map函数
		public void map(Object key, Text value, Context context)

		throws IOException, InterruptedException {
			
			String line = value.toString(); // 将输入的每一行数据转换为String类型

			data.set(Integer.parseInt(line)); // 将String 转换为Integer

			context.write(data, new IntWritable(1)); // 将 date->key
														// 统计key出现的次数自增为value
		}
	}

	// reduce将输入中的key复制到输出数据的key上，并直接输出 这是数据区重的思想
	public static class Reduce extends
			Reducer<IntWritable, IntWritable, IntWritable, IntWritable> {

		private static IntWritable linenum = new IntWritable(1);
		private IntWritable result = new IntWritable();

		// 实现reduce函数

		public void reduce(IntWritable key, Iterable<IntWritable> values,  //Iterable转为List
				Context context)

		throws IOException, InterruptedException {
			
			for (IntWritable val : values) {

				context.write(linenum, key);

				linenum = new IntWritable(linenum.get() + 1);
			}
		}

	}

	public static void main(String[] args) throws Exception {

		Configuration conf = new Configuration();

		conf.set("mapred.job.tracker", "192.168.60.129:9000");

		// 指定带运行参数的目录为输入输出目录
		String[] otherArgs = new GenericOptionsParser(conf, args)
				.getRemainingArgs();

		/*
		 * 指定工程下的input2为文件输入目录 output2为文件输出目录 String[] ioArgs = new String[] {
		 * "input2", "output2" };
		 * 
		 * String[] otherArgs = new GenericOptionsParser(conf, ioArgs)
		 * .getRemainingArgs();
		 */

		if (otherArgs.length != 2) { // 判断路径参数是否为2个

			System.err.println("Usage: Data Deduplication <in> <out>");

			System.exit(2);

		}

		// set maprduce job name
		Job job = new Job(conf, "Data sort");

		job.setJarByClass(DataSort.class);

		// 设置Map、Combine和Reduce处理类

		job.setMapperClass(Map.class);

		job.setCombinerClass(Reduce.class);

		job.setReducerClass(Reduce.class);

		// 设置输出类型

		job.setOutputKeyClass(IntWritable.class);

		job.setOutputValueClass(IntWritable.class);

		// 设置输入和输出目录

		FileInputFormat.addInputPath(job, new Path(otherArgs[0]));

		FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));

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

	}

}

　　结果