应用需求

通常在数据文件中包含大量的记录，每条记录中包含了这个事物的某个属性，需要根据这个属性对数据进行排序。

解决方案

map 函数对每条记录的事物和属性按照特定的规则进行计算，获得属性值，并以属性为 key,value为原数据值。reduce 函数对同组的排序值进行排序后按顺序输出。

应用案例

对输入文件中数据进行排序。输入文件中的每行内容均为一个数字，即一个数据。要求在输出中每行有两个间隔的数字，其中，第一个代表原始数据在数据集排序中的位次，第二个代表原始数据。

sort1.txt:
34
6543
12
-45
58
753
234
858

sort2.txt:
34
675
349
648
75
39
-7

sort3.txt:
34
76
236
2387
-497
45
34

程序代码

SortMapper

package com.test.sort;

import java.io.IOException;

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

public class SortMapper extends Mapper<LongWritable, Text, IntWritable, IntWritable>{

    private static IntWritable data = new IntWritable();
    private static IntWritable one = new IntWritable(1);
    
    @Override
    protected void map(LongWritable key, Text value,Context context)
            throws IOException, InterruptedException {
        String line = value.toString();
        data.set(Integer.parseInt(line));
        context.write(data, one);
    }
}

SortReducer

package com.test.sort;

import java.io.IOException;

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

public class SortReducer extends Reducer<IntWritable, IntWritable, IntWritable, IntWritable>{

    private static IntWritable lineNum = new IntWritable(1);
    
    @Override
    protected void reduce(IntWritable key, Iterable<IntWritable> values,Context context)
            throws IOException, InterruptedException {
        // map 阶段已经对 key 进行排序
        for (IntWritable val : values) {
            context.write(lineNum, key);
            lineNum = new IntWritable(lineNum.get() + 1);
        }
    }

}

SortRunner

package com.test.sort;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.Tool;
import org.apache.hadoop.util.ToolRunner;

public class SortRunner extends Configured implements Tool{

    @Override
    public int run(String[] args) throws Exception {
        
        Configuration  conf = new Configuration();
        Job job = Job.getInstance(conf, "Simple Sort");
        job.setJarByClass(SortRunner.class);
        
        job.setMapperClass(SortMapper.class);
        job.setReducerClass(SortReducer.class);
        
        job.setMapOutputKeyClass(IntWritable.class);
        job.setMapOutputValueClass(IntWritable.class);
        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(IntWritable.class);
        
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        
        return job.waitForCompletion(true) ? 0:1;
    }

    public static void main(String[] args) throws Exception {
        int res = ToolRunner.run(new Configuration(), new SortRunner(), args);
        System.exit(res);
    }
}

运行结果

1   -497
2   -45
3   -7
4   12
5   34
6   34
7   34
8   34
9   39
10  45
11  58
12  75
13  76
14  234
15  236
16  349
17  648
18  675
19  753
20  858
21  2387
22  6543

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