MapReduce使用快速排序算法实现对文件里面数字进行排序的java代码

下面是使用MapReduce实现对文件里面数字进行排序的Java代码，其中使用了快速排序算法：

import java.io.IOException;
import java.util.*;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;

public class SortNumbers {

    public static class Map extends Mapper<LongWritable, Text, IntWritable, NullWritable> {
        private IntWritable num = new IntWritable();

        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String line = value.toString();
            StringTokenizer tokenizer = new StringTokenizer(line);
            while (tokenizer.hasMoreTokens()) {
                num.set(Integer.parseInt(tokenizer.nextToken()));
                context.write(num, NullWritable.get());
            }
        }
    }

    public static class Reduce extends Reducer<IntWritable, NullWritable, IntWritable, NullWritable> {
        public void reduce(IntWritable key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
            context.write(key, NullWritable.get());
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "sort numbers");

        job.setJarByClass(SortNumbers.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);

        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(NullWritable.class);

        job.setNumReduceTasks(1); // 设置Reduce任务数为1

        // 设置输入和输出文件路径
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

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

在这段代码中，Map任务将输入文件按照空格分隔符进行拆分，将每个数字作为key值输出，value值为NullWritable。Reduce任务将输入的key值进行全局排序，并输出结果。最终结果会按照升序排列的顺序输出到输出文件中。