hadoop笔记之MapReduce的应用案例(利用MapReduce进行排序)

MapReduce的应用案例(利用MapReduce进行排序)

MapReduce的应用案例(利用MapReduce进行排序)

思路：

Reduce之后直接进行结果合并

具体样例：

程序名：Sort.java

import java.io.IOException;

import java.util.StringTokenizer;

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.Partitioner;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import org.apache.hadoop.util.GenericOptionsParser;

public class Sort {
//map将输入中的value化成IntWritable类型作为输出的key
    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();

            data.set(Integer.parseInt(line));

            context.write(data, new IntWritable(1));

        }

    }
/*reduce将输入中的key复制到输出数据的key上，
然后根据输入的value-list中的元素的个数决定key的输出次数，
用全局linenum来代表key的位次*/
    public static class Reduce extends
            Reducer<IntWritable, IntWritable, IntWritable, IntWritable> {

        private static IntWritable linenum = new IntWritable(1);
//实现reduce函数
        public void reduce(IntWritable key, Iterable<IntWritable> values,
                Context context) throws IOException, InterruptedException {

            for (IntWritable val : values) {

                context.write(linenum, key);

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

        }
    }

    public static class Partition extends Partitioner<IntWritable, IntWritable> {

        @Override
        public int getPartition(IntWritable key, IntWritable value,
                int numPartitions) {
            int MaxNumber = 65223;
            int bound = MaxNumber / numPartitions + 1;
            int keynumber = key.get();
            for (int i = 0; i < numPartitions; i++) {
                if (keynumber < bound * i && keynumber >= bound * (i - 1))
                    return i - 1;
            }
            return 0;
        }
    }

    /**
     * @param args
     */

    public static void main(String[] args) throws Exception {
        // TODO Auto-generated method stub
        Configuration conf = new Configuration();
        String[] otherArgs = new GenericOptionsParser(conf, args)
                .getRemainingArgs();
        if (otherArgs.length != 2) {
            System.err.println("Usage WordCount <int> <out>");
            System.exit(2);
        }
        Job job = new Job(conf, "Sort");
        job.setJarByClass(Sort.class);
        //设置map和reduce处理类
        job.setMapperClass(Map.class);
        job.setPartitionerClass(Partition.class);
        job.setReducerClass(Reduce.class);
        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }

}