03Hadoop的TopN的问题

TopN的问题分为两种:一种是建是唯一的，还有是建非唯一。我们这边做的就是建是唯一的。

这里的建指得是：下面数据的第一列。

有一堆数据，想根据第一列找出里面的Top10.

如下：

关键：在map和reduce阶段都使用了TreeMap这个数据结构，他有从小到大的排序功能，所以排第一的最小，依次增大。限定大小为10 ，只要超过十，就把排在第一个的值给删除。

代码如下：

package com.book.topn;

import java.io.IOException;

import java.util.Iterator;

import java.util.Set;

import java.util.SortedMap;

import java.util.TreeMap;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.NullWritable;

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.lib.input.FileInputFormat;

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

public class TopN {

    static class Mapper1 extends Mapper<LongWritable, Text, NullWritable, Text> {

        public SortedMap<Double, Text> top10cats = new TreeMap<Double, Text>();

        public int N = 10;

        @Override

        protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, NullWritable, Text>.Context context)

                throws IOException, InterruptedException {

            String[] lines = value.toString().split(",");

            Double weight = Double.parseDouble(lines[0]);

            // 一行读完，然后把数据

            top10cats.put(weight, new Text(value));

            // 如果Map

            if (top10cats.size() > N) {

                top10cats.remove(top10cats.firstKey());

            }

        }

        // 待执行完map的读取比较操作后，就把TreeMap里面的数据打印出来。

        @Override

        protected void cleanup(Mapper<LongWritable, Text, NullWritable, Text>.Context context)

                throws IOException, InterruptedException {

            Set<Double> set = top10cats.keySet();

            Iterator<Double> iterator = set.iterator();

            while (iterator.hasNext()) {

                context.write(NullWritable.get(), top10cats.get(iterator.next()));

            }

        }

    }

    static class reduce1 extends Reducer<NullWritable, Text, NullWritable, Text> {

        SortedMap<Double, Text> finalTop = new TreeMap<Double, Text>();

        private int N = 10;

        @Override

        protected void reduce(NullWritable arg0, Iterable<Text> values,

                Reducer<NullWritable, Text, NullWritable, Text>.Context context)

                throws IOException, InterruptedException {

            for (Text value : values) {

                String[] finalresult = value.toString().split(",");

                finalTop.put(Double.parseDouble(finalresult[0]), new Text(value));

                if (finalTop.size() > N) {

                    finalTop.remove(finalTop.firstKey());

                }

                ;

            }

            Set<Double> set = finalTop.keySet();

            Iterator<Double> iterator = set.iterator();

            // 依次写入到文件中

            while (iterator.hasNext()) {

                context.write(NullWritable.get(), finalTop.get(iterator.next()));

            }

        }

    }

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

        Configuration conf = new Configuration();

        Job job = Job.getInstance(conf);

        job.setJarByClass(TopN.class);

        job.setMapperClass(Mapper1.class);

        job.setReducerClass(reduce1.class);

        job.setMapOutputKeyClass(NullWritable.class);

        job.setMapOutputValueClass(Text.class);

        job.setOutputValueClass(NullWritable.class);

        job.setOutputKeyClass(Text.class);

        // 指定输入的数据的目录

        FileInputFormat.setInputPaths(job, new Path("/Users/mac/Desktop/TopN.txt"));

        FileOutputFormat.setOutputPath(job, new Path("/Users/mac/Desktop/flowresort"));

        boolean result = job.waitForCompletion(true);

        System.exit(result ? 0 : 1);

    }

}

结果：

注意点：

上面的注意点一定要切记。