如何使用map reduce获取前两个元素？

Question

我正在学习Hadoop。 我正在尝试使用map reduce解决以下问题：

给出一个包含医生和相应患者记录的文件。找到治疗了大多数患者的前两名医生。

例如，

输入：

医生病人

D4 P1

D2 P2

D3 P3

D4 P4

D1 P5

D4 P1

D2 P2

D3 P1

D2 P5

D2 P6

D2 P1

D2 P5

D4 P2

D2 P1

输出：

D2 7

D4 4

我能够列出所有医生的患者治疗次数。但是无法弄清楚如何让前两名医生使用MR？

package com.doctors;

import java.io.IOException;

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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.KeyValueTextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class TopDoctors {

    public static class Mapper extends
            org.apache.hadoop.mapreduce.Mapper<Text, Text, Text, IntWritable> {

        @Override
        protected void map(
                Text key,
                Text value,
                org.apache.hadoop.mapreduce.Mapper<Text, Text, Text, IntWritable>.Context context)
                throws IOException, InterruptedException {
            context.write(key, new IntWritable(1));
        }
    }

    public static class Reducer
            extends
            org.apache.hadoop.mapreduce.Reducer<Text, IntWritable, IntWritable, Text> {
        @Override
        protected void reduce(
                Text key,
                Iterable<IntWritable> value,
                org.apache.hadoop.mapreduce.Reducer<Text, IntWritable, IntWritable, Text>.Context context)
                throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable i : value) {
                sum += i.get();
            }
            context.write(new IntWritable(sum), key);
        }
    }

    public static void main(String[] args) throws IOException,
            ClassNotFoundException, InterruptedException {
        Job job = Job.getInstance();

        job.setJarByClass(TopDoctors.class);

        job.setMapperClass(Mapper.class);
        job.setReducerClass(Reducer.class);

        job.setOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        job.setInputFormatClass(KeyValueTextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);

        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

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

    }
}

请澄清。

Answer 1

我认为这可以解决你的问题  把你的总和＆amp; TreeMap中的doctor-number并写入cleanup method

  public static class Reducer  ......

#In your reduce class create a TreeMap
 TreeMap<Integer, String> TopN = new TreeMap<Integer, String>();


protected void reduce(......){
   #here  is  your  get sum code

 if (TopN.size() < 3) {  //TopN
     if (TopN.get(sum) != null) 
     { TopN.put(sum, TopN.get(sum) + "-----" + key.toString()); 
    } else {
     TopN.put(sum, key.toString());
     }
} else {
 // if map.size> N  add one  & remove one
 if (TopN.get(sum) != null) { 
    TopN.put(sum, TopN.get(sum) + "------" + key.toString()); 
     // 
    } else { 
     TopN.put(sum, key.toString());
    TopN.remove(TopN.firstKey()); 
     } 
     } // when you put your data in treemap, it will sorted by itself; 
     } 
 @Override 
 protected void cleanup(Reducer<Text, IntWritable, Text, IntWritable>.Context context)throws IOException, InterruptedException { 
if (TopN != null && !TopN.isEmpty()) { 
     Set<Integer> keys = TopN.keySet(); 
    for (Integer key : keys) { outkey.set(TopN.get(key));
         outvalue.set(key); 
        context.write(outkey, outvalue); 
                             } } } }