MapReduce当中Partitioner的用法

Partitioner的用法：

马克-to-win @ 马克java社区：防盗版实名手机尾号：73203。如果现在我们的需求变成，输出放在两个文件当中，按照关键字的首个字母的26个字母来分，头13个放在一个文件当中，以此类推， 这时我们就要用到partition的技术。

package com;
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.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
 
public class WordCountMark_to_win {
    public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
        private IntWritable one = new IntWritable(1);
        private Text word = new Text();

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            System.out.println("key is 马克-to-win @ 马克java社区："+key.toString()+" value is "+value.toString());
            StringTokenizer itr = new StringTokenizer(value.toString());
            while (itr.hasMoreTokens()) {
                word.set(itr.nextToken());
                context.write(word, one);
            }
        }
    }

    public static class PartitionClass extends Partitioner<Text, IntWritable> {
        public int getPartition(Text key, IntWritable value, int numPartitions)
        // numPartitions参数值从主函数中的job.setNumReduceTasks()获得马克-to-win @ 马克java社区：
        {
            int distancemark_to_win=26/numPartitions;
            int result ;
            if((key.charAt(0) - 'a')<distancemark_to_win)
                result=0;
            else
               result = 1;
            return result;
        }
    }
      

更多内容请见原文，文章转载自：https://blog.csdn.net/qq_44594249/article/details/97007904

标签：mapreduce,MapReduce,用法,Partitioner,org,apache,import,hadoop,public
来源： https://www.cnblogs.com/xiaolongxia1922/p/15488771.html