Hadoop学习笔记 3 - Partioner 学习-优快云博客

本文链接：https://blog.youkuaiyun.com/iteye_17645/article/details/82644408

Partioner在Hadoop中扮演重要角色，负责在Mapper和Reducer间进行数据分组，根据设定将数据分配到特定节点执行，以实现数据分布的控制。文章介绍了Partioner的工作原理，并提供了实现代码示例。

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Partioner是通过启动多个map 与Reduce来将文件中的数据进行分组, 在Mapper向Reducer输出之前
对输出进行分组并根据此次分组指定每组数据在那台机器上执行，将结果输出到不同文件。

以下为实现代码：

package com.itbuilder.mr;

import java.io.IOException;
import java.util.HashMap;
import java.util.Map;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
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 com.itbuilder.mr.bean.DataBean;

/**
 * 手机流量计算
 * @author mrh
 *
 */
public class GRSDataCount {
	
	
	public static void main(String[] args) throws Exception {
		Job job = Job.getInstance(new Configuration());
		job.setJarByClass(GRSDataCount.class);
		
		
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(DataBean.class);
		job.setMapperClass(DCMapper.class);
		FileInputFormat.setInputPaths(job, new Path(args[0]));
		
		job.setNumReduceTasks(Integer.parseInt(args[2]));
		
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(DataBean.class);
		job.setReducerClass(DCRuducer.class);
		FileOutputFormat.setOutputPath(job, new Path(args[1]));
		job.setPartitionerClass(DCPartioner.class);
		
		job.waitForCompletion(true);
		
	}
	
	/**
	 * 
	 * @author mrh
	 *
	 */
	public static class DCMapper extends Mapper<LongWritable, Text, Text, DataBean> {

		@Override
		protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, DataBean>.Context context)
				throws IOException, InterruptedException {
			String datas[] = value.toString().split("\t");
			DataBean dataBean = new DataBean(datas[1], Long.parseLong(datas[8]), Long.parseLong(datas[9]));
			context.write(new Text(dataBean.getTelNo()), dataBean);
		}
	}
	
	/**
	 * Partitioner
	 * @author mrh
	 *
	 */
	public static class DCPartioner extends Partitioner<Text, DataBean> {

		private static Map<String, Integer> providerMap = new HashMap<String, Integer>();
		static {
			providerMap.put("135", 1);
			providerMap.put("136", 1);
			providerMap.put("137", 1);
			providerMap.put("138", 1);
			providerMap.put("139", 1);
			providerMap.put("150", 2);
			providerMap.put("159", 2);
			providerMap.put("180", 3);
			providerMap.put("182", 3);
			
		}
		@Override
		public int getPartition(Text key, DataBean value, int numPartitions) {
			String code = key.toString();
			Integer partion = providerMap.get(code.substring(0, 3));
			if (partion == null) {
				return 0;
			}
			return partion.intValue();
		}
		
	}
	
	/**
	 * 
	 * @author mrh
	 *
	 */
	public static class DCRuducer extends Reducer<Text, DataBean, Text, DataBean> {

		@Override
		protected void reduce(Text key, Iterable<DataBean> beans, Reducer<Text, DataBean, Text, DataBean>.Context context)
				throws IOException, InterruptedException {
			long upPayLoad = 0;
			long downPayLoad = 0;
			
			for (DataBean bean : beans) {
				upPayLoad += bean.getUpload();
				downPayLoad += bean.getDownload();
			}
			DataBean outBean = new DataBean(key.toString(), upPayLoad, downPayLoad);
			
			context.write(key, outBean);
		}
		
	}
}