MapReduce-Partition分区

Partition分区

1.默认Partitioner分区

(key.hashcode() & Interger.MAX_VALUE) % numReduceTasks

numReduceTasks默认为：1
//输出文件一个

默认分区根据key的hashCode对ReduceTasks个数取模。
用户控制那个key存储到那个分区

2.手动设置分区

//设置分区
job.setNumReduceTasks(2);

3.自定义分区步骤

（1）三步

a.自定义类继承Partitioner,重写getPartition()方法

public class ProvincePartitioner extends Partitioner<Text, FlowBean> {
    @Override
    public int getPartition(Text text, FlowBean flowBean, int i) {
        return 0;
    }
}

b.在Job驱动中，设置自定义Partitioner

 job.setPartitionerClass(ProvincePartitioner.class);

c.自定义Partition后，根据自定义Partitioner的逻辑设置相应的ReduceTask

job.setNumReduceTasks(5);

Partition分区案例实操

1．需求

将统计结果按照手机归属地不同省份输出到不同文件中（分区）

（1）输入数据

1	13736230513	192.196.100.1	www.atguigu.com	2481	24681	200
2	13846544121	192.196.100.2			264	0	200
3 	13956435636	192.196.100.3			132	1512	200
4 	13966251146	192.168.100.1			240	0	404
5 	18271575951	192.168.100.2	www.atguigu.com	1527	2106	200
6 	84188413	192.168.100.3	www.atguigu.com	4116	1432	200
7 	13590439668	192.168.100.4			1116	954	200
8 	15910133277	192.168.100.5	www.hao123.com	3156	2936	200
9 	13729199489	192.168.100.6			240	0	200
10 	13630577991	192.168.100.7	www.shouhu.com	6960	690	200
11 	15043685818	192.168.100.8	www.baidu.com	3659	3538	200
12 	15959002129	192.168.100.9	www.atguigu.com	1938	180	500
13 	13560439638	192.168.100.10			918	4938	200
14 	13470253144	192.168.100.11			180	180	200
15 	13682846555	192.168.100.12	www.qq.com	1938	2910	200
16 	13992314666	192.168.100.13	www.gaga.com	3008	3720	200
17 	13509468723	192.168.100.14	www.qinghua.com	7335	110349	404
18 	18390173782	192.168.100.15	www.sogou.com	9531	2412	200
19 	13975057813	192.168.100.16	www.baidu.com	11058	48243	200
20 	13768778790	192.168.100.17			120	120	200
21 	13568436656	192.168.100.18	www.alibaba.com	2481	24681	200
22 	13568436656	192.168.100.19			1116	954	200

（2）期望输出数据

手机号136、137、138、139开头都分别放到一个独立的4个文件中，其他开头的放到一个文件中。

2.代码实现

在Flow基础上实现

Partitioner类

package com.saddam.bigdata.ShangGuiGu.Shuffle.Partition;

import com.saddam.bigdata.ShangGuiGu.Writable.FlowBean;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;

public class ProvincePartitioner extends Partitioner<Text, FlowBean> {
    @Override
    public int getPartition(Text key, FlowBean value, int numPartitions) {
        //key是手机号，value是流量信息bean对象

        //第一步：获取手机号前三位
        String prePhoneNum=key.toString().substring(0,3);

        int partition=4;
        //判断
        if ("136".equals(numPartitions)) {
            partition = 0;
        }else if ("137".equals(numPartitions)){
            partition=1;
        }else if ("138".equals(numPartitions)) {
            partition = 2;
        }else if ("139".equals(numPartitions)) {
            partition = 3;
        }
        return partition;
    }
}

Driver类

package com.saddam.bigdata.ShangGuiGu.Shuffle.Partition;

import com.saddam.bigdata.ShangGuiGu.Writable.FlowBean;
import com.saddam.bigdata.ShangGuiGu.Writable.FlowMapper;
import com.saddam.bigdata.ShangGuiGu.Writable.FlowReducer;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.log4j.BasicConfigurator;

public class ProvinceDriver {
    public static void main(String[] args) throws Exception{

        BasicConfigurator.configure();
        //1.获取job
        Configuration configuration=new Configuration();
        Job job=Job.getInstance(configuration);

        //2.设置jar包
        job.setJarByClass(ProvinceDriver.class);

        //3.关联Mapper和Reducer
        job.setMapperClass(FlowMapper.class);
        job.setReducerClass(FlowReducer.class);

        //4.设置map输出类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);

        //5.设最终输出的kv类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);


        job.setPartitionerClass(ProvincePartitioner.class);

        job.setNumReduceTasks(5);

        //6.设置输入输出路径
        FileInputFormat.setInputPaths(job,new Path("D:\\MR\\MapReduce\\InputDatas\\phone.txt"));
        FileOutputFormat.setOutputPath(job,new Path("D:\\MR\\MapReduce\\OutputDatas\\output_partition\\output_Flow"));

        //7.提交job
        boolean result=job.waitForCompletion(true);

        System.exit(result?0:1);
    }
}

总结

若Partition类中int partition=4;设置5个分区

但是

        job.setNumReduceTasks(5);--》   job.setNumReduceTasks(1);
        
成功运行，但是输出结果就一个文件，相当于未分区



		job.setNumReduceTasks(5);--》   job.setNumReduceTasks(2);
报错IO异常

		job.setNumReduceTasks(5);--》   job.setNumReduceTasks(6);
大于程序可以运行，输出多一个空文件