通过MapReduce原理进行简单的分区。

 操作内容 
1. 需求 
将统计结果按照手机号前三位输出到不同文件中（分区） 
（1） 输入数据 

    
 
（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，其他开头放到一个文件中 
（3） 在序列化作业的基础上，增加一个分区类 

2. 需求分析 
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） 
2. 数据输入 
 

3. 期望数据输出 

   

4. 增加一个 ProvincePartitioner 分区 
 
5. Driver 驱动类 

 

这是所用到的phone_data.txt

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

根据MapReduce原理编写FlowBean、FlowMapper、FlowReducer、FlowDriver、FlowPartitioner代码：

FlowBean.java

package com.mapreduce.writable;import org.apache.hadoop.io.Writable;import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;public class FlowBean implements Writable {private long upFlow;//上行流量private long downFlow;//下行流量private long sumFlow;//总流量//空参构造public FlowBean() {}public long getUpFlow() {return upFlow;}public void setUpFlow(long upFlow) {this.upFlow = upFlow;}public long getDownFlow() {return downFlow;}public void setDownFlow(long downFlow) {this.downFlow = downFlow;}public long getSumFlow() {return sumFlow;}public void setSumFlow(long sumFlow) {this.sumFlow = sumFlow;}public void setSumFlow() {this.sumFlow = this.upFlow+this.downFlow;}@Overridepublic void write(DataOutput dataOutput) throws IOException {dataOutput.writeLong(upFlow);dataOutput.writeLong(downFlow);dataOutput.writeLong(sumFlow);}@Overridepublic void readFields(DataInput dataInput) throws IOException {this.upFlow=dataInput.readLong();this.downFlow=dataInput.readLong();this.sumFlow=dataInput.readLong();}@Overridepublic String toString() {return upFlow + "\t" +downFlow +"\t" +sumFlow;}
}

FlowDriver.java

package com.mapreduce.writable;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 java.io.IOException;public class FlowDriver {public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {//1.获取配置信息，获取job对象实例Configuration conf = new Configuration();Job job = Job.getInstance(conf);//2.指定本程序的jar包所在的本利路径job.setJarByClass(FlowDriver.class);//3.关联Mapper Reducerjob.setMapperClass(FlowMapper.class);job.setReducerClass(FlowReducer.class);//4.指定Mapper输出数据的kv类型job.setMapOutputKeyClass(Text.class);job.setOutputValueClass(IntWritable.class);//5.指定最终输出的数据的kv类型job.setOutputKeyClass(Text.class);job.setOutputValueClass(FlowBean.class);//指定自定义分区job.setPartitionerClass(FlowPartitioner.class);//指定相应数量的ReduceTaskjob.setNumReduceTasks(5);//6.设置出入路径和输出路径FileInputFormat.setInputPaths(job,new Path("E:\\HAOHAO\\input\\phone_data.txt"));FileOutputFormat.setOutputPath(job,new Path("E:\\HAOHAO\\output"));//7.提交作业boolean result = job.waitForCompletion(true);System.exit(result ? 0:1);}
}

（这里的new path自己设置，将phone_data.txt放到指定的input文件夹下，注意路径一定要写对，output文件夹不用创建，代码运行之后自动生成，为了方便可以把input和output文件夹放在同一目录下）

FlowMapper.java

package com.mapreduce.writable;import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;import java.io.IOException;public class FlowMapper extends Mapper<LongWritable, Text,Text,FlowBean> {private Text outK=new Text();private FlowBean outV=new FlowBean();@Overrideprotected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, FlowBean>.Context context) throws IOException, InterruptedException {//1.获取一行数据 转成字符串String line=value.toString();//2.切割数据String[] split=line.split("\t");//3.抓取想要的数据String phone=split[1];String up=split[split.length-3];String down=split[split.length-2];//4.封装数据 outK outVoutK.set(phone);outV.setUpFlow(Long.parseLong(up));outV.setDownFlow(Long.parseLong(down));outV.setSumFlow();//5.写出outK outVcontext.write(outK,outV);}
}

FlowReducer.java

package com.mapreduce.writable;import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;import java.io.IOException;public class FlowReducer extends Reducer<Text,FlowBean,Text,FlowBean> {private FlowBean outV=new FlowBean();@Overrideprotected void reduce(Text key, Iterable<FlowBean> values, Reducer<Text, FlowBean, Text, FlowBean>.Context context) throws IOException, InterruptedException {//遍历values，分别累加上行流量、下行流量long totalUp=0;long totalDown=0;for (FlowBean value:values){totalUp+=value.getUpFlow();totalDown+=value.getDownFlow();}//封装outK outVoutV.setUpFlow(totalUp);outV.setDownFlow(totalDown);outV.setSumFlow();//写出outk,outVcontext.write(key,outV);}
}

FlowPartitioner.java

package com.mapreduce.writable;import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;public class FlowPartitioner extends Partitioner<Text,FlowBean> {@Overridepublic int getPartition(Text text, FlowBean flowBean, int i) {String phone = text.toString();String subphone = phone.substring(0,3);int partition;if ("136".equals(subphone)){partition=0;}else if ("137".equals(subphone)) {partition = 1;}else if ("138".equals(subphone)){partition=2;}else if ("139".equals(subphone)) {partition = 3;}   else {partition = 4;}return partition;}
}

运行结果：

 

  （老师上课的案例，自己做了一遍，希望对大家有所帮助，如有错误，还请大佬指正）