Partition分区的使用案例：
将统计结果按照条件输出到不同文件中（分区）
文章目录
1）需求
将统计结果按照手机号开头输出到不同文件中
期望输出：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）需求分析
3）编程实现
在原基础上，增加一个分区类：
1.创建Partitioner类
package com.yingzi.mapreduce.partitioner2;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;
/**
* @author 影子
* @create 2022-01-14-13:16
**/
public class ProvincePartitioner extends Partitioner<Text, FlowBean> {
@Override
public int getPartition(Text text, FlowBean flowBean, int numPartitions) {
// text：手机号
String phone = text.toString();
String prePhone = phone.substring(0, 3);
int partition;
if ("136".equals(prePhone)){
partition = 0;
}else if("137".equals(prePhone)){
partition = 1;
}else if("138".equals(prePhone)){
partition = 2;
}else if("139".equals(prePhone)){
partition = 3;
}else{
partition = 4;
}
return partition;
}
}
2.创建Bean类
package com.yingzi.mapreduce.partitioner2;
/**
* @author 影子
* @create 2022-01-13-15:57
**/
import org.apache.hadoop.io.Writable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
/**
* 1、定义类实现writable接口
* 2、重写序列化和反序列化方法
* 3、重写空参构造
* 4、toString方法
*/
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;
}
@Override
public void write(DataOutput dataOutput) throws IOException {
dataOutput.writeLong(upFlow);
dataOutput.writeLong(downFlow);
dataOutput.writeLong(sumFlow);
}
@Override
public void readFields(DataInput dataInput) throws IOException {
this.upFlow = dataInput.readLong();
this.downFlow = dataInput.readLong();
this.sumFlow = dataInput.readLong();
}
@Override
public String toString() {
return upFlow + "\t" + downFlow + "\t" + sumFlow;
}
}
3.创建Mapper类
package com.yingzi.mapreduce.partitioner2;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
/**
* @author 影子
* @create 2022-01-13-16:12
**/
public class FlowMapper extends Mapper<LongWritable, Text, Text, FlowBean> {
private Text outK = new Text();
private FlowBean outV = new FlowBean();
@Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, FlowBean>.Context context) throws IOException, InterruptedException {
// 1.获取一行
// 1 13736230513 192.196.100.1 www.atguigu.com 2481 24681 200
String line = value.toString();
// 2.切割
// 1,13736230513,192.196.100.1,www.atguigu.com 2481,24681,200
String[] split = line.split("\t");
// 3.抓取想要的数据
// 手机号：13736230513
// 上行流量：2481 下行流量：24681
String phone = split[1];
String up = split[split.length - 3];
String down = split[split.length - 2];
// 4.封装
outK.set(phone);
outV.setUpFlow(Long.parseLong(up));
outV.setDownFlow(Long.parseLong(down));
outV.setSumFlow();
// 5.写出
context.write(outK,outV);
}
}
4.创建Reducer类
package com.yingzi.mapreduce.partitioner2;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
/**
* @author 影子
* @create 2022-01-13-16:32
**/
public class FlowReducer extends Reducer<Text, FlowBean,Text, FlowBean>{
private FlowBean outV = new FlowBean();
@Override
protected void reduce(Text key, Iterable<FlowBean> values, Reducer<Text, FlowBean, Text, FlowBean>.Context context) throws IOException, InterruptedException {
// 1.遍历集合累加值
long totalUp = 0;
long totalDown = 0;
for (FlowBean value : values) {
totalUp += value.getUpFlow();
totalDown += value.getDownFlow();
}
// 2.封装outK,outV
outV.setUpFlow(totalUp);
outV.setDownFlow(totalDown);
outV.setSumFlow();
// 3.写出
context.write(key,outV);
}
}
5.创建Driver类
package com.yingzi.mapreduce.partitioner2;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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;
/**
* @author 影子
* @create 2022-01-13-16:40
**/
public class FlowDriver {
public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {
// 1.获取job
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
// 2.设置jar
job.setJarByClass(FlowDriver.class);
// 3.关联Mapper、Reducer
job.setMapperClass(FlowMapper.class);
job.setReducerClass(FlowReducer.class);
// 4.设置mapper,输出的key和value类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(FlowBean.class);
// 5.设置最终数据输出的key和value类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(FlowBean.class);
//8.指定自定义分区器
job.setPartitionerClass(ProvincePartitioner.class);
//9 同时指定相应数量的 ReduceTask
job.setNumReduceTasks(5);
// 6.设置数据的输入和输出路径
FileInputFormat.setInputPaths(job,new Path("G:\\计算机资料\\大数据开发\\尚硅谷大数据技术之Hadoop3.x\\资料\\11_input\\inputflow"));
FileOutputFormat.setOutputPath(job,new Path("G:\\计算机资料\\大数据开发\\尚硅谷大数据技术之Hadoop3.x\\资料\\_output\\output7"));
// 7.提交job
boolean result = job.waitForCompletion(true);
System.exit(result ? 0:1);
}
}