Partition分区的使用案例

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）

1）需求2）需求分析3）编程实现

1.创建Partitioner类2.创建Bean类3.创建Mapper类4.创建Reducer类 4.查看结果

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

在原基础上，增加一个分区类：

package com.yingzi.mapreduce.partitioner2;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;


public class ProvincePartitioner extends Partitioner {

    @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;
    }
}

package com.yingzi.mapreduce.partitioner2;



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;
    }

    @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;
    }
}

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;


public class FlowMapper extends Mapper {
    private Text outK = new Text();
    private FlowBean outV = new FlowBean();


    @Override
    protected void map(LongWritable key, Text value, Mapper.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);
    }
}

package com.yingzi.mapreduce.partitioner2;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;


public class FlowReducer extends Reducer{

    private FlowBean outV = new FlowBean();
    @Override
    protected void reduce(Text key, Iterable values, Reducer.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;



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);

    }
}