MapReduce-Partition分区 （From 尚硅谷）

MapReduce-Partition分区 （From 尚硅谷）

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MapReduce-Partition分区

1.1 Partition分区

1. 问题引出：

​ 要求将统计结果按照条件输出到不同文件中（分区）。比如：将统计结果按照手机归属地不同省份输出到不同文件中（5个分区）。

2. 默认Partitioner分区

public class HashPartitioner extends Partitioner{
    public HashPartitioner(){
    }
    public int getPartition(K key,V value,int numReduceTasks){
        return (key.hashCode() & 2147483467) % numReduceTasks;
    }
}

默认分区是根据key的hashCode对ReduceTasks个数取模得到。用户需自定义Partitioner控制key的存储分区。

3. 自定义Partitioner步骤

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

public class CustomPartitioner extends Partitioner{
    @Override
    public int getPartition(Text key,FlowBean value,int numPartitions){
        //控制分区逻辑代码
        
        return partition;
    }
}

（2）在Job驱动中，设置自定义Partitioner

job.setPartitionerClass(CustomPartitioner.Class);

（3）自定义Partition后，要根据自定义的Partitioner的逻辑设置相应数量的ReduceTask

Job.setNumReduceTasks(5);

4. 分区总结

（1）如果ReduceTask的数量>getPartition的结果数，则会多产生几个空的输出文件part-r-000xx;

（2）如果1

（3）如果ReduceTask的数量=1，则不管MapTask端输出多少个分区文件，最终结果都会交给这一个ReduceTask，最终也就会产生一个结果文件part-r-00000;

可以看到当partitions数量为1时，则最终都会交给一个ReduceTask，那么最终也就会产生于一个结果文件。

（4）分区号必须从零开始，逐一累加。

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;
//上述partition必须从0开始，逐一累加

（5）例如假设自定义分区为5，则

job.setNumReduceTasks(1);//会正常运行，只不过会产生一个输出文件
job.setNumReduceTasks(2);//会报错
job.setNumReduceTasks(6);//大于5，程序会正常运行，会产生空文件

1.2 Partition分区案例实 ***

（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）希望输出数据：手机号136、137、138、139开头都分别到一个独立的4个文件中，其他开头的放到一个文件中。

（3）分析：

• FlowBean类

package com.atguigu.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 {//实现Writable接口

    private long upFlow;
    private long downFlow;
    private long sumFlow;

    //反序列化时，需要反射调用空参构造函数，所以必须有空参构造
    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() {
        this.sumFlow = this.upFlow + this.downFlow;
    }

    //重写序列化方法
    @Override
    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeLong(upFlow);
        dataOutput.writeLong(downFlow);
        dataOutput.writeLong(sumFlow);
    }
    //重写反序列方法
    @Override
    public void readFields(DataInput dataInput) throws IOException {
        this.upFlow = dataInput.readLong();
        this.downFlow = dataInput.readLong();
        this.sumFlow = dataInput.readLong();
    }
    //反序列化的顺序必须和序列化的顺序相同

    @Override
    public String toString() {
        return upFlow + "t" + downFlow + "t" + sumFlow;
    }
}

• Mapper类

package com.atguigu.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、上行流量和下行流量)
        String phone = split[1];
        String up = split[split.length-3];//从末尾开始算索引
        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);
    }
}

• Reducer类

package com.atguigu.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);
    }
}

• 增加一个ProvincePartitioner类

package com.atguigu.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 i){
        //text是手机号
        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;
    }
}

• 在驱动函数中增加自定义函数分区设置（步骤8）和ReduceTask设置（步骤9）

package com.atguigu.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、获取配置信息以及获取job对象
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        //2、关联本Driver的jar
        job.setJarByClass(FlowDriver.class);

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

        //4、设置Mapper输出的kv类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);

        //5、设置最终输出的kv类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);
		
        //8.指定自定义分区
        job.setPartitionerClass(ProvincePartitioner.class);
        
        //9.同时指定相应数量的ReduceTask
        job.setNumReduceTasks(5);

        //6、设置输入和输出路径
        FileInputFormat.setInputPaths(job,new Path("D:\downloads\hadoop-3.1.0\data_input\inputflow"));
        FileOutputFormat.setOutputPath(job,new Path("D:\downloads\hadoop-3.1.0\data\output\output5"));
//        FileInputFormat.setInputPaths(job,new Path(args[0]));
//        FileOutputFormat.setOutputPath(job,new Path(args[1]));

        //7、提交job
        boolean result = job.waitForCompletion(true);
        System.exit(result?0:1);
    }
}