|NO.Z.00036|——————————|BigDataEnd|——|Hadoop&MapReduce.V09|——|Hadoop.v09|MapReduce原理剖析之自定义分区及ReduceTask数|自定义分区案例.v02|

一、编程实现步骤

 

### --- 创建项目：partition
### --- Mapper

package com.yanqi.mr.partition;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

/*
1. 读取一行文本，按照制表符切分

2. 解析出appkey字段，其余数据封装为PartitionBean对象（实现序列化Writable接口）

3. 设计map()输出的kv,key-->appkey(依靠该字段完成分区)，PartitionBean对象作为Value输出
 */
public class PartitionMapper extends Mapper<LongWritable, Text, Text, PartitionBean> {
    final PartitionBean bean = new PartitionBean();
    final Text k = new Text();

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        final String[] fields = value.toString().split("\t");
        String appkey = fields[2];

        bean.setId(fields[0]);
        bean.setDeviceId(fields[1]);
        bean.setAppkey(fields[2]);
        bean.setIp(fields[3]);
        bean.setSelfDuration(Long.parseLong(fields[4]));
        bean.setThirdPartDuration(Long.parseLong(fields[5]));
        bean.setStatus(fields[6]);

        k.set(appkey);
        context.write(k, bean); //shuffle开始时会根据k的hashcode值进行分区，但是结合我们自己的业务，默认hash分区方式不能满足需求
    }
}

### --- PartitionBean

package com.yanqi.mr.partition;

import org.apache.hadoop.io.Writable;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

public class PartitionBean implements Writable {

    //准备一个空参构造


    public PartitionBean() {
    }

    public PartitionBean(String id, String deviceId, String appkey, String ip, Long selfDuration, Long thirdPartDuration, String status) {
        this.id = id;
        this.deviceId = deviceId;
        this.appkey = appkey;
        this.ip = ip;
        this.selfDuration = selfDuration;
        this.thirdPartDuration = thirdPartDuration;
        this.status = status;
    }

    //序列化方法
    @Override
    public void write(DataOutput out) throws IOException {
        out.writeUTF(id);
        out.writeUTF(deviceId);
        out.writeUTF(appkey);
        out.writeUTF(ip);
        out.writeLong(selfDuration);
        out.writeLong(thirdPartDuration);
        out.writeUTF(status);

    }

    //反序列化方法  要求序列化与反序列化字段顺序要保持一致
    @Override
    public void readFields(DataInput in) throws IOException {
        this.id = in.readUTF();
        this.deviceId = in.readUTF();
        this.appkey = in.readUTF();
        this.ip = in.readUTF();
        this.selfDuration = in.readLong();
        this.thirdPartDuration = in.readLong();
        this.status = in.readUTF();

    }

    //定义属性
    private String id;//日志id
    private String deviceId;//设备id
    private String appkey;//appkey厂商id
    private String ip;//ip地址
    private Long selfDuration;//自有内容播放时长
    private Long thirdPartDuration;//第三方内容时长
    private String status;//状态码

    public String getId() {
        return id;
    }

    public void setId(String id) {
        this.id = id;
    }

    public String getDeviceId() {
        return deviceId;
    }

    public void setDeviceId(String deviceId) {
        this.deviceId = deviceId;
    }

    public String getAppkey() {
        return appkey;
    }

    public void setAppkey(String appkey) {
        this.appkey = appkey;
    }

    public String getIp() {
        return ip;
    }

    public void setIp(String ip) {
        this.ip = ip;
    }

    public Long getSelfDuration() {
        return selfDuration;
    }

    public void setSelfDuration(Long selfDuration) {
        this.selfDuration = selfDuration;
    }

    public Long getThirdPartDuration() {
        return thirdPartDuration;
    }

    public void setThirdPartDuration(Long thirdPartDuration) {
        this.thirdPartDuration = thirdPartDuration;
    }

    public String getStatus() {
        return status;
    }

    public void setStatus(String status) {
        this.status = status;
    }

//方便文本中的数据易于观察，重写toString()方法

    @Override
    public String toString() {
        return id + '\t' +
                "\t" + deviceId + '\t' + appkey + '\t' +
                ip + '\t' +
                selfDuration +
                "\t" + thirdPartDuration +
                "\t" + status;
    }
}

### --- CustomPartitioner

package com.yanqi.mr.partition;

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

//Partitioner分区器的泛型是map输出的kv类型
public class CustomPartitioner extends Partitioner<Text, PartitionBean> {
    @Override
    public int getPartition(Text text, PartitionBean partitionBean, int numPartitions) {
        int partition = 0;

        if (text.toString().equals("kar")) {
            //只需要保证满足此if条件的数据获得同个分区编号集合
            partition = 0;
        } else if (text.toString().equals("pandora")) {
            partition = 1;
        } else {
            partition = 2;
        }
        return partition;
    }
}

### --- PartitionReducer

package com.yanqi.mr.partition;

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

import java.io.IOException;

//reduce输入类型：Text,PartitionBean,输出：Text,PartitionBean
public class PartitionReducer extends Reducer<Text, PartitionBean, Text, PartitionBean> {
    @Override
    protected void reduce(Text key, Iterable<PartitionBean> values, Context context) throws IOException, InterruptedException {
        //无需聚合运算，只需要进行输出即可
        for (PartitionBean bean : values) {
            context.write(key, bean);
        }
    }
}

### --- PartitionDriver

package com.yanqi.mr.partition;

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 PartitionDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {

        //1 获取配置文件
        final Configuration conf = new Configuration();
        //2 获取job实例
        final Job job = Job.getInstance(conf);
        //3 设置任务相关参数
        job.setJarByClass(PartitionDriver.class);
        job.setMapperClass(PartitionMapper.class);
        job.setReducerClass(PartitionReducer.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(PartitionBean.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(PartitionBean.class);

        // 4 设置使用自定义分区器
        job.setPartitionerClass(CustomPartitioner.class);
        //5 指定reducetask的数量与分区数量保持一致,分区数量是3
        job.setNumReduceTasks(3); //reducetask不设置默认是1个
//        job.setNumReduceTasks(5);
//        job.setNumReduceTasks(2);
        // 6 指定输入和输出数据路径
        FileInputFormat.setInputPaths(job, new Path("e:/speak.data"));
        FileOutputFormat.setOutputPath(job, new Path("e:/parition/out"));
        // 7 提交任务
        final boolean flag = job.waitForCompletion(true);

        System.exit(flag ? 0 : 1);

    }
}

三、运行程序

### --- 运行程序

~~~     配置input数据参数
~~~     打印输出

四、总结

### --- 总结

~~~     自定义分区器时最好保证分区数量与reduceTask数量保持一致；
~~~     如果分区数量不止1个，但是reduceTask数量1个，此时只会输出一个文件。
~~~     如果reduceTask数量大于分区数量，但是输出多个空文件
~~~     如果reduceTask数量小于分区数量，有可能会报错。

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Walter Savage Landor:strove with none,for none was worth my strife.Nature I loved and, next to Nature, Art:I warm'd both hands before the fire of life.It sinks, and I am ready to depart

                                                                                                                                                   ——W.S.Landor

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