WritableComparable 案例 全排序

一、需求分析

1、需求

按照流量降序排序

2、分析

a、原文件的总流量是value，排序是按照key进行排序的，因此需要把 value -> key

b、自定义Hadoop序列化类，(需要有排序功能) 实现 WritableComparable

二、代码

1、自定义Hadoop序列化，实现WritableComparable

package com.sort;

import org.apache.hadoop.io.WritableComparable;

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

public class FlowBean implements WritableComparable<FlowBean> {
    private long upFlow;
    private long downFlow;
    private long sumFlow;

    public FlowBean() {
    }

    // 排序
    public int compareTo(FlowBean bean) {
        int result;
        if (this.sumFlow > bean.getSumFlow()){
            result = -1;
        }else if (this.sumFlow < bean.getSumFlow()){
            result = 1;
        }else {
            result = 0;
        }
        return result;
    }
    // 序列化
    public void write(DataOutput out) throws IOException {
        out.writeLong(upFlow);
        out.writeLong(downFlow);
        out.writeLong(sumFlow);
    }
    // 反序列化
    public void readFields(DataInput in) throws IOException {
        this.upFlow = in.readLong();
        this.downFlow = in.readLong();
        this.sumFlow = in.readLong();
    }

    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;
    }

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

2、Mapper

package com.sort;

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

import java.io.IOException;

public class SortMapper extends Mapper<LongWritable, Text, FlowBean, Text> {
    FlowBean k = new FlowBean();
    Text v = new Text();

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        // 13509468723    7335    110349    117684
        // 1. 读取一行数据
        String line = value.toString();
        String[] words = line.split("	");
        // 2.设置 key
        k.setUpFlow(Long.parseLong(words[1]));
        k.setDownFlow(Long.parseLong(words[2]));
        k.setSumFlow(Long.parseLong(words[3]));
        // 3.设置 value
        v.set(words[0]);
        // 4.写入
        context.write(k, v);
    }
}

注意：需要把FlowBean 作为输出的 Key，Text作为输出的 Value

3、Reducer

package com.sort;

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

import java.io.IOException;

public class SortReducer extends Reducer<FlowBean, Text,Text,FlowBean> {
    @Override
    protected void reduce(FlowBean key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
        // 1. 循环写入
        for (Text value : values) {
            context.write(value, key);
        }
    }
}

注意：Values含有一个数据，但为了以防万一，使用for循环遍历

4、Driver

package com.sort;

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 SortDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        args = new String[]{"E:\a\output", "E:\a\output1"};
        // 1.获取job
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        // 2.设置jar
        job.setJarByClass(SortDriver.class);
        // 3.关联mapper和reducer
        job.setMapperClass(SortMapper.class);
        job.setReducerClass(SortReducer.class);
        // 4.设置mapper输出的k v
        job.setMapOutputKeyClass(FlowBean.class);
        job.setMapOutputValueClass(Text.class);
        // 5.设置整体输出的 k, v
        job.setOutputKeyClass(Text.class);
        job.setOutputKeyClass(FlowBean.class);
        // 6.设置输入输出路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        // 7.提交job
        boolean wait = job.waitForCompletion(true);
        System.exit(wait? 0: 1);
    }
}