hadoop排序

目的：用hadoop做一个简单的排序项目

准备文件

目的文件trade_info.txt

文件含义

以上文件可以理解为某淘宝用户的收入与支出情况（简单理解）
第一列：淘宝账号
第二列：某一订单的收入
第三列：某一订单的支出（退款情况）
第四列：订单时间

排序规则

对商家的总收入和总支出进行求和
按照商家的总收入从高到低进行排序，总收入相同时，支出少的商家排在前面。

项目分析

此项目中，先要对商家所有的订单进行求和，然后再进行排序。显然，要进行两次的mapreduce。第一次mapreduce的结果作为第二次mapreduce的输入。
思路

1、第一次map输入的key、value类型为LongWritable和Text,输出的key、value类型为Text和收入和支出（这里封装成InfoBean对象）
2、第一次reduce的输入为第一次map的输出，唯一不同的是reduce的value是迭代的，输出的key、value类型为Text和InfoBean.
3、第一次mapreduce主要是完成订单求和功能（具体项目中是业务需求），第二次mapreduce主要是完成排序功能。
4、排序规则为按照收入的高低进行排序，所以第二次map的key即为InfoBean(收入情况封装在InfoBean中)，value为NullWritable。
5、第二次reduce输出的key、value即为Text和InfoBean.下面直接上代码。

代码

InfoBean.java：封装

package cn.master.hadoop.mr.sort;

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

import org.apache.hadoop.io.WritableComparable;

public class InfoBean implements WritableComparable<InfoBean>{

    private String account;

    private double income;

    private double expenses;

    private double surplus;

    public void set(String account,double income,double expenses){
        this.account = account;
        this.income = income;
        this.expenses = expenses;
    }

    public void write(DataOutput out) throws IOException {
        // TODO Auto-generated method stub
        out.writeUTF(account);
        out.writeDouble(income);
        out.writeDouble(expenses);
        out.writeDouble(surplus);
    }

    public void readFields(DataInput in) throws IOException {
        // TODO Auto-generated method stub
        this.account = in.readUTF();
        this.income = in.readDouble();
        this.expenses = in.readDouble();
        this.surplus = in.readDouble();
    }

    public int compareTo(InfoBean o) {
        // TODO Auto-generated method stub
        if(this.income == o.getIncome()){
            return this.expenses > o.getExpenses() ? 1 :-1;
        }else {
            return this.income > o.getIncome() ? -1 : 1;
        }
    }

    @Override
    public String toString() {
        return this.income +  "\t" + this.expenses + "\t" + this.surplus;
    }

    public String getAccount() {
        return account;
    }

    public void setAccount(String account) {
        this.account = account;
    }

    public double getIncome() {
        return income;
    }

    public void setIncome(double income) {
        this.income = income;
    }

    public double getExpenses() {
        return expenses;
    }

    public void setExpenses(double expenses) {
        this.expenses = expenses;
    }

    public double getSurplus() {
        return surplus;
    }

    public void setSurplus(double surplus) {
        this.surplus = surplus;
    }


}

SumStep.java：完成订单求和

package cn.master.hadoop.mr.sort;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class SumStep {

    public static class SumMapper extends Mapper<LongWritable, Text, Text, InfoBean>{

        private Text k = new Text();
        private InfoBean v = new InfoBean();
        @Override
        protected void map(LongWritable key, Text value, Context context)
                throws IOException, InterruptedException {
            //接收数据
            String line = value.toString();
            String[] fileds = line.split("\t");
            String account = fileds[0];
            double in = Double.parseDouble(fileds[1]);
            double out = Double.parseDouble(fileds[2]);
            k.set(account);
            v.set(account, in, out);
            context.write(k, v);
        }

    }

    public static class SumReducer extends Reducer<Text, InfoBean,Text, InfoBean>{

        private InfoBean v = new InfoBean();
        @Override
        protected void reduce(Text key, Iterable values,Context context)
                throws IOException, InterruptedException {
            double in_sum = 0;
            double out_sum = 0;
            for (InfoBean infoBean : values) {
                in_sum += infoBean.getIncome();
                out_sum +=infoBean.getExpenses();
            }
            v.set("", in_sum, out_sum);
            context.write(key, v);
        }
    }

    public static void main(String[] args) throws Exception{
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        job.setJarByClass(SumStep.class);

        job.setMapperClass(SumMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(InfoBean.class);
        FileInputFormat.setInputPaths(job, new Path(args[0]));


        job.setReducerClass(SumReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(InfoBean.class);
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        job.waitForCompletion(true);
    }

}

SortStep.java:排序

package cn.master.hadoop.mr.sort;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class SortStep {

    public static class SortMapper extends Mapper{

        private InfoBean k = new InfoBean();
        @Override
        protected void map(LongWritable key, Text value,
                Context context)
                throws IOException, InterruptedException {
            //接收数据
            String line = value.toString();
            String[] fileds = line.split("\t");
            String account = fileds[0];
            double in = Double.parseDouble(fileds[1]);
            double out = Double.parseDouble(fileds[2]);
            k.set(account, in, out);
            context.write(k, NullWritable.get());
        }
    }

    public static class SortReducer extends Reducer{

        private Text k = new Text();
        @Override
        protected void reduce(InfoBean bean, Iterable values,
                Context context) throws IOException, InterruptedException {
            String acount = bean.getAccount();
            k.set(acount);
            context.write(k, bean);
        }

    }

    public static void main(String[] args) throws Exception {

        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        job.setJarByClass(SortStep.class);

        job.setMapperClass(SortMapper.class);
        job.setMapOutputKeyClass(InfoBean.class);
        job.setMapOutputValueClass(NullWritable.class);
        FileInputFormat.setInputPaths(job, new Path(args[0]));

        job.setReducerClass(SortReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(InfoBean.class);
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        job.waitForCompletion(true);

    }

}

运行jar包

将项目打成jar包后，在hadoop环境下运行。
命令 hadoop jar /路径（jar包的路径） 主程序入口 源文件 生成文件

hadoop jar /root/mrc.jar cn.master.hadoop.mr.sort.SumStep /trade_info.txt /sum
hadoop jar /root/mrc.jar cn.master.hadoop.mr.sort.SortStep /sum /sort

查看

hadoop fs -ls /sort
hadoop fs -cat /sort/part-r-00000