Hadoop之MapReduce-自定义排序编程

一、问题描述

        根据给出的数据计算每一个账户总的收入，总的支出以及总利润，并按照总利润由高到低排序，如果总利润相同，则按照总的支出由低到高排序。

二、数据格式

       2.1输入数据格式

       zhangsan@163.com    6000    0    2014-02-20

       zhangsan@163.com    0    1000    2014-02-20

       lisi@163.com    2000    1000    2014-02-20

       lisi@163.com    10000    9000    2014-02-20

       beibei@126.com    100    0    2014-02-20

       wangwu@163.com    6000    2000    2014-02-20

       2.2输出数据格式 

       zhangsan@163.com    6000.0    1000.0    5000.0

       wangwu@163.com    6000.0    2000.0    4000.0

       lisi@163.com    12000.0    10000.0    2000.0

       beibei@126.com    100.0    0.0    100.0

三、问题实现

        第一步：将每个账户的总的收入，总的支出以及总利润计算输出到HDFS。【默认按照数据字典排序】

        第二步：将输出的结果自定义排序。

        类InforBean

package edu.jianwei.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 profit;

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

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

/**
* serialize
*/
public void write(DataOutput out) throws IOException {
out.writeUTF(account);
out.writeDouble(income);
out.writeDouble(expenses);
out.writeDouble(profit);
}

/**
* deserialize
*/
public void readFields(DataInput in) throws IOException {
this.account = in.readUTF();
this.income = in.readDouble();
this.expenses = in.readDouble();
this.profit = in.readDouble();
}

public int compareTo(InfoBean o) {
        if (this.profit == o.getProfit()) {
            return this.expenses > o.getExpenses() ? 1 : -1;
        } else {
            return this.profit > o.getProfit() ? -1 : 1;
        }
        }

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 getProfit() {
return profit;
}

public void setProfit(double profit) {
this.profit = profit;
}

}

         类SumStep:

package edu.jianwei.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 {
static class SumMapper extends Mapper<LongWritable, Text, Text, InfoBean> {
public Text k = new Text();
public InfoBean v = new InfoBean();

@Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String line = value.toString();
String[] str = line.split("\t");
String account = str[0];
double income = Double.parseDouble(str[1]);
double expenses = Double.parseDouble(str[2]);
k.set(account);
v.set(account, income, expenses);
context.write(k, v);
}

}

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

public InfoBean v = new InfoBean();

@Override
protected void reduce(Text key, Iterable<InfoBean> values,
Context context) throws IOException, InterruptedException {
double total_inclome = 0;
double total_expenses = 0;
for (InfoBean v : values) {
total_inclome += v.getIncome();
total_expenses += v.getExpenses();
}
v.set(null, total_inclome, total_expenses);
context.write(key, v);
}
}

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

}

        

        类SortStep:

package edu.jianwei.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 void main(String[] args) throws IOException,
InterruptedException, ClassNotFoundException {
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);
}

public static class SortMapper extends
Mapper<LongWritable, Text, InfoBean, NullWritable> {
public InfoBean k = new InfoBean();

@Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String line = value.toString();
String[] strs = line.split("\t");
String account = strs[0];
double income = Double.parseDouble(strs[1]);
double expenses = Double.parseDouble(strs[2]);
k.set(account, income, expenses);
context.write(k, NullWritable.get());
}
}

public static class SortReducer extends
Reducer<InfoBean, NullWritable, Text, InfoBean> {
public Text k = new Text();

@Override
protected void reduce(InfoBean bean, Iterable<NullWritable> v2s,
Context context) throws IOException, InterruptedException {
String account = bean.getAccount();
k.set(account);
context.write(k, bean);
}

}

}

四、输出结果

        第一步：将每个账户的总的收入，总的支出以及总利润计算输出到HDFS。     

        1.代码运行 

          hadoop jar /root/sort.jar edu.jianwei.hadoop.mr.sort.SumStep /sort  /sort/sum

        2.输出结果

        beibei@126.com    100.0    0.0    100.0

        lisi@163.com    12000.0    10000.0    2000.0

        wangwu@163.com    6000.0    2000.0    4000.0

        zhangsan@163.com    6000.0    1000.0    5000.0

        第二步：将输出的结果自定义排序。

       1.代码运行 

         hadoop jar /root/sort.jar edu.jianwei.hadoop.mr.sort.SortStep /sort/sum  /sort/sortRes、

        2.输出结果

        zhangsan@163.com    6000.0    1000.0    5000.0

        wangwu@163.com    6000.0    2000.0    4000.0

        lisi@163.com    12000.0    10000.0    2000.0

        beibei@126.com    100.0    0.0    100.0