hdfs环境下使用MapReduce求出各年销售笔数、各年销售总额（java编译）

使用Intellij IDEA编辑java程序，打包成jar后在linux的hdfs环境下运行

1、首先在pom.xml里导入依赖：

      org.apache.hadoop
      hadoop-common
      2.7.3
    
    
      org.apache.hadoop
      hadoop-hdfs
      2.7.3
    
    
      org.apache.hadoop
      hadoop-client
      2.7.3
    

2、程序代码：
（1）Javabean类Sold.java

package com.mystudy;

import org.apache.hadoop.io.Writable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
public class Sold implements Writable {
//字段名 prod_id,cust_id,time,channel_id,promo_id,quantity_sold,amount_sold
    //数据类型：Int，Int，Date， Int，Int ，Int ,float(10,2),
    //数据: 13, 987, 1998/1/10, 3, 999,1, 1232.16
//由以上定义变量
    private int prod_id;
    private int cust_id;
    private String time;
    private int channel_id;
    private int promo_id;
    private int quantity_sold;
    private float amount_sold;//奖金


    //序列化方法：将java对象转化为可跨机器传输数据流（二进制串/字节）的一种技术
    public void write(DataOutput out) throws IOException {
        out.writeInt(this.prod_id);
        out.writeInt(this.cust_id);
        out.writeUTF(this.time);
        out.writeInt(this.channel_id);
        out.writeInt(this.promo_id);
        out.writeInt(this.quantity_sold);
        out.writeFloat(this.amount_sold);

    }
    //反序列化方法：将可跨机器传输数据流（二进制串）转化为java对象的一种技术
    public void readFields(DataInput in) throws IOException {
        this.prod_id = in.readInt();
        this.cust_id = in.readInt();
        this.time = in.readUTF();
        this.channel_id = in.readInt();
        this.promo_id = in.readInt();
        this.quantity_sold = in.readInt();
        this.amount_sold = in.readFloat();
    }

    public int getProd_id() {
        return prod_id;
    }
    public void setProd_id(int prod_id) {
        this.prod_id = prod_id;
    }
    public int getCust_id() {
        return cust_id;
    }
    public void setCust_id(int cust_id) {
        this.cust_id = cust_id;
    }
    public String getTime() {
        return time;
    }
    public void setTime(String time) {
        this.time = time;
    }
    public int getChannel_id() {
        return channel_id;
    }
    public void setChannel_id(int channel_id) {
        this.channel_id = channel_id;
    }
    public int getPromo_id() {
        return promo_id;
    }
    public void setPromo_id(int promo_id) {
        this.promo_id = promo_id;
    }
    public int getQuantity_sold() {
        return quantity_sold;
    }
    public void setQuantity_sold(int quantity_sold) {
        this.quantity_sold = quantity_sold;
    }
    public float getAmount_sold() {
        return amount_sold;
    }
    public void setAmount_sold(float amount_sold) {
        this.amount_sold = amount_sold;
    }

}

 （2）Mapper类SoldMapper.java

package com.mystudy;

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class SoldMapper extends Mapper {
    @Override
    protected void map(LongWritable k1, Text v1,
                       Context context)
            throws IOException, InterruptedException {
        //字段名 prod_id,cust_id,time,channel_id,promo_id,quantity_sold,amount_sold
        //数据类型：Int，Int，Date， Int，Int ，Int ,float(10,2),
        //数据: 13,987,1998-01-10,3,999,1,1232.16
        String data = v1.toString();
        String[] words = data.split(",");
//数据: t1=987,1998-01-10,3,999,1,1232.16
        String t1 = StringUtils.substringAfter(data, ",");
//数据: t2=1998-01-10,3,999,1,1232.16 
        String t2 = StringUtils.substringAfter(t1, ",");
//取年份为偏移量，数据: words2[0]=1998，words2[1]=01，words2[2]=10,3,999,1,1232.16
        String[] words2 = t2.split("-");
//        StringUtils.substringAfter("dskeabcedeh", "e");
//        /*结果是：abcedeh*/
        Sold sold = new Sold();
        sold.setTime(words[2]);//数组word[]
        sold.setQuantity_sold(Integer.parseInt(words[5]));
        sold.setAmount_sold(Float.valueOf(words[6]));
        context.write(new Text(words2[0]), sold);//数组word2[],word2[0]代表年份作为k2
    }
}

（3）Reduce类SoldReduce.java

package com.mystudy;

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

import java.io.IOException;

public class SoldReduce extends Reducer {
    protected void reduce(Text k3, Iterable v3, Context context) throws IOException, InterruptedException {
        int total1 = 0;
        float total2 = 0;

        for (Sold sold : v3) {
            total1 = total1 + sold.getQuantity_sold();
            total2 = total2 + sold.getAmount_sold();
        }
        String total = "销售笔数:" + Integer.toString(total1) + "," + "销售总额:" + Float.toString(total2);
        context.write(k3, new Text(total));
    }
}

（4）主类SoldMain.java

package com.mystudy;

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;

public class SoldMain {
    public static void main(String[] args) throws Exception {
        //1. 创建一个job和任务入口(指定主类)
        Job job = Job.getInstance(new Configuration());
        job.setJarByClass(SoldMain.class);

        //2. 指定job的mapper和输出的类型
        job.setMapperClass(SoldMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Sold.class);

        //3. 指定job的reducer和输出的类型
        job.setReducerClass(SoldReduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        //4. 指定job的输入和输出路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        //5. 执行job
        job.waitForCompletion(true);
    }
}

3、在Telminal里运行mvn clean package，将程序打包好，再复制进linux里，创建合适的输入输出文件夹，和相对应Sold类的文件，例如通过以下命令运行：
hadoop jar WordCount/WordCount4-1.0-SNAPSHOT.jar /204/input/sales.csv /204/output/sales

4、运行结果：