Mapreduce对csv文件数据进行价格排序处理

1、总体思路

（1）首先将要分析的csv文件对象price和id，定义成String类型，因为MapReduce的输入和输出都是k，v键值对的形式。

@Override
    protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {

        for (CsvBean value : values) {

            context.write(key,value);
        }
    }

（2）所以我们这里将price封装成一个对象，将price的对象属性按照csv文件进行设置。

//4 封装到对象
outV.setId(id);
outV.setAge(price);

outK.set(price);

（3）封装了对象后，我们需要对定义输入和输出的类型，这里用的是重写序列化方法以及重写反序列化方法。

重写序列化方法：writeUTF方法

@Override
    public void write(DataOutput dataOutput) throws IOException {

        dataOutput.writeUTF(id);
        dataOutput.writeUTF(price);
    }

重写反序列化方法：readUTF方法

@Override
    public void readFields(DataInput dataInput) throws IOException {

        this.id = dataInput.readUTF();
        this.price = dataInput.readUTF();
    }

（4）把结果显示在文件中，重写 toString()，这里用 "," 隔开显示。

@Override
    public String toString() {
        return "," + id ;
    }

（5）最后我们设置csv文件的输入输出路径（这里的路径设置可以改为自己放置csv文件的位置）

//6	设置输入输出路径
FileInputFormat.setInputPaths(job,new Path("D:\IDEA\mapreduce\steam\input\steam.csv"));
FileOutputFormat.setOutputPath(job,new Path("D:\IDEA\mapreduce\steam\output"));

2、代码展示

（1）封装对象CsvBean

package com.gis507.test.CsvSplit;

import org.apache.hadoop.io.Writable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

public class CsvBean implements Writable{

    private String id;
    private String price;

    public CsvBean() {

    }

    public CsvBean(String id, String age) {

        this.id = id;
        this.price = age;
    }

    public String getId() {
        return id;
    }
    public void setId(String id) {
        this.id = id;
    }
    public String getAge() {
        return price;
    }
    public void setAge(String age) {
        this.price = age;
    }

    @Override
    public String toString() {
        return "," + id ;
    }

    @Override
    public void write(DataOutput dataOutput) throws IOException {

        dataOutput.writeUTF(id);
        dataOutput.writeUTF(price);
    }

    @Override
    public void readFields(DataInput dataInput) throws IOException {

        this.id = dataInput.readUTF();
        this.price = dataInput.readUTF();
    }
}

（2）Mapper类，把id和price读进来

package com.gis507.test.CsvSplit;

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

import java.io.IOException;

public class CsvSplitMapper extends Mapper {

    private Text outK = new Text();
    private CsvBean outV = new CsvBean();

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

        // 1获取一行转为String
        String line = value.toString();

        //2 按照逗号分割
        String[] csvComments = line.split(",");

        //3 获取需要的值
        String id = csvComments[0];
        String price = csvComments[2];

        //4 封装到对象
        outV.setId(id);
        outV.setAge(price);

        outK.set(price);

        //5 写出
        context.write(outK,outV);

    }
}

（3）Reducer类

package com.gis507.test.CsvSplit;

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

import java.io.IOException;

public class CsvSplitReducer extends Reducer {

    @Override
    protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {

        for (CsvBean value : values) {

            context.write(key,value);
        }
    }
}

（4）定义输入输出函数CsvSplitDriver

package com.gis507.test.CsvSplit;

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

        //1 获取job对象
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        //2 关联Driver类
        job.setJarByClass(CsvSplitDriver.class);

        //3 关联Mapper和Reducer类
        job.setMapperClass(CsvSplitMapper.class);
        job.setReducerClass(CsvSplitReducer.class);

        //4 设置Map的输入输出类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(CsvBean.class);

        //5	设置最终的输入输出类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(CsvBean.class);

        //6	设置输入输出路径
        FileInputFormat.setInputPaths(job,new Path("D:\IDEA\mapreduce\steam\input\steam.csv"));
        FileOutputFormat.setOutputPath(job,new Path("D:\IDEA\mapreduce\steam\output"));

        //7 提交job
        boolean result = job.waitForCompletion(true);
        System.exit(result?0:1);
    }
}

3、查看输出结果

4、项目源代码

Mapreduce对csv文件数据进行价格排序处理