MapReduce04——自定义排序之倒序

1、有words.txt文件内容如下，其中以制表符分割

1	Smith
3	Alice
2	Tom
4	Tony

2、分析
（1）、定义实体类实现WritableComparable接口，重写compareTo方法

package com.qujiuge.sort_;

import org.apache.hadoop.io.WritableComparable;

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

public class Bean implements WritableComparable<Bean> {
     
    private int id;
    private String name;

    public int getId() {
     
        return id;
    }

    public void setId(int id) {
     
        this.id = id;
    }

    public String getName() {
     
        return name;
    }

    public void setName(String name) {
     
        this.name = name;
    }

    @Override
    public void write(DataOutput dataOutput) throws IOException {
     
        dataOutput.writeInt(id);
        dataOutput.writeUTF(name);
    }

    @Override
    public void readFields(DataInput dataInput) throws IOException {
     
        id = dataInput.readInt();
        name = dataInput.readUTF();
    }

    @Override
    public String toString() {
     
        return id + "\t" + name;
    }

    @Override
    public int compareTo(Bean o) {
     
        return this.id > o.getId() ? -1 : 1;
    }
}

（2）、map阶段
（2.1）、mapreduce逐行读取文件，得到每行的值
（2.2）、以制表符分割后，将满足条件的数据封装到bean中
（3）、reduce阶段
（1）、直接输出
3、创建maven工程后，添加如下依赖

<dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>2.9.2</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>2.9.2</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-core</artifactId>
            <version>2.9.2</version>
        </dependency>
    </dependencies>

4、编写mapreduce程序

package com.qujiuge.sort_;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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;

import java.io.IOException;

public class Driver {
     
    static class SortMapper extends Mapper<LongWritable, Text, Bean, NullWritable> {
     
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
     
            String line = value.toString();
            String[] fields = line.split("\t");
            if (fields.length == 2) {
     
                Bean bean = new Bean();
                int id = Integer.parseInt(fields[0]);
                String name = fields[1];
                bean.setId(id);
                bean.setName(name);
                context.write(bean, NullWritable.get());
            }
        }
    }

    static class SortReducer extends Reducer<Bean, NullWritable, Bean, NullWritable> {
     
        @Override
        protected void reduce(Bean key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
     
            context.write(key, values.iterator().next());
        }
    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
     
        Configuration config = new Configuration();
        Job job = Job.getInstance(config);

        job.setJarByClass(Driver.class);

        job.setMapperClass(SortMapper.class);
        job.setReducerClass(SortReducer.class);

        job.setMapOutputKeyClass(Bean.class);
        job.setMapOutputValueClass(NullWritable.class);

        job.setOutputKeyClass(Bean.class);
        job.setOutputValueClass(NullWritable.class);

        FileSystem fs = FileSystem.get(config);
        Path outPath = new Path(args[1]);
        if (fs.exists(outPath)) {
     
            fs.delete(outPath, true);
        }
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, outPath);

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

项目结构如下：

5、将words.txt文件上传到hdfs中

6、将项目打包成jar文件后，用hadoop jar命令执行

运行结果：