Hadoop学习之路(7)MapReduce自定义排序

本文测试文本：

tom 20 8000
nancy 22 8000
ketty 22 9000
stone 19 10000
green 19 11000
white 39 29000
socrates 30 40000

   MapReduce中，根据key进行分区、排序、分组
MapReduce会按照基本类型对应的key进行排序，如int类型的IntWritable，long类型的LongWritable,Text类型，默认升序排序
   为什么要自定义排序规则？现有需求，需要自定义key类型，并自定义key的排序规则，如按照人的salary降序排序，若相同，则再按age升序排序
以Text类型为例：




Text类实现了WritableComparable接口，并且有write()、readFields()和compare()方法
readFields()方法：用来反序列化操作
write()方法：用来序列化操作
所以要想自定义类型用来排序需要有以上的方法
自定义类代码：

import org.apache.hadoop.io.WritableComparable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
public class Person implements WritableComparable<Person> {
    private String name;
    private int age;
    private int salary;
    public Person() {
    }
    public Person(String name, int age, int salary) {
        //super();
        this.name = name;
        this.age = age;
        this.salary = salary;
    }
    public String getName() {
        return name;
    }
    public void setName(String name) {
        this.name = name;
    }
    public int getAge() {
        return age;
    }
    public void setAge(int age) {
        this.age = age;
    }
    public int getSalary() {
        return salary;
    }
    public void setSalary(int salary) {
        this.salary = salary;
    }
    @Override
    public String toString() {
        return this.salary + "  " + this.age + "    " + this.name;
    }
    //先比较salary，高的排序在前；若相同，age小的在前
    public int compareTo(Person o) {
        int compareResult1= this.salary - o.salary;
        if(compareResult1 != 0) {
            return -compareResult1;
        } else {
            return this.age - o.age;
        }
    }
    //序列化，将NewKey转化成使用流传送的二进制
    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeUTF(name);
        dataOutput.writeInt(age);
        dataOutput.writeInt(salary);
    }
    //使用in读字段的顺序，要与write方法中写的顺序保持一致
    public void readFields(DataInput dataInput) throws IOException {
        //read string
        this.name = dataInput.readUTF();
        this.age = dataInput.readInt();
        this.salary = dataInput.readInt();
    }

}

MapReuduce程序：

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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;
import java.io.IOException;
import java.net.URI;
public class  SecondarySort {
	public static void main(String[] args) throws Exception {
		System.setProperty("HADOOP_USER_NAME","hadoop2.7");
		Configuration configuration = new Configuration();
        //设置本地运行的mapreduce程序 jar包
        configuration.set("mapreduce.job.jar","C:\\Users\\tanglei1\\IdeaProjects\\Hadooptang\\target\\com.kaikeba.hadoop-1.0-SNAPSHOT.jar");
		Job job = Job.getInstance(configuration, SecondarySort.class.getSimpleName());
		FileSystem fileSystem = FileSystem.get(URI.create(args[1]), configuration);
		if (fileSystem.exists(new Path(args[1]))) {
			fileSystem.delete(new Path(args[1]), true);
		}
		FileInputFormat.setInputPaths(job, new Path(args[0]));
		job.setMapperClass(MyMap.class);
		job.setMapOutputKeyClass(Person.class);
		job.setMapOutputValueClass(NullWritable.class);
		//设置reduce的个数
		job.setNumReduceTasks(1);
		job.setReducerClass(MyReduce.class);
		job.setOutputKeyClass(Person.class);
		job.setOutputValueClass(NullWritable.class);
		FileOutputFormat.setOutputPath(job, new Path(args[1]));
		job.waitForCompletion(true);
	}
	public static class MyMap extends
			Mapper<LongWritable, Text, Person, NullWritable> {
		//LongWritable：输入参数键类型，Text：输入参数值类型
		//Persion:输出参数键类型，NullWritable：输出参数值类型
		@Override
		//map的输出值是键值对<K,V>，NullWritable说关心V的值
		protected void map(LongWritable key, Text value,
				Context context)
				throws IOException, InterruptedException {
			//LongWritable key：输入参数键值对的键，Text value：输入参数键值对的值
			//获得一行数据，输入参数的键（距首行的位置）,Hadoop读取数据的时候逐行读取文本
			//fields:代表着文本一行的的数据
			String[] fields = value.toString().split(" ");
			// 本列中文本一行数据：nancy 22 8000
			String name = fields[0];
			//字符串转换成int
			int age = Integer.parseInt(fields[1]);
			int salary = Integer.parseInt(fields[2]);
			//在自定义类中进行比较
			Person person = new Person(name, age, salary);
			context.write(person, NullWritable.get());
		}
	}
	public static class MyReduce extends
			Reducer<Person, NullWritable, Person, NullWritable> {
		@Override
		protected void reduce(Person key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
			context.write(key, NullWritable.get());
		}
	}
}

运行结果：

40000  30    socrates
29000  39    white
11000  19    green
10000  19    stone
9000  22    ketty
8000  20    tom
8000  22    nancy