本篇文章给大家分享的是有关MapReduce中怎么实现自定义排序功能，小编觉得挺实用的，因此分享给大家学习，希望大家阅读完这篇文章后可以有所收获，话不多说，跟着小编一起来看看吧。
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{ 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：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，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 {
@Override
protected void reduce(Person key, Iterable 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
以上就是MapReduce中怎么实现自定义排序功能，小编相信有部分知识点可能是我们日常工作会见到或用到的。希望你能通过这篇文章学到更多知识。更多详情敬请关注创新互联-成都网站建设公司行业资讯频道。