Road Hadoop learning (7) MapReduce custom sorting

This paper test text:

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

   MapReduce in, according to key partition, sorting, grouping,
MapReduce will be sorted according to the corresponding key basic types, such as int type IntWritable, long type LongWritable, Text type, default ascending sort
   Why custom collation? Existing requirements, need to customize the key type, and the self-ordering rules defined key, such as in descending order according to the person's salary, if yes, then ascending order of age
in Example Text Type:




Text class implements the WritableComparableinterface and there write(), readFields()and compare()methods
readFields()method: to deserialize
write()method: serialization operation for
so in order to customize the type of sorting method described above requires a
custom class code :

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 program:

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());
        }
    }
}

operation result:

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