大数据笔记（八）——Mapreduce的高级特性（A）

一.序列化

类似于Java的序列化：将对象——>文件

如果一个类实现了Serializable接口，这个类的对象就可以输出为文件

同理，如果一个类实现了的Hadoop的序列化机制（接口：Writable），这个类的对象就可以作为输入和输出的值

例子：使用序列化  求每个部门的工资总额

数据：在map阶段输出k2部门号 v2是Employee对象

           reduce阶段：k4部门号 v3.getSal()得到薪水求和——>v4

Employee.java：封装的员工属性

package saltotal;

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

import org.apache.hadoop.io.Writable;

//定义员工的属性: 7654,MARTIN,SALESMAN,7698,1981/9/28,1250,1400,30
public class Employee implements Writable{

    private int empno;//员工号
    private String ename;//员工姓名
    private String job;//ְ职位
    private int mgr;//经理的员工号
    private String hiredate;//入职日期
    private int sal;//月薪
    private int comm;//奖金
    private int deptno;// 部门号
    
    @Override
    public String toString() {
        return "["+this.empno+"	"+this.ename+"	"+this.sal+"	"+this.deptno+"]";
    }

    @Override
    public void write(DataOutput output) throws IOException {
        // 代表序列化过程：输出
        output.writeInt(this.empno);
        output.writeUTF(this.ename);
        output.writeUTF(this.job);
        output.writeInt(this.mgr);
        output.writeUTF(this.hiredate);
        output.writeInt(this.sal);
        output.writeInt(this.comm);
        output.writeInt(this.deptno);
    }
    
    @Override
    public void readFields(DataInput input) throws IOException {
        // 代表反序列化：输入
        //注意：序列化和反序列化的顺序要一致
        this.empno = input.readInt();
        this.ename = input.readUTF();
        this.job = input.readUTF();
        this.mgr = input.readInt();
        this.hiredate = input.readUTF();
        this.sal = input.readInt();
        this.comm = input.readInt();
        this.deptno = input.readInt();
    }
    
    
    public int getEmpno() {
        return empno;
    }
    public void setEmpno(int empno) {
        this.empno = empno;
    }
    public String getEname() {
        return ename;
    }
    public void setEname(String ename) {
        this.ename = ename;
    }
    public String getJob() {
        return job;
    }
    public void setJob(String job) {
        this.job = job;
    }
    public int getMgr() {
        return mgr;
    }
    public void setMgr(int mgr) {
        this.mgr = mgr;
    }
    public String getHiredate() {
        return hiredate;
    }
    public void setHiredate(String hiredate) {
        this.hiredate = hiredate;
    }
    public int getSal() {
        return sal;
    }
    public void setSal(int sal) {
        this.sal = sal;
    }
    public int getComm() {
        return comm;
    }
    public void setComm(int comm) {
        this.comm = comm;
    }
    public int getDeptno() {
        return deptno;
    }
    public void setDeptno(int deptno) {
        this.deptno = deptno;
    }

    
}

EmployeeMapper.java

package saltotal;

import java.io.IOException;

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

import saltotal.Employee;
//k2 部门号 v2 员工对象
public class SalaryTotalMapper extends Mapper<LongWritable, Text, IntWritable, Employee>{

    @Override
    protected void map(LongWritable k1, Text v1, Context context)
            throws IOException, InterruptedException {
        // 数据：MARTIN,SALEsMAN,7698,1981/9/28,1250,1400,30
        String data = v1.toString();
        
        //分词
        String[] words = data.split(",");
        
        //创建员工的对象
        Employee e = new Employee();
        
        //设置员工号
        e.setEmpno(Integer.parseInt(words[0]));
        //姓名
        e.setEname(words[1]);
        
        //职位
        e.setJob(words[2]);
        
        //经理号：有些没有
        try{
            e.setMgr(Integer.parseInt(words[3]));
        }catch(Exception ex){
            //空值设0
            e.setMgr(0);
        }
        
        //入职日期
        e.setHiredate(words[4]);
        
        //月薪
        e.setSal(Integer.parseInt(words[5]));
        
        //奖金：有的没有
        try{
            e.setComm(Integer.parseInt(words[6]));
        }catch(Exception ex){
            e.setComm(0);
        }
        
        //部门
        e.setDeptno(Integer.parseInt(words[7]));
        
        
        //输出 部门号 员工对象
        context.write(new IntWritable(e.getDeptno()), e);
    }
    
}

SalaryTotalReducer.java

package saltotal;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Reducer;
import saltotal.Employee;
//                                              k3 部门号  v3员工对象    k4部门号 v4 工资总额   
public class SalaryTotalReducer extends Reducer<IntWritable, Employee, IntWritable, IntWritable>{

    @Override
    protected void reduce(IntWritable k3, Iterable<Employee> v3,Context context)
            throws IOException, InterruptedException {
        //对v3求和
        int total = 0;
        for (Employee e : v3) {
            total = total + e.getSal();
        }
        
        //输出
        context.write(k3, new IntWritable(total));
    }
    
}

SalaryTotalMain.java

package saltotal;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class SalaryTotalMain {
    public static void main(String[] args) throws Exception {
        //创建一个job = map + reduce
        Job job = Job.getInstance(new Configuration());
        //ָ指定任务的入口
        job.setJarByClass(SalaryTotalMain.class);
        
        //ָ指定任务的Mapper和输出的数据类型k2 v2
        job.setMapperClass(SalaryTotalMapper.class);
        job.setMapOutputKeyClass(IntWritable.class);
        job.setMapOutputValueClass(Employee.class);
        
        //ָ指定任务的Reducer和输出的数据类型k4 v4
        job.setReducerClass(SalaryTotalReducer.class);
        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(IntWritable.class);
        
        //ָ指定输入输出的路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        
        //执行任务
        job.waitForCompletion(true);
    }
}

输出jar文件，传到Linux上temp文件夹下，然后执行任务：

hadoop jar temp/s3.jar /scott/emp.csv /output/day0301/s3

二.排序

1.数字的排序

　　默认：按照key2进行升序排序

现在HDFS上有一个文件，里面的数据如下：

 开发MapReduce程序进行排序：

NumberMapper.java

package mr.number;

import java.io.IOException;

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

public class NumberMapper extends Mapper<LongWritable, Text, LongWritable, NullWritable>{

    @Override
    protected void map(LongWritable key1, Text value1, Context context)
            throws IOException, InterruptedException {
        //数字：10
        String data = value1.toString().trim();
        
        //输出：把数字作为k2
        context.write(new LongWritable(Long.parseLong(data)), NullWritable.get());
    }
}

NumberMain.java

package mr.number;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class NumberMain {

    public static void main(String[] args) throws Exception {
        // 创建一个job = map + reduce
        Job job = Job.getInstance(new Configuration());
        //ָ指定任务入口
        job.setJarByClass(NumberMain.class);
        
        //ָ指定mapper和输出的数据类型：k2 v2
        job.setMapperClass(NumberMapper.class);
        job.setMapOutputKeyClass(LongWritable.class);
        job.setMapOutputValueClass(NullWritable.class);
        
        //job.setSortComparatorClass(MyNumberComparator.class);
        
        //ָ指定输入和输出的路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        
        //执行任务
        job.waitForCompletion(true);
    }

}

执行任务后看到结果：

如果要改变默认的排序规则，需要创建一个自己的比较器

 定义一个降序比较器类 MyNumberComparator.java

package mr.number;

import org.apache.hadoop.io.LongWritable;

//自己定义的比较器
public class MyNumberComparator extends LongWritable.Comparator{

    @Override
    public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2) {
        // 使用降序排序
        return -super.compare(b1, s1, l1, b2, s2, l2);
    }
}

将NumberMain.java的这句话放开：

job.setSortComparatorClass(MyNumberComparator.class);

然后重新打包执行任务之后可看到如下结果：