大数据笔记（九）——Mapreduce的高级特性（B）

时间：2018-03-04 00:26:44 阅读：198 评论：0 收藏：0 [点我收藏+]

标签：cat dex 效率 dem span 作用 mapred input throw

二.排序

对象排序

员工数据 Employee.java ----> 作为key2输出

需求：按照部门和薪水升序排列

Employee.java

package mr.object;

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

import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;

//?????: 7654,MARTIN,SALESMAN,7698,1981/9/28,1250,1400,30
public class Employee implements WritableComparable<Employee>{

    private int empno;
    private String ename;
    private String job;
    private int mgr;
    private String hiredate;
    private int sal;
    private int comm;
    private int deptno;//    @Override
//    public int compareTo(Employee o) {
//        // 一个列的排序规则：按照员工的薪水排序
//        if(this.sal >= o.getSal()){
//            return 1;
//        }else{
//            return -1;
//        }
//    }
    
    @Override
    public int compareTo(Employee o) {
        // 两个列排序规则：部门
        if(this.deptno > o.getDeptno()){
            return 1;
        }else if(this.deptno < o.getDeptno()){
            return -1;
        }
        
        //薪水
        if(this.sal >= o.getSal()){
            return 1;
        }else{
            return -1;
        }

    }
    
    @Override
    public String toString() {
        return "["+this.empno+"\t"+this.ename+"\t"+this.sal+"\t"+this.deptno+"]";
    }

    @Override
    public void write(DataOutput output) throws IOException {
        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 {
        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;
    }
}

EmployeeSortMapper.java

package mr.object;

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;

//                                                                   Key2
public class EmployeeSortMapper extends Mapper<LongWritable, Text, Employee, NullWritable> {

    @Override
    protected void map(LongWritable key1, Text value1,Context context)
            throws IOException, InterruptedException {
        // ?????7654,MARTIN,SALESMAN,7698,1981/9/28,1250,1400,30
        String data = value1.toString();
        
        //分词
        String[] words = data.split(",");
        
        //创建员工对象
        Employee e = new Employee();
        
        //员工号
        e.setEmpno(Integer.parseInt(words[0]));
        //员工姓名
        e.setEname(words[1]);
        
        //job
        e.setJob(words[2]);
        
        //经理号：注意 有些员工没有经理
        try{
            e.setMgr(Integer.parseInt(words[3]));
        }catch(Exception ex){
            //null
            e.setMgr(0);
        }
        
        //入职日期
        e.setHiredate(words[4]);
        
        //薪水
        e.setSal(Integer.parseInt(words[5]));
        
        //奖金
        try{
            e.setComm(Integer.parseInt(words[6]));
        }catch(Exception ex){
            //无奖金
            e.setComm(0);
        }
        
        //部门
        e.setDeptno(Integer.parseInt(words[7]));
        
        
        //输出key2
        context.write(e, NullWritable.get());
    }
}

EmployeeSortMain.java

package mr.object;

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 EmployeeSortMain {

    public static void main(String[] args) throws Exception {
        // job = map + reduce
        Job job = Job.getInstance(new Configuration());
        //?任务入口
        job.setJarByClass(EmployeeSortMain.class);
        
        job.setMapperClass(EmployeeSortMapper.class);
        job.setMapOutputKeyClass(Employee.class);
        job.setMapOutputValueClass(NullWritable.class);
        
        job.setReducerClass(EmployeeSortReducer.class);
        job.setOutputKeyClass(LongWritable.class);
        job.setOutputValueClass(Employee.class); 
        
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        
        //执行任务
        job.waitForCompletion(true);
    }

}

结果：

技术分享图片

三.分区分区:Partition:

根据Map的输出（k2 v2）进行分区

默认情况下，MapReduce只有一个分区（只有一个输出文件）

作用：提高查询的效率

建立分区：根据条件的不同

技术分享图片

需求：按照员工的部门号进行分区，相同部门号的员工输出到一个分区中

EmpPartionMapper.java

package demo.partion;

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;

//k2部门号 v2 员工对象
public class EmpPartionMapper extends Mapper<LongWritable, Text, LongWritable, Employee> {

    @Override
    protected void map(LongWritable key1, Text value1, Context context)
            throws IOException, InterruptedException {
        
        // ?????7654,MARTIN,SALESMAN,7698,1981/9/28,1250,1400,30
                String data = value1.toString();
                
                String[] words = data.split(",");
                
                Employee e = new Employee();
                
                e.setEmpno(Integer.parseInt(words[0]));

                e.setEname(words[1]);

                e.setJob(words[2]);
                
                try{
                    e.setMgr(Integer.parseInt(words[3]));
                }catch(Exception ex){
                    //null
                    e.setMgr(0);
                }
                
                e.setHiredate(words[4]);
                
                e.setSal(Integer.parseInt(words[5]));
                
                try{
                    e.setComm(Integer.parseInt(words[6]));
                }catch(Exception ex){
                    e.setComm(0);
                }
                
                e.setDeptno(Integer.parseInt(words[7]));
                
                //输出 k2是部门号 v2是员工对象
                context.write(new LongWritable(e.getDeptno()), e);
    }
}

EmpPartionReducer.java

package demo.partion;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.mapreduce.Reducer;

//把相同部门的员工输出到HDFS                                                                                                                          K4: 部门号 v4: 员工对象
public class EmpPartionReducer extends Reducer<LongWritable, Employee, LongWritable, Employee>{

    @Override
    protected void reduce(LongWritable k3, Iterable<Employee> v3, Context context)
            throws IOException, InterruptedException {
        for (Employee e : v3) {
            context.write(k3, e);
        }
    }
    
}

MyEmployeePartitioner.java

package demo.partion;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.mapreduce.Partitioner;
//分区规则：根据Map的输出建立分区                                                                                              k2            v2
public class MyEmployeePartitioner extends Partitioner<LongWritable, Employee>{

    /* 
     * numParts 分区个数
     */
    @Override
    public int getPartition(LongWritable k2, Employee v2, int numParts) {
        //分区规则
        int deptno = v2.getDeptno();
        if (deptno == 10) {
            //放入一号分区
            return 1%numParts;
        }else if (deptno == 20) {
            //放入二号分区
            return 2%numParts;
        }else {
            //放入0号分区
            return 3%numParts;
        }
    }
}

EmpPartitionMain.java

package demo.partion;

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.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class EmpPartitionMain {

    public static void main(String[] args) throws Exception {

        Job job = Job.getInstance(new Configuration());

        job.setJarByClass(EmpPartitionMain.class);
        
        job.setMapperClass(EmpPartionMapper.class);
        job.setMapOutputKeyClass(LongWritable.class);
        job.setMapOutputValueClass(Employee.class);
        
        //指定分区规则
        job.setPartitionerClass(MyEmployeePartitioner.class);
        //指定分区的个数
        job.setNumReduceTasks(3);
        
        job.setReducerClass(EmpPartionReducer.class);
        job.setOutputKeyClass(LongWritable.class);
        job.setOutputValueClass(Employee.class);
        
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        
        job.waitForCompletion(true);

    }

}

结果：建立了三个分区

技术分享图片

一号分区：

技术分享图片

二号分区：

技术分享图片

0号分区：

技术分享图片

四.合并：Combiner

1、MapReduce的任务中，可以没有Combiner
2、Combiner是一种特殊的Reducer，是在Mapper端先做一次Reducer，用来减少Map的输出，从而提高的效率。
3、注意事项：
（1）有些情况，不能使用Combiner -----> 求平均值
（2）引入Combiner，不引人Combiner，一定不能改变原理的逻辑。（MapReduce编程案例：实现倒排索引）

技术分享图片

WordCountMapper.java

package demo.combiner;

import java.io.IOException;

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

public class WordCountMapper extends Mapper<LongWritable, Text, Text, LongWritable>{

    @Override
    protected void map(LongWritable k1, Text v1, Context context)
            throws IOException, InterruptedException {
        
        //取出数据: I love beijing
        String data = v1.toString();
        
        //分词
        String[] words = data.split(" ");
        
        //输出K2：单词  V2：记一次数
        for (String w : words) {
            context.write(new Text(w), new LongWritable(1));
        }
        
    }
    
}

WordCountReducer.java

package demo.combiner;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class WordCountReducer extends Reducer<Text, LongWritable, Text, LongWritable>{

    @Override
    protected void reduce(Text k3, Iterable<LongWritable> v3,
            Context context) throws IOException, InterruptedException {
        long total = 0;
        for (LongWritable l : v3) {
            total = total + l.get();
        }
        
        //输出K4 V4
        context.write(k3, new LongWritable(total));
    }
    
}

WordCountMain.java:增加Combiner

package demo.combiner;

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.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WordCountMain {

    public static void main(String[] args) throws Exception {
        Job job = Job.getInstance(new Configuration());

        job.setJarByClass(WordCountMain.class);
    
        //Mapper
        job.setMapperClass(WordCountMapper.class);
        job.setMapOutputKeyClass(Text.class);//指定k2
        job.setMapOutputValueClass(LongWritable.class);//指定v2
        
        //Combiner
        job.setCombinerClass(WordCountReducer.class);
        
        //?reducer
        job.setReducerClass(WordCountReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        //?mapper/reducer路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        //?执行任务
        job.waitForCompletion(true);
    }        
}

大数据笔记（九）——Mapreduce的高级特性（B）

标签：cat dex 效率 dem span 作用 mapred input throw

原文地址：https://www.cnblogs.com/lingluo2017/p/8503501.html

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