大数据笔记（九）——Mapreduce的高级特性（B）

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;

//Ա����: 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 {

        // ���ݣ�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：

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

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 {

        // ���ݣ�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

一号分区：

二号分区：

0号分区：

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）

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);

    }

}

巴特西

大数据笔记（九）——Mapreduce的高级特性（B）

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