MapReduce之自定義分組實現

MapReduce之自定義分組實現

0.概念

• 說明：分組是一種特殊的比較器，對key做比較，並進行歸併，類似於合併同類項，也類似於SQL中的分組查詢

• 要求：通過自定義分組比較器實現將emp.csv中的數據按照部門號分成三個分組，並顯示出每組的人員名稱，
  最終顯示的結果格式如下所示：
  <10,CLARK1；KING1；MILLER1>
  <20,CLARK2；KING2；MILLER2>
  <30,CLARK3；KING3；MILLER3>

• 思路：
  1、需要自定義分組比較器
  2、需要自定義Employee對象排序規則，並注意所使用排序字段應該與分組比較器規則相同
  3、mapper和reducer都需要修改

• 本文用到的文件內容
  

1.依賴

<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.7.3</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>2.7.3</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-common</artifactId>
<version>2.7.3</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-core</artifactId>
<version>2.7.3</version>
</dependency>

2.添加log4j.properties文件在資源目錄下即resources，文件內容如下

log4j.rootLogger=FATAL, dest1
log4j.logger.dsaLogging=DEBUG, dsa
log4j.additivity.dsaLogging=false

log4j.appender.dest1=org.apache.log4j.ConsoleAppender
log4j.appender.dest1.layout=org.apache.log4j.PatternLayout
log4j.appender.dest1.layout.ConversionPattern=%-5p:%l: %m%n
log4j.appender.dest1.ImmediateFlush=true

log4j.appender.dsa=org.apache.log4j.RollingFileAppender
log4j.appender.dsa.File=./logs/dsa.log
log4j.appender.dsa.MaxFileSize=2000KB
# Previously MaxBackupIndex=2
log4j.appender.dsa.MaxBackupIndex=5
log4j.appender.dsa.layout=org.apache.log4j.PatternLayout
log4j.appender.dsa.layout.ConversionPattern=%l:%d: %m%n

3.編寫序列化類Employee,編寫排序規則

package com.mr.group;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;

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

public class Employee implements WritableComparable<Employee> {

    //7369,SMITH,CLERK,7902,1980/12/17,800,,20
    private IntWritable empNo;
    private Text empName;
    private Text empJob;
    private IntWritable leaderNo;
    private Text hireDate;
    private IntWritable empSalary;
    private Text empBonus;
    private IntWritable deptNo;

    public Employee() {
        this.empNo = new IntWritable();
        this.empName = new Text("");
        this.empJob = new Text("");
        this.leaderNo = new IntWritable();
        this.hireDate = new Text("");
        this.empSalary =new IntWritable();
        this.empBonus = new Text("");
        this.deptNo = new IntWritable();
    }

    public Employee(int empNo, String empName, String empJob, int leaderNo,
                    String hireDate, int empSalary, String empBonus, int deptNo) {
        this.empNo = new IntWritable(empNo);
        this.empName = new Text(empName);
        this.empJob = new Text(empJob);
        this.leaderNo = new IntWritable(leaderNo);
        this.hireDate = new Text(hireDate);
        this.empSalary =new IntWritable(empSalary);
        this.empBonus = new Text(empBonus);
        this.deptNo = new IntWritable(deptNo);
    }

    @Override
    public void write(DataOutput out) throws IOException {
//序列化
        this.deptNo.write(out);
        this.empSalary.write(out);
        this.empNo.write(out);
        this.empName.write(out);
        this.empJob.write(out);
        this.leaderNo.write(out);
        this.hireDate.write(out);
        this.empBonus.write(out);

    }

    @Override
    public void readFields(DataInput in) throws IOException {
        this.deptNo.readFields(in);
        this.empSalary.readFields(in);
        this.empNo.readFields(in);
        this.empName.readFields(in);
        this.empJob.readFields(in);
        this.leaderNo.readFields(in);
        this.hireDate.readFields(in);
        this.empBonus.readFields(in);
    }

    @Override
    public String toString() {
        return "Employee{" +
                "empNo=" + empNo +
                ", empName=" + empName +
                ", empJob=" + empJob +
                ", leaderNo=" + leaderNo +
                ", hireDate=" + hireDate +
                ", empSalary=" + empSalary +
                ", empBonus=" + empBonus +
                ", deptNo=" + deptNo +
                '}';
    }

    public IntWritable getEmpNo() {
        return empNo;
    }

    public void setEmpNo(IntWritable empNo) {
        this.empNo = empNo;
    }

    public Text getEmpName() {
        return empName;
    }

    public void setEmpName(Text empName) {
        this.empName = empName;
    }

    public Text getEmpJob() {
        return empJob;
    }

    public void setEmpJob(Text empJob) {
        this.empJob = empJob;
    }

    public IntWritable getLeaderNo() {
        return leaderNo;
    }

    public void setLeaderNo(IntWritable leaderNo) {
        this.leaderNo = leaderNo;
    }

    public Text getHireDate() {
        return hireDate;
    }

    public void setHireDate(Text hireDate) {
        this.hireDate = hireDate;
    }

    public IntWritable getEmpSalary() {
        return empSalary;
    }

    public void setEmpSalary(IntWritable empSalary) {
        this.empSalary = empSalary;
    }

    public Text getEmpBonus() {
        return empBonus;
    }

    public void setEmpBonus(Text empBonus) {
        this.empBonus = empBonus;
    }

    public IntWritable getDeptNo() {
        return deptNo;
    }

    public void setDeptNo(IntWritable deptNo) {
        this.deptNo = deptNo;
    }

    /**
     * 自定義排序規則
     * 按照部門號升序排,員工工資降序排序
     * @param o
     * @return
     */
    public int compareTo(Employee o) {
        if (this.deptNo.get() > o.getDeptNo().get()){
            return 1;
        }else if(this.deptNo.get() < o.getDeptNo().get()){
            return -1;
        }
//說明：部門號是相同的情況，執行下面代碼
//按照工資降序排
        if (this.empSalary.get() > o.getEmpSalary().get()){
            return -1;
        }else if(this.empSalary.get() < o.getEmpSalary().get()){
            return 1;
        }else{
            return 0;
        }
    }
}

4.編寫自定義分組類MyEmployeeGrouper

package com.mr.group;

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

public class MyEmployeeGrouper extends WritableComparator {

    public MyEmployeeGrouper() {
//每一個實現都需要對比較的對象進行註冊
        super(Employee.class,true);
    }

    /**
     * 然後使用部門號進行比較
     * @param a
     * @param b
     * @return
     */
    @Override
    public int compare(WritableComparable a, WritableComparable b) {
        Employee employee1 = (Employee) a;
        Employee employee2 = (Employee) b;
        return employee1.getDeptNo().compareTo(employee2.getDeptNo());
    }
}

5.編寫mapper類

package com.mr.group;

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 java.io.IOException;

public class EmpGroupMapper extends Mapper<LongWritable, Text, Employee, Text> {

    Employee employee = new Employee();//保證對象的hashCode一致

    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
//數據格式：<0,7369,SMITH,CLERK,7902,1980/12/17,800,,20>
        System.out.println("====key:" + key + "value:" + value.toString() + " ====== ");
//1、分詞
        String[] splits = value.toString().split(",");
//2、創建Employee對象，並且賦值
        employee.setDeptNo(new IntWritable(Integer.valueOf(splits[7])));
        employee.setEmpSalary(new IntWritable(Integer.valueOf(splits[5])));
        employee.setEmpName(new Text(splits[1]));
        employee.setEmpJob(new Text(splits[2]));

//3、通過context寫出去
        context.write(employee,employee.getEmpName());
    }
}

6.編寫reduce類

package com.mr.group;


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

import java.io.IOException;

public class EmpGroupReducer extends Reducer<Employee, Text,IntWritable,Text> {
    @Override
    protected void reduce(Employee key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
//1、對數據進行處理：取出相同部門的員工名稱集合
        System.out.println("======key===========" + key.toString());
        String names = "";
        for (Text e: values) {
            names = e.toString() + ";" + names;
            System.out.println("======value===========" + e.toString());
        }
//2、將結果通過context寫出去
        context.write(key.getDeptNo(),new Text(names));
    }
}

7.編寫Job類

package com.mr.group;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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;

import java.util.Random;

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

        job.setMapperClass(EmpGroupMapper.class);
        job.setMapOutputKeyClass(Employee.class);
        job.setMapOutputValueClass(Text.class);

//設置分組
        job.setGroupingComparatorClass(MyEmployeeGrouper.class);

//設置reduce
        job.setReducerClass(EmpGroupReducer.class);

        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(Text.class);

        FileInputFormat.setInputPaths(job,new Path("D:\\emp.csv"));
        FileOutputFormat.setOutputPath(job,new Path(getOutputDir()));

        boolean result = job.waitForCompletion(true);
        if (result)
            System.out.println("運行成功");
        else
            System.out.println("運行失敗");
    }

    //用於產生隨機輸出目錄
    public static String getOutputDir(){
        String prefix = "D:\\output\\";
        long time = System.currentTimeMillis();
        int random = new Random().nextInt(1000);
        return prefix + "result_" + time + "_" + random;
    }
}

8.運行結果