Hadoop 实例8 Join讲解1: 获取员工所在部门信息

输出格式要求：员工编号，员工姓名，部门名称，部门编号

1、原始数据

员工数据

empno   ename   job         mgr hiredate    sal comm    deptno  loc
7499    allen   salesman    7698    1981-02-20  1600    300 30
7782    clark   managers    7639    1981-06-09  2450        10
7654    martin  salesman    7698    1981-03-20  1250    1400    30  boston
7900    james   clerk   7698    1981-01-09  950     30
7788    scott   analyst 7566    1981-09-01  3000    100 20

部门数据

deptno  dname   loc
30  sales   chicago
20  research    dallas
10  accounting  newyork

2、处理join的思路：

将Join key 当作map的输出key, 也就是reduce的输入key , 这样只要join的key相同，shuffle过后，就会进入到同一个reduce 的key - value list 中去。

需要为join的2张表设计一个通用的一个bean. 并且bean中加一个flag的标志属性，这样可以根据flag来区分是哪张表的数据。

reduce 阶段根据flag来判断是员工数据还是部门数据就很容易了 。而join的真正处理是在reduce阶段。

3.实现中间bean

存储数据的bean (由于数据要在网络上传输必须序列化，hadoop处理的时候需要分组和排序，所以要实现WritableComparable接口):

package cn.edu.bjut.joinone;

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

import org.apache.hadoop.io.WritableComparable;

public class Emplyee implements WritableComparable<Emplyee> {

private String empNo = "";
private String empName = "";
private String deptNo = "";
private String deptName = "";
private int flag = 0;//部门数据为1，员工数据为0

public Emplyee() {}

public Emplyee(String empNo, String empName, String deptNo,
String deptName, int flag) {
super();
this.empNo = empNo;
this.empName = empName;
this.deptNo = deptNo;
this.deptName = deptName;
this.flag = flag;
}

public Emplyee(Emplyee e) {
this.empNo = e.getEmpNo();
this.empName = e.getEmpName();
this.deptNo = e.getDeptNo();
this.deptName = e.getDeptName();
this.flag = e.getFlag();
}

public void write(DataOutput out) throws IOException {
out.writeUTF(getEmpNo());
out.writeUTF(getEmpName());
out.writeUTF(getDeptNo());
out.writeUTF(getDeptName());
out.writeInt(getFlag());

}

public void readFields(DataInput in) throws IOException {
this.empNo = in.readUTF();
this.empName = in.readUTF();
this.deptNo = in.readUTF();
this.deptName = in.readUTF();
this.flag = in.readInt();
}

public int compareTo(Emplyee o) {
return 0;
}

@Override
public String toString() {
return "empNo=" + empNo + ", empName=" + empName + ", deptNo="
+ deptNo + ", deptName=" + deptName;
}

public String getEmpNo() {
return empNo;
}

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

public String getEmpName() {
return empName;
}

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

public String getDeptNo() {
return deptNo;
}

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

public String getDeptName() {
return deptName;
}

public void setDeptName(String deptName) {
this.deptName = deptName;
}

public int getFlag() {
return flag;
}

public void setFlag(int flag) {
this.flag = flag;
}

}

4.Mapper程序：

package cn.edu.bjut.joinone;

import java.io.IOException;

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

public class JoinMapper extends Mapper<LongWritable, Text, LongWritable, Emplyee> {

@Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String line = value.toString();
String[] arr = line.split("\t");

if(arr.length <= 3) {
Emplyee e = new Emplyee();
e.setDeptNo(arr[0]);
e.setDeptName(arr[1]);
e.setFlag(1);

context.write(new LongWritable(Long.parseLong(e.getDeptNo())), e);
} else {
Emplyee e = new Emplyee();
e.setEmpNo(arr[0]);
e.setEmpName(arr[1]);
e.setDeptNo(arr[7]);
e.setFlag(0);

context.write(new LongWritable(Long.parseLong(e.getDeptNo())), e);
}
}

}

5.Reducer程序：

package cn.edu.bjut.joinone;

import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

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

public class JoinReducer extends Reducer<LongWritable, Emplyee, Text, NullWritable> {

@Override
protected void reduce(LongWritable key, Iterable<Emplyee> values, Context context)
throws IOException, InterruptedException {

Emplyee e = null;
List<Emplyee> list = new ArrayList<Emplyee>();

for(Emplyee emplyee : values) {
if(0 == emplyee.getFlag()) {
list.add(new Emplyee(emplyee));
} else {
e = new Emplyee(emplyee);
}
}

if(null != e) {
for(Emplyee emplyee : list) {
emplyee.setDeptName(e.getDeptName());
context.write(new Text(emplyee.toString()), NullWritable.get());
}
}
}

}

6.主程序：

package cn.edu.bjut.joinone;

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

public class MainJob {

public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = new Job(conf, "join");
job.setJarByClass(MainJob.class);

job.setMapperClass(JoinMapper.class);
job.setMapOutputKeyClass(LongWritable.class);
job.setMapOutputValueClass(Emplyee.class);

job.setReducerClass(JoinReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(NullWritable.class);

FileInputFormat.addInputPath(job, new Path(args[0]));
Path outPath = new Path(args[1]);
FileSystem fs = FileSystem.get(conf);
if(fs.exists(outPath)) {
fs.delete(outPath, true);
}
FileOutputFormat.setOutputPath(job, outPath);

job.waitForCompletion(true);
}

}