[DB] MapReduce

概述

大数据计算的核心思想：移动计算比移动数据更划算
MapReduce既是一个编程模型，又是一个计算框架
包含Map和Reduce两个过程
终极目标：用SQL语句分析大数据（Hive、SparkSQL，将SQL语句转换为MR程序）

特性

序列化
- java序列化：实现序列化接口（标识接口）

Student.java

 1 package serializable;
 2 
 3 import java.io.Serializable;
 4 
 5 public class Student implements Serializable{
 6     private int stuID;
 7     private String stuName;
 8     public int getStuID() {
 9         return stuID;
10     }
11     public void setStuID(int stuID) {
12         this.stuID = stuID;
13     }
14     public String getStuName() {
15         return stuName;
16     }
17     public void setStuName(String stuName) {
18         this.stuName = stuName;
19     }
20 }

View Code

TestMain.java

 1 package serializable;
 2 
 3 import java.io.FileOutputStream;
 4 import java.io.ObjectOutputStream;
 5 import java.io.OutputStream;
 6 
 7 import serializable.Student;
 8 
 9 public class TestMain {
10 
11     public static void main(String[] args) throws Exception{
12         // 创建学生对象
13         Student s = new Student();
14         s.setStuID(1);
15         s.setStuName("Tom");
16         
17         // 输出对象到文件
18         OutputStream out = new FileOutputStream("F:\eclipse-workspace\student.ooo");
19         ObjectOutputStream oos = new ObjectOutputStream(out);
20         oos.writeObject(s);
21             
22         oos.close();
23         out.close();
24     }
25 }

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- MapReduce序列化：核心接口Writable，实现了Writeble的类的对象可作为MapReduce的key和value
  - 读取员工数据，生成员工对象，直接输出到HDFS
  - hadoop jar s2.jar /scott/emp.csv /output/0910/s2

EmpInfoMain.java

 1 import org.apache.hadoop.conf.Configuration;
 2 import org.apache.hadoop.fs.Path;
 3 import org.apache.hadoop.io.IntWritable;
 4 import org.apache.hadoop.mapreduce.Job;
 5 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
 6 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
 7 
 8 public class EmpInfoMain {
 9 
10     public static void main(String[] args) throws Exception {
11         Job job = Job.getInstance(new Configuration());
12         job.setJarByClass(EmpInfoMain.class);
13         
14         job.setMapperClass(EmpInfoMapper.class);
15         job.setMapOutputKeyClass(IntWritable.class);
16         job.setMapOutputValueClass(Emp.class);
17         
18         job.setOutputKeyClass(IntWritable.class);
19         job.setOutputKeyClass(Emp.class);
20         
21         FileInputFormat.setInputPaths(job, new Path(args[0]));
22         FileOutputFormat.setOutputPath(job, new Path(args[1]));
23 
24         job.waitForCompletion(true);
25     }
26 }

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EmpInfoMapper.java

 1 import java.io.IOException;
 2 
 3 import org.apache.hadoop.io.IntWritable;
 4 import org.apache.hadoop.io.LongWritable;
 5 import org.apache.hadoop.io.Text;
 6 import org.apache.hadoop.mapreduce.Mapper;
 7 
 8 public class EmpInfoMapper extends Mapper<LongWritable, Text, IntWritable, Emp>{
 9 
10     @Override
11     protected void map(LongWritable key1, Text value1,
12             Context context)
13             throws IOException, InterruptedException {
14         //数据：7654,MARTIN,SALESMAN,7698,1981/9/28,1250,1400,30
15         String data = value1.toString();
16         
17         // 分词
18         String[] words = data.split(",");
19         
20         // 生成员工对象
21         Emp emp = new Emp();
22         emp.setEmpno(Integer.parseInt(words[0]));
23         emp.setEname(words[1]);
24         emp.setJob(words[2]);
25         emp.setMgr(Integer.parseInt(words[3]));
26         emp.setHiredate(words[4]);
27         emp.setSal(Integer.parseInt(words[5]));
28         emp.setComm(Integer.parseInt(words[6]));
29         emp.setDeptno(Integer.parseInt(words[7]));
30         
31         // 输出员工对象
32         context.write(new IntWritable(emp.getEmpno()), emp);
33     }
34 }

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Emp.java

  1 import java.io.DataInput;
  2 import java.io.DataOutput;
  3 import java.io.IOException;
  4 
  5 import org.apache.hadoop.io.Writable;
  6 
  7 public class Emp implements Writable{
  8     private int empno;
  9     private String ename;
 10     private String job;
 11     private int mgr;
 12     private String hiredate;
 13     private int sal;
 14     private int comm;
 15     private int deptno;
 16     
 17     @Override
 18     public void readFields(DataInput input)
 19             throws IOException {
 20         // 实现反序列化，从输入流中读取对象（与输出流顺序一样）
 21         this.empno = input.readInt();
 22         this.ename = input.readUTF();
 23         this.job = input.readUTF();
 24         this.mgr = input.readInt();
 25         this.hiredate = input.readUTF();
 26         this.sal = input.readInt();
 27         this.comm = input.readInt();
 28         this.deptno = input.readInt();
 29     }
 30 
 31     @Override
 32     public void write(DataOutput output) throws IOException {
 33         // 实现序列化，把对象输出到输出流
 34         output.writeInt(this.empno);
 35         output.writeUTF(this.ename);
 36         output.writeUTF(this.job);
 37         output.writeInt(this.mgr);
 38         output.writeUTF(this.hiredate);
 39         output.writeInt(this.sal);
 40         output.writeInt(this.comm);
 41         output.writeInt(this.deptno);
 42     }
 43     
 44     public int getEmpno() {
 45         return empno;
 46     }
 47 
 48     public void setEmpno(int empno) {
 49         this.empno = empno;
 50     }
 51 
 52     public String getEname() {
 53         return ename;
 54     }
 55 
 56     public void setEname(String ename) {
 57         this.ename = ename;
 58     }
 59 
 60     public String getJob() {
 61         return job;
 62     }
 63 
 64     public void setJob(String job) {
 65         this.job = job;
 66     }
 67 
 68     public int getMgr() {
 69         return mgr;
 70     }
 71 
 72     public void setMgr(int mgr) {
 73         this.mgr = mgr;
 74     }
 75 
 76     public String getHiredate() {
 77         return hiredate;
 78     }
 79 
 80     public void setHiredate(String hiredate) {
 81         this.hiredate = hiredate;
 82     }
 83 
 84     public int getSal() {
 85         return sal;
 86     }
 87 
 88     public void setSal(int sal) {
 89         this.sal = sal;
 90     }
 91 
 92     public int getComm() {
 93         return comm;
 94     }
 95 
 96     public void setComm(int comm) {
 97         this.comm = comm;
 98     }
 99 
100     public int getDeptno() {
101         return deptno;
102     }
103 
104     public void setDeptno(int deptno) {
105         this.deptno = deptno;
106     }
107 
108     @Override
109     public String toString() {
110         return "Emp [empno=" + empno + ", ename=" + ename
111                 + ", sal=" + sal + ", deptno=" + deptno
112                 + "]";
113     }
114 
115 }

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- - 求部门工资总额
    - 由于实现了序列化接口，员工可作为key和value
    - 相比之前的程序更加简洁
    - 对于大对象可能产生性能问题

EmpInfoMain.java

 1 import org.apache.hadoop.conf.Configuration;
 2 import org.apache.hadoop.fs.Path;
 3 import org.apache.hadoop.io.IntWritable;
 4 import org.apache.hadoop.mapreduce.Job;
 5 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
 6 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
 7 
 8 public class EmpInfoMain {
 9 
10     public static void main(String[] args) throws Exception {
11         Job job = Job.getInstance(new Configuration());
12         job.setJarByClass(EmpInfoMain.class);
13         
14         job.setMapperClass(EmpInfoMapper.class);
15         job.setMapOutputKeyClass(IntWritable.class);
16         job.setMapOutputValueClass(Emp.class);  // 输出就是员工对象
17         
18         job.setOutputKeyClass(IntWritable.class);
19         job.setOutputValueClass(Emp.class);
20         
21         FileInputFormat.setInputPaths(job, new Path(args[0]));
22         FileOutputFormat.setOutputPath(job, new Path(args[1]));
23 
24         job.waitForCompletion(true);
25     }
26 
27 }

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SalaryTotalMapper.java

 1 import java.io.IOException;
 2 import org.apache.hadoop.io.IntWritable;
 3 import org.apache.hadoop.io.LongWritable;
 4 import org.apache.hadoop.io.Text;
 5 import org.apache.hadoop.mapreduce.Mapper;
 6 
 7 //                                                               k2:部门号  v2:员工对象
 8 public class SalaryTotalMapper extends Mapper<LongWritable, Text, IntWritable, Emp> {
 9 
10     @Override
11     protected void map(LongWritable key1, Text value1, Context context)
12             throws IOException, InterruptedException {
13         // 数据：7654,MARTIN,SALESMAN,7698,1981/9/28,1250,1400,30
14         String data = value1.toString();
15         
16         // 分词
17         String[] words = data.split(",");
18         
19         // 生成员工对象
20         Emp emp = new Emp();
21         emp.setEmpno(Integer.parseInt(words[0]));
22         emp.setEname(words[1]);
23         emp.setJob(words[2]);
24         emp.setMgr(Integer.parseInt(words[3]));
25         emp.setHiredate(words[4]);
26         emp.setSal(Integer.parseInt(words[5]));
27         emp.setComm(Integer.parseInt(words[6]));
28         emp.setDeptno(Integer.parseInt(words[7]));
29         
30         // 输出员工对象  k2:部门号                 v2:员工对象
31         context.write(new IntWritable(emp.getDeptno()), emp);
32     }
33 }

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SalaryTotalReducer.java

 1 import java.io.IOException;
 2 import org.apache.hadoop.io.IntWritable;
 3 import org.apache.hadoop.mapreduce.Reducer;
 4 
 5 public class SalaryTotalReducer extends Reducer<IntWritable, Emp, IntWritable, IntWritable> {
 6 
 7     @Override
 8     protected void reduce(IntWritable k3, Iterable<Emp> v3,Context context) throws IOException, InterruptedException {
 9         int total = 0;
10         
11         //取出员工薪水，并求和
12         for(Emp e:v3){
13             total = total + e.getSal();
14         }
15         context.write(k3, new IntWritable(total));
16     }
17 }

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排序
- 规则：按照Key2排序
- 基本数据类型
  - 数字：默认升序
    - 自定义比较器：MyNumberComparator.java
    - 主程序中添加比较器：job.setSortComparatorClass(MyNumberCompatator.class)

MyNumberComparator.java

 1 import org.apache.hadoop.io.IntWritable;
 2 
 3 //针对数字创建自己的比较规则，执行降序排序
 4 public class MyNumberComparator extends IntWritable.Comparator {
 5 
 6     @Override
 7     public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2) {
 8         // TODO Auto-generated method stub
 9         return -super.compare(b1, s1, l1, b2, s2, l2);
10     }
11 }

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- 字符串：默认字典顺序

MyTextComparator.java

 1 import org.apache.hadoop.io.Text;
 2 
 3 // 针对Text定义比较规则
 4 public class MyTextComparator extends Text.Comparator{
 5 
 6     @Override
 7     public int compare(byte[] b1, int s1, int l1, byte[] b2,
 8             int s2, int l2) {
 9         return -super.compare(b1, s1, l1, b2, s2, l2);
10     }
11 }

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- 对象
  - SQL排序：order by + 列名 / 表达式 / 列的别名 / 序号 / 多个列（desc作用于最近一列）
  - 前提：对象必须是Key2；必须实现Writable接口；必须是可排序对象（类似Java对象排序，要实现Comparable<T>）

Student.java

 1 //学生对象：按照学生的age年龄进行排序
 2 public class Student implements Comparable<Student>{
 3 
 4     private int stuID;
 5     private String stuName;
 6     private int age;
 7         
 8     @Override
 9     public String toString() {
10         return "Student [stuID=" + stuID + ", stuName=" + stuName + ", age=" + age + "]";
11     }
12 
13     @Override
14     public int compareTo(Student o) {
15         // 定义排序规则：按照学生的age年龄进行排序
16         if(this.age >= o.getAge()){
17             return 1;
18         }else{
19             return -1;
20         }
21     }
22     
23     public int getStuID() {
24         return stuID;
25     }
26     public void setStuID(int stuID) {
27         this.stuID = stuID;
28     }
29     public String getStuName() {
30         return stuName;
31     }
32     public void setStuName(String stuName) {
33         this.stuName = stuName;
34     }
35     public int getAge() {
36         return age;
37     }
38     public void setAge(int age) {
39         this.age = age;
40     }
41     
42 }

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StudentMain.java

 1 import java.util.Arrays;
 2 
 3 public class StudentMain {
 4 
 5     public static void main(String[] args) {
 6         //创建几个学生对象
 7         Student s1 = new Student();
 8         s1.setStuID(1);
 9         s1.setStuName("Tom");
10         s1.setAge(24);
11         
12         Student s2 = new Student();
13         s2.setStuID(2);
14         s2.setStuName("Mary");
15         s2.setAge(26);
16         
17         Student s3 = new Student();
18         s3.setStuID(3);
19         s3.setStuName("Mike");
20         s3.setAge(25);
21         
22         //生成一个数组
23         Student[] list = {s1,s2,s3};
24         
25         //排序
26         Arrays.sort(list);
27         
28         //输出
29         for(Student s:list){
30             System.out.println(s);
31         }
32     }
33 }

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- - 一个/多个列排序

Emp.java

  1 import java.io.DataInput;
  2 import java.io.DataOutput;
  3 import java.io.IOException;
  4 
  5 import org.apache.hadoop.io.Writable;
  6 import org.apache.hadoop.io.WritableComparable;
  7 
  8 //代表员工
  9 //数据：7654,MARTIN,SALESMAN,7698,1981/9/28,1250,1400,30
 10 public class Emp implements WritableComparable<Emp>{
 11     
 12     private int empno;//员工号
 13     private String ename; //员工姓名
 14     private String job; //职位
 15     private int mgr; //经理的员工号
 16     private String hiredate;//入职日期
 17     private int sal; //月薪
 18     private int comm; //奖金
 19     private int deptno; //部门号
 20     
 21 //    @Override
 22 //    public int compareTo(Emp o) {
 23 //        // 定义自己的排序规则：一个列的排序
 24 //        // 按照薪水进行排序
 25 //        if(this.sal >= o.getSal()){
 26 //            return 1;
 27 //        }else{
 28 //            return -1;
 29 //        }
 30 //    }
 31     
 32     @Override
 33     public int compareTo(Emp o) {
 34         // 定义自己的排序规则：多个列的排序
 35         // 先按照部门号进行排序，再按照薪水进行排序
 36         if(this.deptno > o.getDeptno()){
 37             return 1;
 38         }else if(this.deptno < o.getDeptno()){
 39             return -1;
 40         }
 41         
 42         //再按照薪水进行排序
 43         if(this.sal >= o.getSal()){
 44             return 1;
 45         }else{
 46             return -1;
 47         }
 48     }    
 49         
 50     @Override
 51     public String toString() {
 52         return "Emp [empno=" + empno + ", ename=" + ename + ", sal=" + sal + ", deptno=" + deptno + "]";
 53     }
 54 
 55     @Override
 56     public void readFields(DataInput input) throws IOException {
 57         //实现反序列化，从输入流中读取对象
 58         this.empno = input.readInt();
 59         this.ename = input.readUTF();
 60         this.job = input.readUTF();
 61         this.mgr = input.readInt();
 62         this.hiredate = input.readUTF();
 63         this.sal = input.readInt();
 64         this.comm = input.readInt();
 65         this.deptno = input.readInt();
 66     }
 67     
 68     @Override
 69     public void write(DataOutput output) throws IOException {
 70         // 实现序列化，把对象输出到输出流
 71         output.writeInt(this.empno);
 72         output.writeUTF(this.ename);
 73         output.writeUTF(this.job);
 74         output.writeInt(this.mgr);
 75         output.writeUTF(this.hiredate);
 76         output.writeInt(this.sal);
 77         output.writeInt(this.comm);
 78         output.writeInt(this.deptno);
 79     }
 80     
 81     
 82     public int getEmpno() {
 83         return empno;
 84     }
 85     public void setEmpno(int empno) {
 86         this.empno = empno;
 87     }
 88     public String getEname() {
 89         return ename;
 90     }
 91     public void setEname(String ename) {
 92         this.ename = ename;
 93     }
 94     public String getJob() {
 95         return job;
 96     }
 97     public void setJob(String job) {
 98         this.job = job;
 99     }
100     public int getMgr() {
101         return mgr;
102     }
103     public void setMgr(int mgr) {
104         this.mgr = mgr;
105     }
106     public String getHiredate() {
107         return hiredate;
108     }
109     public void setHiredate(String hiredate) {
110         this.hiredate = hiredate;
111     }
112     public int getSal() {
113         return sal;
114     }
115     public void setSal(int sal) {
116         this.sal = sal;
117     }
118     public int getComm() {
119         return comm;
120     }
121     public void setComm(int comm) {
122         this.comm = comm;
123     }
124     public int getDeptno() {
125         return deptno;
126     }
127     public void setDeptno(int deptno) {
128         this.deptno = deptno;
129     }
130 
131 }

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EmpSortMapper.java

 1 import java.io.IOException;
 2 
 3 import org.apache.hadoop.io.IntWritable;
 4 import org.apache.hadoop.io.LongWritable;
 5 import org.apache.hadoop.io.NullWritable;
 6 import org.apache.hadoop.io.Text;
 7 import org.apache.hadoop.mapreduce.Mapper;
 8 
 9 /*
10  * 一定要把Emp作为key2
11  * 没有value2，返回null值
12  */
13  
14 public class EmpSortMapper extends Mapper<LongWritable, Text, Emp, NullWritable> {
15 
16     @Override
17     protected void map(LongWritable key1, Text value1, Context context)
18             throws IOException, InterruptedException {
19         // 数据：7654,MARTIN,SALESMAN,7698,1981/9/28,1250,1400,30
20         String data = value1.toString();
21         
22         //分词
23         String[] words = data.split(",");
24         
25         //生成员工对象
26         Emp emp = new Emp();
27         emp.setEmpno(Integer.parseInt(words[0]));
28         emp.setEname(words[1]);
29         emp.setJob(words[2]);
30         emp.setMgr(Integer.parseInt(words[3]));
31         emp.setHiredate(words[4]);
32         emp.setSal(Integer.parseInt(words[5]));
33         emp.setComm(Integer.parseInt(words[6]));
34         emp.setDeptno(Integer.parseInt(words[7]));
35         
36         //输出员工对象  k2:员工对象  v2：空值
37         context.write(emp,  NullWritable.get());
38     }
39 }

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EmpSortMain.java

 1 import java.io.IOException;
 2 
 3 import org.apache.hadoop.conf.Configuration;
 4 import org.apache.hadoop.fs.Path;
 5 import org.apache.hadoop.io.IntWritable;
 6 import org.apache.hadoop.io.NullWritable;
 7 import org.apache.hadoop.mapreduce.Job;
 8 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
 9 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
10 
11 public class EmpSortMain {
12 
13     public static void main(String[] args) throws Exception {
14         Job job = Job.getInstance(new Configuration());
15         job.setJarByClass(EmpSortMain.class);
16         
17         job.setMapperClass(EmpSortMapper.class);
18         job.setMapOutputKeyClass(Emp.class); //k2 是员工对象
19         job.setMapOutputValueClass(NullWritable.class);  // v2:是空值
20         
21         job.setOutputKeyClass(Emp.class);
22         job.setOutputValueClass(NullWritable.class);
23         
24         FileInputFormat.setInputPaths(job, new Path(args[0]));
25         FileOutputFormat.setOutputPath(job, new Path(args[1]));
26 
27         job.waitForCompletion(true);
28 
29     }
30 
31 }

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分区（Partition）
- 关系型数据库分区
  - 分区1：sal<=3000，分区2：3000<sal<=5000，分区3：sal>5000
  - 查询薪水1000~2000的员工，只扫描分区1即可
  - Hash分区：根据数值的Hash结果进行分区，如果一样就放入同一个分区中（数据尽量打散，避免热块）
  - Redis Cluster、Hive 桶表、MongoDB 分布式路由
- MR分区
  - 根据Map的输出<key2 value2>进行分区
  - 默认情况下，MR的输出只有一个分区（一个分区就是一个文件）
  - 按部门号分区：不同部门的员工放到不同文件中

Emp.java

  1 import java.io.DataInput;
  2 import java.io.DataOutput;
  3 import java.io.IOException;
  4 
  5 import org.apache.hadoop.io.Writable;
  6 
  7 //代表员工
  8 //数据：7654,MARTIN,SALESMAN,7698,1981/9/28,1250,1400,30
  9 public class Emp implements Writable{
 10     
 11     private int empno;//员工号
 12     private String ename; //员工姓名
 13     private String job; //职位
 14     private int mgr; //经理的员工号
 15     private String hiredate;//入职日期
 16     private int sal; //月薪
 17     private int comm; //奖金
 18     private int deptno; //部门号
 19     
 20         
 21     @Override
 22     public String toString() {
 23         return "Emp [empno=" + empno + ", ename=" + ename + ", sal=" + sal + ", deptno=" + deptno + "]";
 24     }
 25 
 26     @Override
 27     public void readFields(DataInput input) throws IOException {
 28         //实现反序列化，从输入流中读取对象
 29         this.empno = input.readInt();
 30         this.ename = input.readUTF();
 31         this.job = input.readUTF();
 32         this.mgr = input.readInt();
 33         this.hiredate = input.readUTF();
 34         this.sal = input.readInt();
 35         this.comm = input.readInt();
 36         this.deptno = input.readInt();
 37     }
 38     
 39     @Override
 40     public void write(DataOutput output) throws IOException {
 41         // 实现序列化，把对象输出到输出流
 42         output.writeInt(this.empno);
 43         output.writeUTF(this.ename);
 44         output.writeUTF(this.job);
 45         output.writeInt(this.mgr);
 46         output.writeUTF(this.hiredate);
 47         output.writeInt(this.sal);
 48         output.writeInt(this.comm);
 49         output.writeInt(this.deptno);
 50     }
 51     
 52     
 53     public int getEmpno() {
 54         return empno;
 55     }
 56     public void setEmpno(int empno) {
 57         this.empno = empno;
 58     }
 59     public String getEname() {
 60         return ename;
 61     }
 62     public void setEname(String ename) {
 63         this.ename = ename;
 64     }
 65     public String getJob() {
 66         return job;
 67     }
 68     public void setJob(String job) {
 69         this.job = job;
 70     }
 71     public int getMgr() {
 72         return mgr;
 73     }
 74     public void setMgr(int mgr) {
 75         this.mgr = mgr;
 76     }
 77     public String getHiredate() {
 78         return hiredate;
 79     }
 80     public void setHiredate(String hiredate) {
 81         this.hiredate = hiredate;
 82     }
 83     public int getSal() {
 84         return sal;
 85     }
 86     public void setSal(int sal) {
 87         this.sal = sal;
 88     }
 89     public int getComm() {
 90         return comm;
 91     }
 92     public void setComm(int comm) {
 93         this.comm = comm;
 94     }
 95     public int getDeptno() {
 96         return deptno;
 97     }
 98     public void setDeptno(int deptno) {
 99         this.deptno = deptno;
100     }
101 }

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MyPartitionerMain.java

 1 import java.io.IOException;
 2 
 3 import org.apache.hadoop.conf.Configuration;
 4 import org.apache.hadoop.fs.Path;
 5 import org.apache.hadoop.io.IntWritable;
 6 import org.apache.hadoop.mapreduce.Job;
 7 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
 8 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
 9 
10 public class MyPartitionerMain {
11 
12     public static void main(String[] args) throws Exception {
13         Job job = Job.getInstance(new Configuration());
14         job.setJarByClass(MyPartitionerMain.class);
15         
16         job.setMapperClass(MyPartitionerMapper.class);
17         job.setMapOutputKeyClass(IntWritable.class); //k2 是部门号
18         job.setMapOutputValueClass(Emp.class);  // v2输出就是员工对象
19         
20         //加入分区规则
21         job.setPartitionerClass(MyPartitioner.class);    
22         //指定分区的个数
23         job.setNumReduceTasks(3);
24         
25         job.setReducerClass(MyPartitionerReducer.class);
26         job.setOutputKeyClass(IntWritable.class);
27         job.setOutputValueClass(Emp.class);
28         
29         FileInputFormat.setInputPaths(job, new Path(args[0]));
30         FileOutputFormat.setOutputPath(job, new Path(args[1]));
31 
32         job.waitForCompletion(true);
33     }
34 }

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MyPartitionerMapper.java

 1 import java.io.IOException;
 2 
 3 import org.apache.hadoop.io.IntWritable;
 4 import org.apache.hadoop.io.LongWritable;
 5 import org.apache.hadoop.io.Text;
 6 import org.apache.hadoop.mapreduce.Mapper;
 7 
 8 //                                                                 k2 部门号      v2 员工
 9 public class MyPartitionerMapper extends Mapper<LongWritable, Text, IntWritable, Emp> {
10 
11     @Override
12     protected void map(LongWritable key1, Text value1, Context context)
13             throws IOException, InterruptedException {
14         // 数据：7654,MARTIN,SALESMAN,7698,1981/9/28,1250,1400,30
15         String data = value1.toString();
16         
17         //分词
18         String[] words = data.split(",");
19         
20         //生成员工对象
21         Emp emp = new Emp();
22         emp.setEmpno(Integer.parseInt(words[0]));
23         emp.setEname(words[1]);
24         emp.setJob(words[2]);
25         emp.setMgr(Integer.parseInt(words[3]));
26         emp.setHiredate(words[4]);
27         emp.setSal(Integer.parseInt(words[5]));
28         emp.setComm(Integer.parseInt(words[6]));
29         emp.setDeptno(Integer.parseInt(words[7]));
30         
31         //输出员工对象  k2:部门号                                                                     v2：员工对象
32         context.write(new IntWritable(emp.getDeptno()), emp);
33     }
34 }

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MyPartitionerReducer.java

 1 import java.io.IOException;
 2 
 3 import org.apache.hadoop.io.IntWritable;
 4 import org.apache.hadoop.mapreduce.Reducer;
 5 
 6 //就是同一个部门的员工
 7 public class MyPartitionerReducer extends Reducer<IntWritable, Emp, IntWritable, Emp> {
 8 
 9     @Override
10     protected void reduce(IntWritable k3, Iterable<Emp> v3,Context context) throws IOException, InterruptedException {
11         // 直接输出
12         for(Emp e:v3){
13             context.write(k3, e);
14         }
15     }
16 }

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合并（Combiner）
- 一种特殊的Reducer，部署在Mapper端
- 在Mapper端执行一次合并，用于减少Mapper输出到Reducer的数据量，提高效率
- 在wordCountMain.java中添加job.setCombinerClass(WordCountReducer.class)
- 谨慎使用Combiner，有些情况不能使用（如求平均值）
- 不管有没有Combiner，都不能改变Map和Reduce对应的数据类型
- 程序出错，可将Reducer的k3 类型改为DoubleWritable

AvgSalaryMain.java

 1 import java.io.IOException;
 2 
 3 import org.apache.hadoop.conf.Configuration;
 4 import org.apache.hadoop.fs.Path;
 5 import org.apache.hadoop.io.DoubleWritable;
 6 import org.apache.hadoop.io.IntWritable;
 7 import org.apache.hadoop.io.Text;
 8 import org.apache.hadoop.mapreduce.Job;
 9 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
10 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
11 
12 public class AvgSalaryMain {
13 
14     public static void main(String[] args) throws Exception {
15         //1、创建任务、指定任务的入口 
16         Job job = Job.getInstance(new Configuration());
17         job.setJarByClass(AvgSalaryMain.class);
18         
19         //2、指定任务的map和map输出的数据类型
20         job.setMapperClass(AvgSalaryMapper.class);
21         job.setMapOutputKeyClass(Text.class);
22         job.setMapOutputValueClass(IntWritable.class);
23         
24         //加入Combiner
25         job.setCombinerClass(AvgSalaryReducer.class);
26         
27         //3、指定任务的reducer和reducer输出的类型
28         job.setReducerClass(AvgSalaryReducer.class);
29         job.setOutputKeyClass(Text.class);
30         job.setOutputValueClass(DoubleWritable.class);
31         
32         //4、指定任务输入路径和输出路径
33         FileInputFormat.setInputPaths(job, new Path(args[0]));
34         FileOutputFormat.setOutputPath(job, new Path(args[1]));
35         
36         //5、执行任务
37         job.waitForCompletion(true);
38 
39     }
40 }

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AvgSalaryMapper.java

 1 import java.io.IOException;
 2 
 3 import org.apache.hadoop.io.IntWritable;
 4 import org.apache.hadoop.io.LongWritable;
 5 import org.apache.hadoop.io.Text;
 6 import org.apache.hadoop.mapreduce.Mapper;
 7 
 8 //                                                              k2 常量   v2：薪水
 9 public class AvgSalaryMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
10 
11     @Override
12     protected void map(LongWritable key1, Text value1, Context context)
13             throws IOException, InterruptedException {
14         // 数据：7654,MARTIN,SALESMAN,7698,1981/9/28,1250,1400,30
15         String data = value1.toString();
16         
17         //分词
18         String[] words = data.split(",");
19         
20         //输出  k2 常量   v2 薪水
21         context.write(new Text("salary"), new IntWritable(Integer.parseInt(words[5])));
22     }
23 }

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AvgSalaryReducer.java

 1 import java.io.IOException;
 2 
 3 import org.apache.hadoop.io.DoubleWritable;
 4 import org.apache.hadoop.io.IntWritable;
 5 import org.apache.hadoop.io.Text;
 6 import org.apache.hadoop.mapreduce.Reducer;
 7 
 8 //                                                                     v4：平均工资
 9 public class AvgSalaryReducer extends Reducer<Text, IntWritable, Text, DoubleWritable> {
10 
11     @Override
12     protected void reduce(Text k3, Iterable<IntWritable> v3,Context context) throws IOException, InterruptedException {
13         int total = 0;
14         int count = 0;
15         
16         for(IntWritable salary:v3){
17             //工资求和
18             total = total + salary.get();
19             //人数加一
20             count ++;
21         }
22         
23         //输出
24         context.write(new Text("The avg salary is :"), new DoubleWritable(total/count));
25     }
26 
27 }

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Shuffle

MapReduce的核心
map输出后到reduce接收前
Hadoop 3.x以前：会有数据落地（I/O操作）
Spark只有两次I/O操作（读+写），中间运算在内存中完成

Yarn

MapReduce 2.0 后运行在Yarn上
默认NodeManager和DataNode在一台机器上

MRUnit

类似JUnit
添加相应jar包，去掉mockito-all-1.8.5.jar
用Hadoop在windows上的安装包设置环境变量

WordCountMapper.java

 1 import java.io.IOException;
 2 
 3 import org.apache.hadoop.io.IntWritable;
 4 import org.apache.hadoop.io.LongWritable;
 5 import org.apache.hadoop.io.Text;
 6 import org.apache.hadoop.mapreduce.Mapper;
 7 
 8 //实现Map的功能
 9 //                                           k1           v1      k2      v2
10 public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
11 
12     @Override
13     protected void map(LongWritable key1, Text value1, Context context)
14             throws IOException, InterruptedException {
15         /*
16          * context: map的上下文
17          * 上文：HDFS
18          * 下文：Reducer
19          */
20         //得到数据   I love Beijing
21         String data = value1.toString();
22         
23         //分词
24         String[] words = data.split(" ");
25         
26         //输出    k2     v2
27         for(String w:words){
28             //             k2            v2
29             context.write(new Text(w), new IntWritable(1));
30         }
31     }
32 }

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WordCountReducer.java

 1 import java.io.IOException;
 2 
 3 import org.apache.hadoop.io.IntWritable;
 4 import org.apache.hadoop.io.Text;
 5 import org.apache.hadoop.mapreduce.Reducer;
 6 
 7 //实现Reducer的功能
 8 //                                             k3      v3         k4       v4
 9 public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
10 
11     @Override
12     protected void reduce(Text k3, Iterable<IntWritable> v3,Context context) throws IOException, InterruptedException {
13         /*
14          * context是Reducer的上下文
15          * 上文：Map
16          * 下文：HDFS
17          */
18         int total = 0;
19         for(IntWritable v:v3){
20             //求和
21             total = total + v.get();
22         }
23         
24         //输出  k4  v4
25         context.write(k3, new IntWritable(total));
26     }
27 }

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WordCountUnitTest.java

 1 import java.util.ArrayList;
 2 import java.util.List;
 3 
 4 import org.apache.hadoop.io.IntWritable;
 5 import org.apache.hadoop.io.LongWritable;
 6 import org.apache.hadoop.io.Text;
 7 import org.apache.hadoop.mrunit.mapreduce.MapDriver;
 8 import org.apache.hadoop.mrunit.mapreduce.MapReduceDriver;
 9 import org.apache.hadoop.mrunit.mapreduce.ReduceDriver;
10 import org.junit.Test;
11 
12 public class WordCountUnitTest {
13 
14     @Test
15     public void testMapper() throws Exception{
16         /*
17          * java.io.IOException: Could not locate executable nullinwinutils.exe in the Hadoop binaries
18          * 设置环境变量就可以，用到Hadoop在windows上的安装包
19          */
20         System.setProperty("hadoop.home.dir", "E:\tools\hadoop-common-2.2.0-bin-master");
21         
22         //创建一个Map的对象：测试对象
23         WordCountMapper mapper = new WordCountMapper();
24         
25         //创建一个MapDriver进行单元测试
26         MapDriver<LongWritable, Text, Text, IntWritable> driver = new MapDriver<>(mapper);
27         
28         //指定map的输入：k1  v1
29         driver.withInput(new LongWritable(1), new Text("I love Beijing"));
30         
31         //指定map的输出： k2  v2  ------> 我们期望得到结果
32         driver.withOutput(new Text("I"), new IntWritable(1))
33               .withOutput(new Text("love"), new IntWritable(1))
34               .withOutput(new Text("Beijing"), new IntWritable(1));
35         
36         //执行单元测试：对比  期望的结果  和  实际的结果
37         driver.runTest();
38     }
39     
40     @Test
41     public void testReducer() throws Exception{
42         System.setProperty("hadoop.home.dir", "D:\temp\hadoop-2.4.1\hadoop-2.4.1");
43         
44         //创建一个测试对象
45         WordCountReducer reducer = new WordCountReducer();
46         
47         // 创建一个Driver
48         ReduceDriver<Text, IntWritable, Text, IntWritable> driver = new ReduceDriver<>(reducer);
49         
50         //指定Driver的输入：k3  v3
51         //构造一下v3 是一个集合
52         List<IntWritable> value3 = new ArrayList<>();
53         value3.add(new IntWritable(1));
54         value3.add(new IntWritable(1));
55         value3.add(new IntWritable(1));
56         
57         driver.withInput(new Text("Beijing"), value3);
58         
59         //指定输出的数据  指定  k4  v4
60         driver.withOutput(new Text("Beijing"), new IntWritable(3));
61         
62         //执行单元测试
63         driver.runTest();
64     }
65     
66     @Test
67     public void testJob() throws Exception{
68         System.setProperty("hadoop.home.dir", "D:\temp\hadoop-2.4.1\hadoop-2.4.1");
69         
70         //创建测试的对象
71         WordCountMapper mapper = new WordCountMapper();
72         WordCountReducer reducer = new WordCountReducer();
73         
74         //创建一个Driver
75         //MapReduceDriver<K1, V1, K2, V2, K4, V4>
76         MapReduceDriver<LongWritable, Text, Text, IntWritable,Text, IntWritable>
77             driver = new MapReduceDriver<>(mapper,reducer);
78         
79         //指定Map的输入
80         driver.withInput(new LongWritable(1), new Text("I love Beijing"))
81               .withInput(new LongWritable(4), new Text("I love China"))
82               .withInput(new LongWritable(6), new Text("Beijing is the capital of China"));
83         
84         //指定Reducer的输出
85         driver.withOutput(new Text("Beijing"), new IntWritable(2))
86               .withOutput(new Text("China"), new IntWritable(2))
87               .withOutput(new Text("I"), new IntWritable(2))
88               .withOutput(new Text("capital"), new IntWritable(1))
89               .withOutput(new Text("is"), new IntWritable(1))
90               .withOutput(new Text("love"), new IntWritable(2))
91               .withOutput(new Text("of"), new IntWritable(1))
92               .withOutput(new Text("the"), new IntWritable(1));
93         
94         driver.runTest();
95     }
96 }

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参考

Hadoop 新 MapReduce 框架 Yarn 详解

https://www.ibm.com/developerworks/cn/opensource/os-cn-hadoop-yarn/