分区
将输入的csv按照员工号拆分成每个员工，每个员工存储为员工对象，之后按每个员工的不同部门存储
- pom
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"><modelVersion>4.0.0</modelVersion><groupId>com.hadoop</groupId><artifactId>Mapreduce_partition</artifactId><version>1.0-SNAPSHOT</version><name>Mapreduce_partition</name><description>wunaiieq</description><properties><maven.compiler.source>8</maven.compiler.source><maven.compiler.target>8</maven.compiler.target><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><!--版本控制--><hadoop.version>2.7.3</hadoop.version></properties><dependencies><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-common</artifactId><version>${hadoop.version}</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-hdfs</artifactId><version>${hadoop.version}</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-mapreduce-client-core</artifactId><version>${hadoop.version}</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-client</artifactId><version>${hadoop.version}</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-yarn-api</artifactId><version>${hadoop.version}</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-streaming</artifactId><version>${hadoop.version}</version></dependency></dependencies><!--构建配置--><build><plugins><plugin><!--声明--><groupId>org.apache.maven.plugins</groupId><artifactId>maven-assembly-plugin</artifactId><version>3.3.0</version><!--具体配置--><configuration><archive><manifest><!--jar包的执行入口--><mainClass>com.hadoop.Main</mainClass></manifest></archive><descriptorRefs><!--描述符，此处为预定义的，表示创建一个包含项目所有依赖的可执行 JAR 文件;允许自定义生成jar文件内容--><descriptorRef>jar-with-dependencies</descriptorRef></descriptorRefs></configuration><!--执行配置--><executions><execution><!--执行配置ID，可修改--><id>make-assembly</id><!--执行的生命周期--><phase>package</phase><goals><!--执行的目标，single表示创建一个分发包--><goal>single</goal></goals></execution></executions></plugin></plugins></build></project>- main
package com.hadoop;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Job;
import java.io.IOException;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;public class Main {public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {Job job = Job.getInstance(new Configuration());job.setJarByClass(Main.class);//mapjob.setMapperClass(Map_1.class);job.setMapOutputKeyClass(IntWritable.class);//k2job.setMapOutputValueClass(Employee.class);//v2//指定分区规则job.setPartitionerClass(partition.class);//分区个数,此处的形参3传递给partition中的numjob.setNumReduceTasks(3);//Reducejob.setReducerClass(Reduce_1.class);//输出job.setOutputKeyClass(IntWritable.class);job.setOutputValueClass(Employee.class);//输入和输出FileInputFormat.setInputPaths(job,new Path(args[0]));FileOutputFormat.setOutputPath(job,new Path(args[1]));//执行job.waitForCompletion(true);}
}- Map_1
package com.hadoop;import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;import java.io.IOException;
//1,ZhangSan,101,5000
public class Map_1 extends Mapper<LongWritable, Text, IntWritable, Employee> {@Overrideprotected void map(LongWritable k1, Text v1, Context context)throws IOException, InterruptedException {//获取数据String data = v1.toString();//分词String[] words =data.split(",");Employee e=new Employee();//设置v2的输出内容（输出内容为对象e，这里的区别是每个对象不同，以下为属性设置）e.setId(Integer.parseInt(words[0]));e.setName(words[1]);e.setDepartment_id(Integer.parseInt(words[2]));e.setSalary(Integer.parseInt(words[3]));context.write(new IntWritable(e.getDepartment_id()),e);}
}- Reduce_1
package com.hadoop;import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Reducer;import java.io.IOException;public class Reduce_1 extends Reducer<IntWritable,Employee,IntWritable,Employee> {@Overrideprotected void reduce(IntWritable k3, Iterable<Employee> v3,Context context)throws IOException, InterruptedException {for (Employee e:v3){context.write(k3,e);}}
}- partition
package com.hadoop;import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Partitioner;//分区规则，根据map输出
public class partition extends Partitioner<IntWritable,Employee> {//k2,v2,分区个数@Overridepublic int getPartition(IntWritable k2, Employee v2, int num) {int department_Id= v2.getDepartment_id();//按照部门号存储不同分区if (department_Id==101){return 1%num;}else if (department_Id ==102){return 2%num;}else {return 3%num;}}
}- 效果
输出日志，显示4个输出文件
dfs输出的文件目录
存储效果
)
