csv文件求某个平均数据
查询每个部门的平均工资，最后输出
数据处理过程
employee_noheader.csv（没做关于首行的处理，运行时请自行删除）
EmployeeID,EmployeeName,DepartmentID,Salary
1,ZhangSan,101,5000
2,LiSi,102,6000
3,WangWu,101,5500
4,ZhaoLiu,103,7000
5,SunQi,102,6500
- pom.xml
<?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_csv_average</artifactId><version>1.0-SNAPSHOT</version><name>Mapreduce_csv_average</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>- Map_1
package com.hadoop;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 Map_1 extends Mapper<LongWritable, Text,IntWritable,IntWritable> {@Overrideprotected void map(LongWritable k1, Text v1, Context context)throws IOException, InterruptedException {//处理输入数据，类型转换//以 1,ZhangSan,101,5000 为例String data =v1.toString();//分词操作,csv用","进行分割//一般而言，分词操作大多使用String进行获取，后面可以附跟类型转换String[] words =data.split(",");//下文输出context.write(//K2:部门号输出new IntWritable(Integer.parseInt(words[2])),//K3:工资输出new IntWritable(Integer.parseInt(words[3])));}
}- Reduce_1
package com.hadoop;import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.io.IntWritable;
import java.io.IOException;
public class Reduce_1 extends Reducer<IntWritable,IntWritable,IntWritable,IntWritable>{@Overrideprotected void reduce(IntWritable k3, Iterable<IntWritable> v3, Context context)throws IOException, InterruptedException {//对v3进行求和，计算总额int total=0;int i=0;for (IntWritable v:v3){total+= v.get();i++;}int average=total/i;context.write(k3,new IntWritable(average));}
}- Main
package com.hadoop;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapreduce.Job;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
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(IntWritable.class);//v2//reducejob.setReducerClass(Reduce_1.class);job.setOutputKeyClass(IntWritable.class);job.setOutputValueClass(IntWritable.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);}
}- 运行
请自行上传至hdfs中
hadoop jar Mapreduce_average.jar /input/employee_noheader.csv /output/csv_average
- 效果
hdfs dfs -cat /output/csv_average/part-r-00000
