Mapreduce_Distinct数据去重

MapReduce中数据去重

输入如下的数据，统计其中的地址信息，并对输出的地址信息进行去重

实现方法：Map阶段输出的信息K2为想要去重的内容，利用Reduce阶段的聚合特点，对K2进行聚合，去重。在两阶段中，V2，V3，V4为Null

distinct.csv

John,30,New York
Jane,25,Los Angeles
Tom,33,New York
Doe,45,Chicago
Sam,26,Los Angeles

1. 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.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.streaming.io.InputWriter;



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

        job.setMapperClass(Map.class);
        job.setMapOutputKeyClass(Text.class);//k2
        job.setMapOutputValueClass(NullWritable.class);//v2

        job.setReducerClass(Reduce.class);
        job.setOutputKeyClass(Text.class);//k4
        job.setOutputValueClass(NullWritable.class);//v4


        FileInputFormat.setInputPaths(job,new Path(args[0]));
        FileOutputFormat.setOutputPath(job,new Path(args[1]));

        job.waitForCompletion(true);

    }
}

2. Map

package com.hadoop;

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;
                                                    //Los Angeles
public class Map extends Mapper<LongWritable, Text,Text, NullWritable> {
   //Jane,25,Los Angeles
    @Override
    protected void map(LongWritable k1, Text v1,Context context)
            throws IOException, InterruptedException {
        String data =v1.toString();
        String words[]=data.split(",");

        context.write(new Text(words[2]),NullWritable.get());
    }
}

3. Reduce

package com.hadoop;

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

import java.io.IOException;

public class Reduce extends Reducer<Text, NullWritable,Text,NullWritable> {
    @Override
    protected void reduce(Text k3, Iterable<NullWritable> v3,Context context)
            throws IOException, InterruptedException {
        context.write(k3,NullWritable.get());
    }
}

4. 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_Distinct</artifactId>
    <version>1.0-SNAPSHOT</version>

    <name>Mapreduce_Distinct</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>

5. 效果