尚硅谷大数据技术之Hadoop(MapReduce)(新)第1章 MapReduce概述

1.7 MapReduce编程规范

用户编写的程序分成三个部分:Mapper、Reducer和Driver。

1.8 WordCount案例实操

1.需求

在给定的文本文件中统计输出每一个单词出现的总次数

(1)输入数据

atguigu atguigu
ss ss
cls cls
jiao
banzhang
xue
hadoop

(2)期望输出数据

atguigu 2

banzhang 1

cls 2

hadoop 1

jiao 1

ss 2

xue 1

2.需求分析

按照MapReduce编程规范,分别编写Mapper,Reducer,Driver,如图4-2所示。

图4-2 需求分析

3.环境准备

(1)创建maven工程

(2)在pom.xml文件中添加如下依赖

<dependencies>

<dependency>

<groupId>junit</groupId>

<artifactId>junit</artifactId>

<version>RELEASE</version>

</dependency>

<dependency>

<groupId>org.apache.logging.log4j</groupId>

<artifactId>log4j-core</artifactId>

<version>2.8.2</version>

</dependency>

<dependency>

<groupId>org.apache.hadoop</groupId>

<artifactId>hadoop-common</artifactId>

<version>2.7.2</version>

</dependency>

<dependency>

<groupId>org.apache.hadoop</groupId>

<artifactId>hadoop-client</artifactId>

<version>2.7.2</version>

</dependency>

<dependency>

<groupId>org.apache.hadoop</groupId>

<artifactId>hadoop-hdfs</artifactId>

<version>2.7.2</version>

</dependency>

</dependencies>

(2)在项目的src/main/resources目录下,新建一个文件,命名为“log4j.properties”,在文件中填入。

log4j.rootLogger=INFO, stdout

log4j.appender.stdout=org.apache.log4j.ConsoleAppender

log4j.appender.stdout.layout=org.apache.log4j.PatternLayout

log4j.appender.stdout.layout.ConversionPattern=%d %p [%c] - %m%n

log4j.appender.logfile=org.apache.log4j.FileAppender

log4j.appender.logfile.File=target/spring.log

log4j.appender.logfile.layout=org.apache.log4j.PatternLayout

log4j.appender.logfile.layout.ConversionPattern=%d %p [%c] - %m%n

4.编写程序

(1)编写Mapper类

package com.atguigu.mapreduce;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

 

public class WordcountMapper extends Mapper<LongWritable, Text, Text, IntWritable>{

Text k = new Text();

IntWritable v = new IntWritable(1);

@Override

protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

// 1 获取一行

String line = value.toString();

// 2 切割

String[] words = line.split(" ");

// 3 输出

for (String word : words) {

k.set(word);

context.write(k, v);

}

}

}

(2)编写Reducer类

package com.atguigu.mapreduce.wordcount;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

 

public class WordcountReducer extends Reducer<Text, IntWritable, Text, IntWritable>{

 

int sum;

IntWritable v = new IntWritable();

 

@Override

protected void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {

// 1 累加求和

sum = 0;

for (IntWritable count : values) {

sum += count.get();

}

// 2 输出

       v.set(sum);

context.write(key,v);

}

}

(3)编写Driver驱动类

package com.atguigu.mapreduce.wordcount;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

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 WordcountDriver {

 

public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {

 

// 1 获取配置信息以及封装任务

Configuration configuration = new Configuration();

Job job = Job.getInstance(configuration);

 

// 2 设置jar加载路径

job.setJarByClass(WordcountDriver.class);

 

// 3 设置map和reduce类

job.setMapperClass(WordcountMapper.class);

job.setReducerClass(WordcountReducer.class);

 

// 4 设置map输出

job.setMapOutputKeyClass(Text.class);

job.setMapOutputValueClass(IntWritable.class);

 

// 5 设置最终输出kv类型

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

// 6 设置输入和输出路径

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

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

 

// 7 提交

boolean result = job.waitForCompletion(true);

 

System.exit(result ? 0 : 1);

}

}

5.本地测试

(1)如果电脑系统是win7的就将win7的hadoop jar包解压到非中文路径,并在Windows环境上配置HADOOP_HOME环境变量。如果是电脑win10操作系统,就解压win10的hadoop jar包,并配置HADOOP_HOME环境变量。

注意:win8电脑和win10家庭版操作系统可能有问题,需要重新编译源码或者更改操作系统。

(2)在Eclipse/Idea上运行程序

6.集群上测试

(0)用maven打jar包,需要添加的打包插件依赖

注意:标记红颜色的部分需要替换为自己工程主类

<build>

<plugins>

<plugin>

<artifactId>maven-compiler-plugin</artifactId>

<version>2.3.2</version>

<configuration>

<source>1.8</source>

<target>1.8</target>

</configuration>

</plugin>

<plugin>

<artifactId>maven-assembly-plugin </artifactId>

<configuration>

<descriptorRefs>

<descriptorRef>jar-with-dependencies</descriptorRef>

</descriptorRefs>

<archive>

<manifest>

<mainClass>com.atguigu.mr.WordcountDriver</mainClass>

</manifest>

</archive>

</configuration>

<executions>

<execution>

<id>make-assembly</id>

<phase>package</phase>

<goals>

<goal>single</goal>

</goals>

</execution>

</executions>

</plugin>

</plugins>

</build>

注意:如果工程上显示红叉。在项目上右键->maven->update project即可。

(1)将程序打成jar包,然后拷贝到Hadoop集群中

步骤详情:右键->Run as->maven install。等待编译完成就会在项目的target文件夹中生成jar包。如果看不到。在项目上右键-》Refresh,即可看到。修改不带依赖的jar包名称为wc.jar,并拷贝该jar包到Hadoop集群。

(2)启动Hadoop集群

(3)执行WordCount程序

[atguigu@hadoop102 software]$ hadoop jar  wc.jar

 com.atguigu.wordcount.WordcountDriver /user/atguigu/input /user/atguigu/output