大数据之Hadoop(MapReduce):WordCount案例实操

Posted 2021-06-20 浊酒南街

目录

• 1．需求
• 2．需求分析
• 3．环境准备

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）期望输出数据

jinghang	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

3．环境准备

（1）创建Maven项目
（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>

（3）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

用于屏蔽日志；
4．编写程序
（1）编写Mapper类

package com.jinghang.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.jinghang.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.jinghang.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);
	}
}