MapReduce之WordCount本地测试-Toy模板网

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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
MapReduce之WordCount本地测试

2）期望输出数据

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。

3）环境准备

（1）创建maven工程，MapReduceDemo
（2）在pom.xml文件中添加如下依赖

<dependencies>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-client</artifactId>
        <version>3.1.3</version>
    </dependency>
    <dependency>
        <groupId>junit</groupId>
        <artifactId>junit</artifactId>
        <version>4.12</version>
    </dependency>
    <dependency>
        <groupId>org.slf4j</groupId>
        <artifactId>slf4j-log4j12</artifactId>
        <version>1.7.30</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

（3）创建包名：com.xiaoming.mapreduce.wordcount

4）编写程序

（1）编写Mapper类

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

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

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 获取配置信息以及获取job对象
		Configuration conf = new Configuration();
		Job job = Job.getInstance(conf);

		// 2 关联本Driver程序的jar
		job.setJarByClass(WordCountDriver.class);

		// 3 关联Mapper和Reducer的jar
		job.setMapperClass(WordCountMapper.class);
		job.setReducerClass(WordCountReducer.class);

		// 4 设置Mapper输出的kv类型
		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job
		boolean result = job.waitForCompletion(true);
		System.exit(result ? 0 : 1);
	}
}