相信大家看了博主上一篇博客《什么是MapReduce
》后,对MapReduce的概念有了更深的认知!本篇博客,博主给大家带来的是MapReduce的一个简单的实战项目——统计输出给定的文本文档每一个单词出现的总次数。

在进行之前我们先看一下我们的数据源：

1. 创建Maven工程

下面的跟之前使用API一样,我们同样需要在IDEA中使用JAVA代码来书写MapReduce。这时候我们需要新建一个一个Maven工程

• 1. 创建项目

• 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> <dependency> <groupId>jdk.tools</groupId> <artifactId>jdk.tools</artifactId> <version>1.8</version> <scope>system</scope> <systemPath>D:/java/jdk-1.8.0/lib/tools.jar</systemPath> </dependency> </dependencies> 

• 3. 在项目的src/main/resources目录下，新建一个文件，命名为“log4j.properties”

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  

2. 编写程序

2.1 编写Mapper类

package com.buwenbuhuo.wordcount; 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; /**  * @author 卜温不火  * @create 2020-04-22 21:24  * com.buwenbuhuo.wordcount - the name of the target package where the new class or interface will be created.  * mapreduce0422 - the name of the current project.  */ public class WcMapper 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); } } } 

2.2 编写Reducer类

package com.buwenbuhuo.wordcount; import java.io.IOException; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; /**  * @author 卜温不火  * @create 2020-04-22 21:24  * com.buwenbuhuo.wordcount - the name of the target package where the new class or interface will be created.  * mapreduce0422 - the name of the current project.  */ public class WcReducer 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); } } 

2.3 编写Driver驱动类

package com.buwenbuhuo.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; /**  * @author 卜温不火  * @create 2020-04-22 21:24  * com.buwenbuhuo.wordcount - the name of the target package where the new class or interface will be created.  * mapreduce0422 - the name of the current project.  */ public class WcDriver { 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(WcDriver.class); // 3 设置map和reduce类         job.setMapperClass(WcMapper.class);         job.setReducerClass(WcReducer.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); } } 

2.4 运行

• 1.但是如果现在直接运行得话，会出现如下错误：

• 2.这是因为缺少了原始文件和要输出的目录，这是我们可以通过下列方法进行解决
  
  
• 3. 再次运行

成功的截图

• 4. 下面我们来看下运行的结果

打开进入并用Notepad++ 打开文件查看内容!发现统计的结果已经呈现在里面了!说明我们的程序运行成功了!

过程梳理:
每读取一行数据,MapReduce就会调用一次map方法,在map方法中我们把每行数据用空格” “分隔成一个数组,遍历数组,把数组中的每一个元素作为key,1作为value作为map的输出传递给reduce。reduce把收集到的数据根据key值进行分区,把每个分区的内容进行单独计算，并把结果输出。

本次的就到这里了,受益的小伙伴们不要忘了加关注呀，下一期博主将为大家继续带来MapReduce中如何打包jar包，并在集群上运行的博文，敬请期待。