使用MapReduce运行单词统计案例

朴灿烈づ我的快乐病毒、 2022-11-14 13:19 181阅读 0赞

1.Map阶段的核心处理逻辑需要编写在Mapper中  
2.Reduce阶段的核心处理逻辑需要编写在Reducer中  
3.将编写的Mapper和Reducer进行组合，组合成一个Job  
4.对Job进行设置，设置后运行

准备数据文件

需求：统计文件中单词出现的次数

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zNDUwMzUyNg_size_16_color_FFFFFF_t_70][]

## 1.创建WordCountMapper类，继承Mapper，重写map 方法 ##

Mapper<LongWritable, Text, Text, IntWritable> 两两一组,输入，输出  
  LongWritable (偏移量) Text (一行数据)  
 Text(key值,单词), IntWritable(value值，数量)

>   
> import lombok.SneakyThrows;  
> import org.apache.hadoop.io.IntWritable;  
> import org.apache.hadoop.io.LongWritable;  
> import org.apache.hadoop.io.Text;  
> import org.apache.hadoop.mapreduce.Mapper;
> 
> /\*\*  
>  \* wordCountMapper  
>  \*  
>  \* @author   
>  \* @description:  
>  \* @time: 2021/3/24 15:07  
>  \*/  
> public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> \{  
>     /\*\*  
>      \* 输出key  
>      \*/  
>     private Text out\_key = new Text();  
>     /\*\*  
>      \* 输出value  
>      \* 每个单词出现一次记为1  
>      \*/  
>     private IntWritable out\_value = new IntWritable(1);
> 
>     @SneakyThrows  
>     @Override  
>     protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context) \{  
>         /\*\*  
>          \* value为一行数据转成字符串  
>          \*  hello world text 第一行数据  
>          \*  根据"\\t"空格分隔  
>          \*/  
>         String\[\] words = value.toString().split("\\t");
> 
>         for (String word : words) \{  
>             out\_key.set(word);  
>             /\*\*  
>              \* 写出数据(单词,1)  
>              \*/  
>             context.write(out\_key, out\_value);  
>         \}
> 
>     \}  
> \}

##  2.创建WordCountReducer类，继承Reducer，重写reduce方法 ##

>     import lombok.SneakyThrows;
>     import org.apache.hadoop.io.IntWritable;
>     import org.apache.hadoop.io.Text;
>     import org.apache.hadoop.mapreduce.Reducer;
>     
>     /**
>      * @author
>      * @description:
>      * @time: 2021/3/24 15:10
>      */
>     public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
>         private IntWritable out_value = new IntWritable();
>     
>         /**
>          * reduce一次处理一组数据，key相同的视为一组
>          *
>          * @param key
>          * @param values  相当于集合
>          * @param context
>          */
>         @SneakyThrows
>         @Override
>         protected void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context) {
>     
>             int sum = 0;
>             /**
>              * 遍历 values ，统计次数
>              */
>             for (IntWritable intWritable : values) {
>                 sum += intWritable.get();
>             }
>     
>             out_value.set(sum);
>     
>             //将累加的值写出
>             context.write(key, out_value);
>     
>         }
>     }

## 3.创建JOB WordCountDriver ##

>     import com.atguigu.demomptest.service.mapper.WordCountMapper;
>     import com.atguigu.demomptest.service.reducer.WordCountReducer;
>     import org.apache.hadoop.conf.Configuration;
>     import org.apache.hadoop.fs.FileSystem;
>     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;
>     
>     
>     /**
>      * ①Map阶段的核心处理逻辑需要编写在Mapper中
>      * ②Reduce阶段的核心处理逻辑需要编写在Reducer中
>      * ③将编写的Mapper和Reducer进行组合，组合成一个Job
>      * ④对Job进行设置，设置后运行
>      *
>      * @author 
>      * @description:
>      * @time: 2021/3/24 15:22
>      */
>     public class WordCountDriver {
>         public static void main(String[] args) throws Exception {
>             Path inputPath = new Path("d:/input/wordcount");
>             //保证输出目录不存在
>             Path outputPath = new Path("d:/output/wordcount");
>     
>             //作为整个Job的配置
>             //空参表示默认使用本地的文件系统
>             Configuration conf = new Configuration();
>            
>             //一定要保证输出目录不存在
>             FileSystem fs = FileSystem.get(conf);
>     
>             if (fs.exists(outputPath)) {
>                 fs.delete(outputPath, true);
>             }
>             // ①创建Job
>             Job job = Job.getInstance(conf);
>     
>             // 将某个类所在地jar包作为job的jar包
>             job.setJarByClass(WordCountDriver.class);
>     
>             // ②设置Job
>             // 设置Job运行的Mapper，Reducer类型，Mapper,Reducer输出的key-value类型
>             job.setMapperClass(WordCountMapper.class);
>             job.setReducerClass(WordCountReducer.class);
>             // Job需要根据Mapper和Reducer输出的Key-value类型准备序列化器，通过序列化器对输出的key-value进行序列化和反序列化
>             // 如果Mapper和Reducer输出的Key-value类型一致，直接设置Job最终的输出类型
>             job.setOutputKeyClass(Text.class);
>             job.setOutputValueClass(IntWritable.class);
>     
>             // 设置输入目录和输出目录
>             FileInputFormat.setInputPaths(job, inputPath);
>             FileOutputFormat.setOutputPath(job, outputPath);
>     
>             // ③运行Job
>             job.waitForCompletion(true);
>     
>         }
>     }

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zNDUwMzUyNg_size_16_color_FFFFFF_t_70]: /images/20221022/29872ff35a684dbaba4796dd3e30745c.png