【云计算与大数据计算】Hadoop MapReduce实战之统计每个单词出现次数、单词平均长度、Grep（附源码）

男娘i 2023-09-30 17:49 8阅读 0赞

> **需要全部代码请点赞关注收藏后评论区留言私信~~~**

下面通过WordCount，WordMean等几个例子讲解MapReduce的实际应用，编程环境都是以Hadoop MapReduce为基础

## 一、WordCount ##

WordCount用于计算文件中每个单词出现的次数，非常适合采用MapReduce进行处理，处理单词计数问题的思路很简单，在 Map阶段处理每个文本split中的数据,产生<word,1> 这样的键-值对，在Reduce阶段对相同的关键字求和,最后生成所有的单词计数 。

运行示意图如下

![8b63c9c2aa9f426abfe3f9e4646930b9.png][]

运行结果如下

![62a83f7f9af74dc2856b8930470c4fda.png][]

## 二、WordMean ##

对上面例子的代码稍作修改，改成计算所有文件中单词的平均长度，单词长度的定义是单词的字符个数，现在HDFS集群中有大量的文件，需要统计所有文件中所出现单词的平均长度。

## 三、Grep ##

还是进行大规模文本中单词的相关操作，现在希望提供类似Linux系统中的Grep命令的功能，找出匹配目标串的所有文件，并统计出每个文件中出现目标字符串的个数。

在 Map阶段根据提供的文件split信息、给定的每个字符串输出 <filename,1> 这样 的键-值对信息

在 Reduce阶段根据filename对 Map阶段产生的结果进行合并

运行效果如下

![70f60e41c38c4436995524e1c0d2cce2.png][]

## 四、代码 ##

部分代码如下 全部代码请点赞关注收藏后评论区留言私信~

package alibook.odps;
    
    import java.io.IOException;
    import java.util.Iterator;
    
    import com.aliyun.odps.data.Record;
    import com.aliyun.odps.data.TableInfo;
    import com.aliyun.odps.mapred.JobClient;
    import com.aliyun.odps.mapred.MapperBase;
    import com.aliyun.odps.mapred.ReducerBase;
    import com.aliyun.odps.mapred.conf.JobConf;
    import com.aliyun.odps.mapred.utils.InputUtils;
    import com.aliyun.odps.mapred.utils.OutputUtils;
    import com.aliyun.odps.mapred.utils.SchemaUtils;
    
    public class wordcount {
    
      public static class TokenizerMapper extends MapperBase {
        private Record word;
        private Record one;
    
        @Override
        public void setup(TaskContext context) throws IOException {
          word = context.createMapOutputKeyRecord();
          one = context.createMapOutputValueRecord();
          one.set(new Object[] { 1L });
          System.out.println("TaskID:" + context.getTaskID().toString());
        }
    
        @Override
        public void map(long recordNum, Record record, TaskContext context)
            throws IOException {
          for (int i = 0; i < record.getColumnCount(); i++) {
            word.set(new Object[] { record.get(i).toString() });
            context.write(word, one);
          }
        }
      }
    
      /**
       * A combiner class that combines map output by sum them.
       **/
      public static class SumCombiner extends ReducerBase {
        private Record count;
    
        @Override
        public void setup(TaskContext context) throws IOException {
          count = context.createMapOutputValueRecord();
        }
    
        @Override
        public void reduce(Record key, Iterator<Record> values, TaskContext context)
            throws IOException {
          long c = 0;
          while (values.hasNext()) {
            Record val = values.next();
            c += (Long) val.get(0);
          }
          count.set(0, c);
          context.write(key, count);
        }
      }
    
      /**
       * A reducer class that just emits the sum of the input values.
       **/
      public static class SumReducer extends ReducerBase {
        private Record result = null;
    
        @Override
        public void setup(TaskContext context) throws IOException {
          result = context.createOutputRecord();
        }
    
        @Override
        public void reduce(Record key, Iterator<Record> values, TaskContext context)
            throws IOException {
          long count = 0;
          while (values.hasNext()) {
            Record val = values.next();
            count += (Long) val.get(0);
          }
          result.set(0, key.get(0));
          result.set(1, count);
          context.write(result);
        }
      }
    
      public static void main(String[] args) throws Exception {
        if (args.length != 2) {
          System.err.println("Usage: WordCount <in_table> <out_table>");
          System.exit(2);
        }
    
        JobConf job = new JobConf();
    
        job.setMapperClass(TokenizerMapper.class);
        job.setCombinerClass(SumCombiner.class);
        job.setReducerClass(SumReducer.class);
    
        job.setMapOutputKeySchema(SchemaUtils.fromString("word:string"));
        job.setMapOutputValueSchema(SchemaUtils.fromString("count:bigint"));
    
        InputUtils.addTable(TableInfo.builder().tableName(args[0]).build(), job);
        OutputUtils.addTable(TableInfo.builder().tableName(args[1]).build(), job);
    
        JobClient.runJob(job);
      }
    
    }

pom.xml文件代码如下

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
      xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
      <modelVersion>4.0.0</modelVersion>
    
      <groupId>alibook</groupId>
      <artifactId>odps</artifactId>
      <version>0.0.1</version>
      <packaging>jar</packaging>
    
      <name>odps</name>
      <url>http://maven.apache.org</url>
    
      <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
      </properties>
    
      <dependencies>
        <dependency>
          <groupId>junit</groupId>
          <artifactId>junit</artifactId>
          <version>3.8.1</version>
          <scope>test</scope>
        </dependency>
        
        <dependency>
          <groupId>com.aliyun.odps</groupId>
          <artifactId>odps-sdk-core</artifactId>
          <version>0.23.3-public</version>
        </dependency>
        
        <dependency>
          <groupId>com.aliyun.odps</groupId>
          <artifactId>odps-sdk-commons</artifactId>
          <version>0.23.3-public</version>
        </dependency>
      
        <dependency>
          <groupId>com.aliyun.odps</groupId>
          <artifactId>odps-sdk-mapred</artifactId>
          <version>0.23.3-public</version>
        </dependency>
      
      </dependencies>
    </project>

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[8b63c9c2aa9f426abfe3f9e4646930b9.png]: https://img-blog.csdnimg.cn/8b63c9c2aa9f426abfe3f9e4646930b9.png
[62a83f7f9af74dc2856b8930470c4fda.png]: https://img-blog.csdnimg.cn/62a83f7f9af74dc2856b8930470c4fda.png
[70f60e41c38c4436995524e1c0d2cce2.png]: https://img-blog.csdnimg.cn/70f60e41c38c4436995524e1c0d2cce2.png