【Hadoop】MapReduce案例——词频统计

• 一、前期准备
• 二、WordCount案例
• 三、射雕英雄传词频案例

1.新建maven项目
2.pom.xml中添加项目依赖

  4.0.0

  com.hdtrain
  wordcount
  1.0-SNAPSHOT

  wordcount
  
  http://www.example.com

  
    UTF-8
    
    1.8
    1.8
  

  
      
          junit
          junit
          4.11
          test
      
      
      
          log4j
          log4j
          1.2.17
      
      
      
          org.apache.hadoop
          hadoop-hdfs
          2.7.1
      
      
      
          org.apache.hadoop
          hadoop-common
          2.7.1
      
      
      
          org.apache.hadoop
          hadoop-client
          2.7.1
      
      
      
          org.apache.hadoop
          hadoop-mapreduce-client-core
          2.7.1
      
      
      
          com.janeluo
          ikanalyzer
          2012_u6
      
  

  
    
      
        
        
          maven-clean-plugin
          3.1.0
        
        
        
          maven-resources-plugin
          3.0.2
        
        
          maven-compiler-plugin
          3.8.0
        
        
          maven-surefire-plugin
          2.22.1
        
        
          maven-jar-plugin
          3.0.2
        
        
          maven-install-plugin
          2.5.2
        
        
          maven-deploy-plugin
          2.8.2
        
        
        
          maven-site-plugin
          3.7.1
        
        
          maven-project-info-reports-plugin
          3.0.0
        
      
    
  

3.项目中添加resource文件夹
添加配置文件：core-site.xml、hdfs-site.xml、mapred-site.xml
修改文件夹文件结构为resource

采用”哈利波特“英文版作为数据
1.WordCountJob.class

package com.hdtrain;

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;

import java.io.IOException;

// 定义wordcount任务
public class WordCountJob {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
		//System.setProperty("HADOOP_USER_NAME", "root");
        //读取配置文件
        Configuration configuration = new Configuration(true);
        configuration.set("mapreduce.framework.name", "local");
        //创建job
        Job job = Job.getInstance(configuration);
        //设置Job的参数
        job.setJobName("wordcount-" + System.currentTimeMillis()); //设置job名
        job.setJarByClass(WordCountJob.class); //设置当前job主类
        job.setNumReduceTasks(2);

        //设置要处理文件的路径
        FileInputFormat.setInputPaths(job, "/data/harry.txt");
        //设置输出结果路径
        FileOutputFormat.setOutputPath(job, new Path("/results/wordcount-"+System.currentTimeMillis()));

        //设置map要输出的数据类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        //设置map类
        job.setMapperClass(WordCountMapper.class);
        //设置reduce类
        job.setReducerClass(WordCountReduce.class);

        //提交任务
        job.waitForCompletion(true);
    }
}

2.WordCountMapper.class

package com.hdtrain;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class WordCountMapper extends Mapper {

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //获取单词
        String[] words = value.toString().replaceAll("[^a-zA-Z0-9\s']", "").split(" ");
        开始向context添加数据,写出到reduce，统计单词数量
        for (int i = 0; i < words.length; i++){
            context.write(new Text(words[i]), new IntWritable(1));
        }
    }
}

3.WordCountReducer.class

package com.hdtrain;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;
import java.util.Iterator;

public class WordCountReducer extends Reducer {
    @Override
    protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
        //声明一个变量存放总数
        long count = 0;
        //获取迭代器
        Iterator iterator = values.iterator();
        //开始遍历迭代器
        while (iterator.hasNext()){
            int value = iterator.next().get();
            count += value;
        }
        //继续写出
        context.write(key, new LongWritable(count));
    }
}

4.计算结果

采用”哈利波特“英文版作为数据
1.SdxyzJob.class

package com.hdtrain;


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;
import sun.net.sdp.SdpSupport;

import java.io.IOException;

public class SdyxzJob {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        //1.读取配置文件
        Configuration configuration = new Configuration(true);
        configuration.set("mapreduce.framework.name", "local");

        //2.创建job
        Job job = Job.getInstance(configuration);

        //3.设置job的参数
        job.setJobName("射雕英雄传-"+System.currentTimeMillis());
        job.setJarByClass(SdyxzJob.class);
        job.setNumReduceTasks(2);

        //4.设置要处理数据文件的路径
        FileInputFormat.setInputPaths(job, new Path("/data/sdyxz.txt"));

        //5.设置输出结果路径
        FileOutputFormat.setOutputPath(job, new Path("/results/sdyxz-"+System.currentTimeMillis()));

        //6.设置map要输出的数据类型
        job.setOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        //7.设置map类
        job.setMapperClass(SdyxzMapper.class);

        //8.设置reduce类
        job.setReducerClass(SdyxzReducer.class);

        //9.提交job
        job.waitForCompletion(true);
    }
}

2.SdxyzMapper.class

package com.hdtrain;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.wltea.analyzer.core.IKSegmenter;
import org.wltea.analyzer.core.Lexeme;

import java.io.IOException;
import java.io.StringReader;

public class SdyxzMapper extends Mapper {

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        StringReader stringReader = new StringReader(value.toString());
        IKSegmenter ikSegmenter = new IKSegmenter(stringReader, true);
        Lexeme lexeme = null;
        while((lexeme = ikSegmenter.next()) != null){
            context.write(new Text(lexeme.getLexemeText()), new IntWritable(1));
        }
    }
}

3.SdxyzReducer.class

package com.hdtrain;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;
import java.util.Iterator;

public class SdyxzReducer extends Reducer {

    @Override
    protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
        long count = 0;
        Iterator iterator = values.iterator();
        while(iterator.hasNext()){
            int value = iterator.next().get();
            count += value;
        }
        context.write(key, new LongWritable(count));
    }
}

4.IK分词器示例

package com.hdtrain;

import org.wltea.analyzer.core.IKSegmenter;
import org.wltea.analyzer.core.Lexeme;

import java.io.IOException;
import java.io.StringReader;

public class IKword {
    public static void main(String[] args) throws IOException {

        StringReader stringReader = new StringReader("畔一排数十株乌柏树，叶子似火烧般红，正是八月天时。村前村后的野草刚起始变黄，一抹斜阳映照之下，更增了几分萧索。两株大松树下围着一堆村民，男男女女和十几个小孩，正自聚精会神的听着一个瘦削的老者说话。");
        IKSegmenter ikSegmenter = new IKSegmenter(stringReader, true);
        Lexeme lexeme = null;
        while((lexeme = ikSegmenter.next()) != null){
            System.out.println(lexeme.getLexemeText());
        }
    }
}

5.计算结果