MapReduce中词频统计简单实现

输入数据

test1
test2 test2
test3 test3 test3
test5 test5 test4
test5 test5 test4 test5
test4 test4

Maven必须配置

注意：Windos本地运行需要确定本地有Hadoop依赖并确保和Pom配置文件中版本一致，WordCountDriver中第6点输入输出需要自行修改

    4.0.0

    com.test2
    mapredceDemo1
    1.0-SNAPSHOT

    
        
        
            org.apache.hadoop
            hadoop-client
            3.0.1
        
        
        
            junit
            junit
            4.12
        
        
        
            org.slf4j
            slf4j-log4j12
            1.7.30
        
    

    
        
            
            
                maven-compiler-plugin
                3.6.1
                
                    1.8
                    1.8
                
            
            
            
                maven-assembly-plugin
                
                    
                        jar-with-dependencies
                    
                
                
                    
                        make-assembly
                        package
                        
                            single
                        
                    
                
            
        
    

resources目录下log4j.properties 配置

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

自定义Mapper类实现(WordCountMapper)

package com.atguigu.mapreduce.wordcount;

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 {

    // 定义一个Text对象，封装数据
    private Text k = new Text();
    // 定义一个IntWritable对象，直接封装1为值
    private IntWritable v = new IntWritable(1);

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        // 1. 读取每一行
        String line = value.toString();
        // 2. 字符串切割
        String[] words = line.split(" ");
        // 3. 循环设置并输出
        for (String word : words) {
            k.set(word);
            context.write(k, v);
        }
    }
}

 自定义Reducer类实现(WordCountReducer)

package com.test.mapreduce.wordcount;

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

import java.io.IOException;

public class WordCountReducer extends Reducer {

    // 定义变量统计词频
    int sum;
    // 定义一个IntWritable对象，以便封装数据
    IntWritable v = new IntWritable();

    @Override
    protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
        // 1. 初始化词频
        sum = 0;
        // 2. 统计词频
        for (IntWritable count : values) sum += count.get();
        // 3. 封装value
        v.set(sum);
        // 4. 输出
        context.write(key, v);
    }
}

自定义Driver类实现(WordCountDriver)

package com.atguigu.mapreduce.wordcount;

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;

public class WordCountDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        // 1.创建配置信息Configuration对象并获取Job单例对象
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

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

        // 3.设置关联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. 设置最终输出的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);
    }
}

Linux集群环境下运行命令（代码打包后改名为wc.jar）：

hadoop jar wc.jar com.atguigu.mapreduce.wordcount.WordCountDriver /input /output

输出数据

test1 1
test2 2
test3 3
test4 4
test5 5