MapReduce实现wordcount案例

MapReduce实现wordcount案例

1、创建maven工程

导入hadoop所需要的依赖包

	
    
        3.1.4
    

    
    
        
            org.apache.hadoop
            hadoop-client
            ${hadoop.version}
        
        
            org.apache.hadoop
            hadoop-common
            ${hadoop.version}
        

        
            org.apache.hadoop
            hadoop-hdfs
            ${hadoop.version}
        

        
            org.apache.hadoop
            hadoop-mapreduce-client-core
            ${hadoop.version}
        
        
        
            junit
            junit
            4.11
            test
        
        
            org.testng
            testng
            RELEASE
        
        
        
            log4j
            log4j
            1.2.17
        
    
    
        
            
                org.apache.maven.plugins
                maven-compiler-plugin
                3.0
                
                    1.8
                    1.8
                    UTF-8
                    
                
            
            
                org.apache.maven.plugins
                maven-shade-plugin
                2.4.3
                
                    
                        package
                        
                            shade
                        
                        
                            true
                        
                    
                
            
        
    

2、创建自定义的Mapper逻辑

package 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 WordMapper extends Mapper {

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //这个value指的是一行文本数据
        String s = value.toString();
        //把一行文本数据，按照“，”的方式进行切割
        String[] split = s.split(",");
        for (String word:split) {
            //每个数据，一个单词，对应的value值是1
            context.write(new Text(word),new IntWritable(1));
        }
    }
}

3、自定义Mapper类

package 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 WordReduce extends Reducer {

    @Override
    protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable value:values) {
            sum = sum + value.get();
        }
        context.write(key,new IntWritable(sum));
    }
}

4、自定义测试类

package 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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.log4j.BasicConfigurator;

import java.io.IOException;

public class Test {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        //这一句话是为了能打印日志的，进行的配置
        BasicConfigurator.configure();

        //1、获取到我们的job对象，让它运行
        Job job = Job.getInstance(new Configuration(), "WordCount");

        //2、如果我们打包成jar文件，指定我们程序的入口类是哪一个
        job.setJarByClass(Test.class);

        //3、从存储系统中获取到什么样的文件，这里指的是Text这样的输入流文件
        job.setInputFormatClass(TextInputFormat.class);
        //4、指定输入流文件的位置
        TextInputFormat.addInputPath(job,new Path("E:\\hadoop\\mapreduce\\input"));

        //5、设置自定义的Mapper
        job.setMapperClass(WordMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        //6、设置自定义的Reduce
        job.setReducerClass(WordReduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        //7、设置Reduce个数
        job.setNumReduceTasks(1);

        //8、设置输出的文件以什么类型存储，这里是Text形式的输出流
        job.setOutputFormatClass(TextOutputFormat.class);
        //9、输出的文件夹的位置（文件中不能存在这个文件夹）
        TextOutputFormat.setOutputPath(job,new Path("E:\\hadoop\\mapreduce\\output\\text19"));

        //10、等待结果输出
        boolean b = job.waitForCompletion(true);

        //11、退出
        System.exit(b?0:1);


    }
}