写一个 Mapreduce 小程序玩玩？

最近搭好了 Hadoop 的环境，赶快整一个小程序试验一下（过两天再写怎么搭的环境吧）。
想法很简单就是想做一个单词种类的统计，首先是 Map 部分：(开始使用 Maven ，真的是神器，几个代码 jar 包就配好了)
我是用的是免费版的 idea，可以使用 Maven 功能，毕竟能不用盗版就不用盗版软件，不管是使用 idea 还是 eclipse 都可以新建一个 Marven Project。

然后配置 pom.xml，可以登陆 http://mvnrepository.com/ 查找 hadoop-common、hadoop-client、hadoop-mapreduce-client-jobclient 的对应你 Hadoop 版本的代码加入到文件中就好，我的是 2.7.3

然后在 pom.xml 中添加：

    <dependencies>
        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-common -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>2.7.3</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-client -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.7.3</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-mapreduce-client-jobclient -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-jobclient</artifactId>
            <version>2.7.3</version>
            <scope>provided</scope>
        </dependency>

    </dependencies>

然后等待包加载完成就可以开心的写代码啦。因为会自动导入相关依赖，所以引入的包还是很多的，写的时候需要注意不要导错包。
首先是 Map 部分：

package test2;


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 MyMap1 extends Mapper<LongWritable, Text, Text, IntWritable> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String line = value.toString();
        String [] words = line.split(" ");
        for(String word:words){
            context.write(new Text(word), new IntWritable(1));
        }
    }
}

然后是 Reduce：

package test;

import java.io.IOException;

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

public class MyReduce extends Reducer<Text, IntWritable, Text, IntWritable>{
    @Override
    protected void reduce(Text text, Iterable<IntWritable> values,
            Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
        int count = 0;
        for(IntWritable value: values) {
            count+=value.get();
        }
        context.write(text,new IntWritable(count));
    }

}

接下来是工作类：

package test;

import javax.servlet.ServletOutputStream;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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 org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class MyJob extends Configured implements Tool{
    public static void main(String[] args) {
        try {
            ToolRunner.run(new MyJob(), null);
            System.out.println("运行结束！");
        } catch (Exception e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
    }
    @Override
    public int run(String[] args) throws Exception {
        // TODO Auto-generated method stub
        Configuration configuration = new Configuration();
        configuration.set("fs.defaultFS", "hdfs://192.168.80.131:9000");
        Job job = Job.getInstance(configuration);
        job.setJarByClass(MyJob.class);
        job.setMapperClass(MyMap1.class);
        job.setReducerClass(MyReduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(job, new Path("/abc/MapReduceTest1.txt"));
        FileOutputFormat.setOutputPath(job, new Path("/abc/out1"));
        job.waitForCompletion(true);
        
        return 0;
    }
    

}

最后运行，多了一个 out 目录，运行结果在里面，