Hadoop 用Eclipse来Mapreduce WordCount实战（1）

川长思鸟来 2023-10-17 11:21 9阅读 0赞

**（一）官网下载[http://www.eclipse.org/][http_www.eclipse.org]**

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**（二）maven [http://www.mvnrepository.com][http_www.mvnrepository.com]**

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**选择对应的hadoop版本**

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**拷贝对应的hadoop**

<dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-mapreduce-client-common</artifactId>
        <version>2.4.1</version>
    </dependency>
    
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-mapreduce-client-core</artifactId>
        <version>2.4.1</version>
    </dependency>
    
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-common</artifactId>
        <version>2.4.1</version>
    </dependency>
    
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-hdfs</artifactId>
        <version>2.4.1</version>
    </dependency>

**（三）解压下载Eclipse**

**(3.1) 右键工程|Build Path|Configure Build Path**

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**(3.2) 安装Hadoop插件**

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（a）关闭Eclipse； 将这个jar放入到D:\\eclipse-jee-mars-2-win32\\eclipse\\plugins目录下

（b）启动Eclipse；Window|Preferences| (注意：Browse...选择当前Hadoop的目录)

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（c）Window|Show View| Other

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（d）必须先启动linux中的Hadoop

\[hadoop@master-hadoop hadoop-2.4.1\]$ sbin/start-dfs.sh

\[hadoop@master-hadoop hadoop-2.4.1\]$ sbin/start-yarn.sh

（e）单击右下角![Center 13][]小象；设置 New Hadoop Location....

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（f）显示效果

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**(3.3) Hadoop中的bin中缺少编译文件**

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(a) 将winutils.ext文件复制到C:\\hadoop-2.4.1\\hadoop-2.4.1\\bin目录下

(b)将Hadoop.dll 文件复制到 C:\\Windows\\System32目录下

**（3.4）编写源代码**

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**WordCountMapper类**

package com.hlx.mapreduce.wc;
    
    import java.io.IOException;
    
    import org.apache.hadoop.io.IntWritable;
    import org.apache.hadoop.io.LongWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Mapper;
    
    /**
     * 继承这个mapper LongWritable ==>long Text ===>String IntWritable==>int
     * 
     * @author Administrator
     *
     */
    public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
    
    	/**
    	 * 重写这个方法
    	 */
    	@Override
    	protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
    			throws IOException, InterruptedException {
    		// TODO Auto-generated method stub
    		// super.map(key, value, context);
    		
    		// 1) 获得每一行的数据
    		// hello hadoop
    		String line = value.toString();
    
    		// 2)分割每一行的数据
    		//hello,hadoop
    		String[] splits = line.split(" ");
    		
    		//3)遍历每一行的数据
    		//hello 1
    		//hadoop 1
    		for(String str :splits){
    			 //context上下 文数据（key--value 每个单词输出1次）
    			context.write(new Text(str), new IntWritable(1));
    		}
    	}
    }

**WordCountReduce类**

package com.hlx.mapreduce.wc;
    
    import java.io.IOException;
    
    import org.apache.hadoop.io.IntWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Reducer;
    /**
     * 继承Reduce 
     * Text ==>String
     * IntWritable ==>int 
     * (输入(key,value)，输出(key,value))
     * @author Administrator
     *
     */
    public class WordCountReduce extends Reducer<Text, IntWritable, Text, IntWritable> {
    	// a 1
    	// b 1
    	// c 1
    	// hello{1,1,1}==> hello{3}  ===>其实就是values
        @Override
        protected void reduce(Text key, Iterable<IntWritable> values,
        		Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
           int count=0; //累计和
           //遍历数据
           for(IntWritable value :values){
        	   count +=value.get();
           }
           
           //写入到上下文
           context.write(key, new IntWritable(count));
        	
        }
    }

**WordCountMapReduce类**

package com.hlx.mapreduce.wc;
    
    
    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;
    
    /**
     * 测试类
     * 
     * @author Administrator
     *
     */
    public class WordCountMapReduce {
    
    	public static void main(String[] args) throws Exception {
    		// 创建配置对象
    		Configuration conf = new Configuration();
    		
    		// 创建job对象
    		Job job = Job.getInstance(conf, "wordcount0");
    		
    		//设置运行的主类
    		job.setJarByClass(WordCountMapReduce.class);
    		
    		//设置map类
    		job.setMapperClass(WordCountMapper.class);
    		
    		//设置reduce类
    		job.setReducerClass(WordCountReduce.class);
    		
    		//设置map(key,value)
            job.setMapOutputKeyClass(Text.class);
            job.setOutputValueClass(IntWritable.class);
            
        	//设置reduce(key,value)
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(IntWritable.class);
            
            //设置输入 输出路径 words=是输入文件夹中有个words文件； out3=是输出文件夹
            FileInputFormat.setInputPaths(job, new Path("hdfs://master-hadoop.dragon.org:9000/words"));
            FileOutputFormat.setOutputPath(job, new Path("hdfs://master-hadoop.dragon.org:9000/out3"));
            
            //提交job
            boolean flag= job.waitForCompletion(true);
            if(!flag){
            	System.out.println("the task has failed!");
            }
    	}
    }

（3.5）运行

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**效果如下：**

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**注意：其实源代码可以优化的！**

[http_www.eclipse.org]: http://www.eclipse.org/
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[http_www.mvnrepository.com]: http://www.mvnrepository.com/
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