Eclipse下搭建Hadoop2.4.0开发环境

一、安装Eclipse

    下载Eclipse,解压安装,例如安装到/usr/local,即/usr/local/eclipse

    4.3.1版本下载地址:http://pan.baidu.com/s/1eQkpRgu

二、在eclipse上安装hadoop插件

    1、下载hadoop插件

        下载地址:http://pan.baidu.com/s/1mgiHFok

     此zip文件包含了源码,我们使用使用编译好的jar即可,解压后,release文件夹中的hadoop.eclipse-kepler-plugin-2.2.0.jar就是编译好的插件。

 

   2、把插件放到eclipse/plugins目录下

 

    3、重启eclipse,配置Hadoop installation directory    

     如果插件安装成功,打开Windows—Preferences后,在窗口左侧会有Hadoop Map/Reduce选项,点击此选项,在窗口右侧设置Hadoop安装路径。

      

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

4、配置Map/Reduce Locations

     打开Windows—Open Perspective—Other

 

 

 

 

 

 

 

 

 

 

 

 

 

    

    选择Map/Reduce,点击OK

    

    在右下方看到如下图所示

    

 

点击Map/Reduce Location选项卡,点击右边小象图标,打开Hadoop Location配置窗口:

    输入Location Name,任意名称即可.配置Map/Reduce Master和DFS Mastrer,Host和Port配置成与core-site.xml的设置一致即可。

    

 

 

 

 

 

 

 

 

 

 

 

 

 

 

点击"Finish"按钮,关闭窗口。

 点击左侧的DFSLocations—>myhadoop(上一步配置的location name),如能看到user,表示安装成功

   

      

      

 

 

 

 

 

 

 

 

 

    如果如下图所示表示安装失败,请检查Hadoop是否启动,以及eclipse配置是否正确。

 

 

 

 

 

 

 

 

 

 

 

 

 

 

三、新建WordCount项目

    File—>Project,选择Map/Reduce Project,输入项目名称WordCount等。

    在WordCount项目里新建class,名称为WordCount,代码如下:

    

import java.io.IOException;

import java.util.StringTokenizer;

 

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.Mapper;

import org.apache.hadoop.mapreduce.Reducer;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import org.apache.hadoop.util.GenericOptionsParser;

 

public class WordCount {

 

public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{ 

  private final static IntWritable one = new IntWritable(1);

  private Text word = new Text();

 

  public void map(Object key, Text value, Context context) throws IOException, InterruptedException {

    StringTokenizer itr = new StringTokenizer(value.toString());

      while (itr.hasMoreTokens()) {

        word.set(itr.nextToken());

        context.write(word, one);

      }

  }

}

 

public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> {

  private IntWritable result = new IntWritable(); 

  public void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {

    int sum = 0;

    for (IntWritable val : values) {

      sum += val.get();

    }

    result.set(sum);

    context.write(key, result);

  }

}

 

public static void main(String[] args) throws Exception {

  Configuration conf = new Configuration();

  String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();

  if (otherArgs.length != 2) {

    System.err.println("Usage: wordcount <in> <out>");

    System.exit(2);

  }

  Job job = new Job(conf, "word count");

  job.setJarByClass(WordCount.class);

  job.setMapperClass(TokenizerMapper.class);

  job.setCombinerClass(IntSumReducer.class);

  job.setReducerClass(IntSumReducer.class);

  job.setOutputKeyClass(Text.class);

  job.setOutputValueClass(IntWritable.class);

  FileInputFormat.addInputPath(job, new Path(otherArgs[0]));

  FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));

  System.exit(job.waitForCompletion(true) ? 0 : 1);

}

}

 

 

四、运行

    1、在HDFS上创建目录input

        hadoop fs -mkdir input

    2、拷贝本地README.txt到HDFS的input里

         hadoop fs -copyFromLocal /usr/local/hadoop/README.txt input

    3、点击WordCount.java,右键,点击Run As—>Run Configurations,配置运行参数,即输入和输出文件夹

  hdfs://localhost:9000/user/hadoop/input hdfs://localhost:9000/user/hadoop/output

 

    

 

    

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  点击Run按钮,运行程序。

 

    4、运行完成后,查看运行结果        

        方法1:

 

        hadoop fs -ls output

        可以看到有两个输出结果,_SUCCESS和part-r-00000

        执行hadoop fs -cat output/*

        

        

        方法2:

        展开DFS Locations,如下图所示,双击打开part-r00000查看结果

    

          

        

 

    

posted @ 2014-06-22 16:39 kinglau 阅读(...) 评论(...) 编辑 收藏