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Ubuntu 14.10 下Eclipse安装Hadoop插件

准备环境

  1 安装好了Hadoop,之前安装了Hadoop 2.5.0,安装参考http://www.cnblogs.com/liuchangchun/p/4097286.html

  2 安装Eclipse,这个直接在其官网下载即可

安装步骤

  1 下载Eclipse插件,我找的是Hadoop 2.2 的插件,在Hadoop 2.5 下可以正常用,获取插件这里有两种方式

    1.1 一是自己下载源码自己编译,过程如下

    首先,下载eclipse-hadoop的插件,网址是https://github.com/winghc/hadoop2x-eclipse-plugin,你可以点击网页右下方的Download ZIP下载。下载之后,解压缩,。

    然后,进入到 hadoop2x-eclipse-plugin-master/src/contrib/eclipse-plugin文件夹里面,执行命令

    ant jar -Declipse.home=/usr/local/eclipse -Dhadoop.home=~/Downloads/hadoop-2.2.0 -Dversion=2.5.0

    编译顺利通过,生成的插件在hadoop2x-eclipse-plugin-master/build/contrib/eclipse-plugin目录下。

    1.2 或是直接下载编译好的插件,下载地址http://pan.baidu.com/s/1mgiHFok

  2 将下载好的插件复制到eclipse/plugins目录下,需要重启Eclipse

  3 配置Hadoop installation directory   

    3.1 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。

    3.2 配置Map/Reduce Locations打开Windows—Open Perspective—Other  选择Map/Reduce,点击OK

    3.3 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。如果没有自己修改端口,那么一个是9001,一个是9000

    3.4 点击左侧的DFSLocations—>Location Name(上一步配置的location name),如能看到Hadoop下的文件,那么表示安装成功。

  4 测试MapReduce。Eclipse中,File—>Project,选择Map/Reduce Project,输入项目名称WordCount等。然后新建一个类,代码拷贝下

 

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);    }}

 

  5 运行项目,先需要做些准备工作  

  5.1、在HDFS上创建目录input

        hadoop fs -mkdir input

  5.2 、随便拷贝本地README.txt到HDFS的input里

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

       5.3、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

 

  5.4 注意,输入目录output不要在Hadoop中建立,否则会报错

  6 查看结果,可以直接在DFS Locations刷新下就会看到多个目录,里面就有结果

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  WordCount程序上面是写在一个类里面,规范一点是Map类,Reduce类,MapRedcueDriver分开建立,低耦合

  1 新建Map/Reduce工程wordcount。

  2 新建Mapper.java,选择File——>New——>Mapper,输入包名及类名。

  3 新建Reduccer.java,选择File——>New——>Reducer,输入包名及类名。

  4 建立Map/Reduce Driver,选择File——>New——>MapReduce Driver,输入包名及类名。

  5 运行,同上面

  

 

 

  

 

Ubuntu 14.10 下Eclipse安装Hadoop插件