Hadoop2.6.2的Eclipse插件的使用

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本文链接：http://www.cnblogs.com/zdfjf/p/5178197.html

首先给出eclipse插件的下载地址：http://download.csdn.net/download/zdfjf/9421244

• 1.插件的安装

插件下载后，放在eclipse安装目录下的plugins文件夹下，然后重启eclipse，就会发现Project Explorer窗口里多出DFS Locations这一项，对应的是HDFS里存放的文件，现在里边还没有显示目录结构，不用着急，第二步配置之后，目录结构就会出现了。

我突然想起来博客园上有一篇文章对这部分介绍的很好，而且我感觉对这一部分，我不会写的比他好。所以我就不浪费时间了，直接参考虾皮工作室的，原文链接http://www.cnblogs.com/xia520pi/archive/2012/05/20/2510723.html，可以对这一部分配置完成，下面我们要说的是配置完成后，有一些问题导致运行程序不能成功。通过不断调试，我把我运行成功的代码和相应的配置贴出来。

• 2.代码

 /**
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  * or more contributor license agreements.  See the NOTICE file
  * distributed with this work for additional information
  * regarding copyright ownership.  The ASF licenses this file
  * to you under the Apache License, Version 2.0 (the
  * "License"); you may not use this file except in compliance
  * with the License.  You may obtain a copy of the License at
  *
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  * distributed under the License is distributed on an "AS IS" BASIS,
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 package org.apache.hadoop.examples;

 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 {
       System.setProperty("HADOOP_USER_NAME", "hadoop");
     Configuration conf = new Configuration();
     conf.set("mapreduce.framework.name", "yarn");
     conf.set("yarn.resourcemanager.address", "192.168.0.1:8032");
     conf.set("mapreduce.app-submission.cross-platform", "true");
     String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
     if (otherArgs.length < 2) {
       System.err.println("Usage: wordcount <in> [<in>...] <out>");
       System.exit(2);
     }
     Job job = new Job(conf, "word count1");
     job.setJarByClass(WordCount.class);
     job.setMapperClass(TokenizerMapper.class);
     job.setCombinerClass(IntSumReducer.class);
     job.setReducerClass(IntSumReducer.class);
     job.setOutputKeyClass(Text.class);
     job.setOutputValueClass(IntWritable.class);
     for (int i = 0; i < otherArgs.length - 1; ++i) {
       FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
     }
     FileOutputFormat.setOutputPath(job,
       new Path(otherArgs[otherArgs.length - 1]));
     System.exit(job.waitForCompletion(true) ? 0 : 1);
   }
 }

这里第69行，因为我windows上用户名为frank,集群上用户名是hadoop ,所以这里增加配置文件，把HADOOP_USER_NAME设置为hadoop。第71和72行是因为配置文件没有起作用，如果不加这两行，会以本地方式运行，没有提交到集群上运行。第73行因为是跨平台的，windows->linux,所以加上这一句。

然后，最重要的一步来了，注意，注意，注意，重要的事说3遍。

插件本来会自动把项目打成jar包，上传运行。但是有问题，现在不会自动打包。所以，我们要把project打成jar包，然后build path ，配置为项目的外部依赖包，然后右键run as -> run on hadoop.就能运行成功了。

ps:这是我的一种方法，在配置的过程中，遇到的问题多种多样，造成问题的原因也不尽相同。So，多搜索，多思考，解决问题。