Hadoop2.6.2的Eclipse插件的使用

歡迎轉載，且請注明出處，在文章頁面明顯位置給出原文連接。

本文鏈接：http://www.cnblogs.com/zdfjf/p/5178197.html

首先給出eclipse插件的下載地址：http://download.csdn.net/download/zdfjf/9421244

• 1.插件的安裝

插件下載后，放在eclipse安裝目錄下的plugins文件夾下，然后重啟eclipse，就會發現Project Explorer窗口里多出DFS Locations這一項，對應的是HDFS里存放的文件，現在里邊還沒有顯示目錄結構，不用着急，第二步配置之后，目錄結構就會出現了。

 

我突然想起來博客園上有一篇文章對這部分介紹的很好，而且我感覺對這一部分，我不會寫的比他好。所以我就不浪費時間了，直接參考蝦皮工作室的，原文鏈接http://www.cnblogs.com/xia520pi/archive/2012/05/20/2510723.html，可以對這一部分配置完成，下面我們要說的是配置完成后，有一些問題導致運行程序不能成功。通過不斷調試，我把我運行成功的代碼和相應的配置貼出來。

• 2.代碼

/**
 * Licensed to the Apache Software Foundation (ASF) under one
 * 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
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
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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"); 70     Configuration conf = new Configuration();
    conf.set("mapreduce.framework.name", "yarn"); 72     conf.set("yarn.resourcemanager.address", "192.168.0.1:8032");     conf.set("mapreduce.app-submission.cross-platform", "true"); 74     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);
  }
}

 這里第69行，因為我windows上用戶名為frank,集群上用戶名是hadoop ,所以這里增加配置文件，把HADOOP_USER_NAME設置為hadoop。第71和72行是因為配置文件沒有起作用，如果不加這兩行，會以本地方式運行，沒有提交到集群上運行。第73行因為是跨平台的，windows->linux,所以加上這一句。

然后，最重要的一步來了，注意，注意，注意，重要的事說3遍。

插件本來會自動把項目打成jar包，上傳運行。但是有問題，現在不會自動打包。所以，我們要把project打成jar包，然后build path ，配置為項目的外部依賴包，然后右鍵run as -> run on hadoop.就能運行成功了。

ps:這是我的一種方法，在配置的過程中，遇到的問題多種多樣，造成問題的原因也不盡相同。So，多搜索，多思考，解決問題。