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Hadoop2.6.2的Eclipse插件的使用

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本文链接：

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。

Hadoop2.6.2的Eclipse插件的使用

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

  1 /**  2  * Licensed to the Apache Software Foundation (ASF) under one  3  * or more contributor license agreements.  See the NOTICE file  4  * distributed with this work for additional information  5  * regarding copyright ownership.  The ASF licenses this file  6  * to you under the Apache License, Version 2.0 (the  7  * "License"); you may not use this file except in compliance  8  * with the License.  You may obtain a copy of the License at  9  * 10  *     http://www.apache.org/licenses/LICENSE-2.0 11  * 12  * Unless required by applicable law or agreed to in writing, software 13  * distributed under the License is distributed on an "AS IS" BASIS, 14  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 15  * See the License for the specific language governing permissions and 16  * limitations under the License. 17  */ 18 package org.apache.hadoop.examples; 19  20 import java.io.IOException; 21 import java.util.StringTokenizer; 22  23 import org.apache.hadoop.conf.Configuration; 24 import org.apache.hadoop.fs.Path; 25 import org.apache.hadoop.io.IntWritable; 26 import org.apache.hadoop.io.Text; 27 import org.apache.hadoop.mapreduce.Job; 28 import org.apache.hadoop.mapreduce.Mapper; 29 import org.apache.hadoop.mapreduce.Reducer; 30 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; 31 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; 32 import org.apache.hadoop.util.GenericOptionsParser; 33  34 public class WordCount { 35  36   public static class TokenizerMapper  37        extends Mapper<Object, Text, Text, IntWritable>{ 38      39     private final static IntWritable one = new IntWritable(1); 40     private Text word = new Text(); 41        42     public void map(Object key, Text value, Context context 43                     ) throws IOException, InterruptedException { 44       StringTokenizer itr = new StringTokenizer(value.toString()); 45       while (itr.hasMoreTokens()) { 46         word.set(itr.nextToken()); 47         context.write(word, one); 48       } 49     } 50   } 51    52   public static class IntSumReducer  53        extends Reducer<Text,IntWritable,Text,IntWritable> { 54     private IntWritable result = new IntWritable(); 55  56     public void reduce(Text key, Iterable<IntWritable> values,  57                        Context context 58                        ) throws IOException, InterruptedException { 59       int sum = 0; 60       for (IntWritable val : values) { 61         sum += val.get(); 62       } 63       result.set(sum); 64       context.write(key, result); 65     } 66   } 67  68   public static void main(String[] args) throws Exception { 69       System.setProperty("HADOOP_USER_NAME", "hadoop"); 70     Configuration conf = new Configuration(); 71     conf.set("mapreduce.framework.name", "yarn"); 72     conf.set("yarn.resourcemanager.address", "192.168.0.1:8032"); 73     conf.set("mapreduce.app-submission.cross-platform", "true"); 74     String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); 75     if (otherArgs.length < 2) { 76       System.err.println("Usage: wordcount <in> [<in>...] <out>"); 77       System.exit(2); 78     } 79     Job job = new Job(conf, "word count1"); 80     job.setJarByClass(WordCount.class); 81     job.setMapperClass(TokenizerMapper.class); 82     job.setCombinerClass(IntSumReducer.class); 83     job.setReducerClass(IntSumReducer.class); 84     job.setOutputKeyClass(Text.class); 85     job.setOutputValueClass(IntWritable.class); 86     for (int i = 0; i < otherArgs.length - 1; ++i) { 87       FileInputFormat.addInputPath(job, new Path(otherArgs[i])); 88     } 89     FileOutputFormat.setOutputPath(job, 90       new Path(otherArgs[otherArgs.length - 1])); 91     System.exit(job.waitForCompletion(true) ? 0 : 1); 92   } 93 }

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。