windows编译hadoop 2.x Hadoop-eclipse-plugin插件

た入场券 2022-08-06 05:03 142阅读 0赞

# 一．简介 #

Hadoop2.x之后没有Eclipse插件工具，我们就不能在Eclipse上调试代码，我们要把写好的java代码的MapReduce打包成jar然后在Linux上运行，所以这种不方便我们调试代码，所以我们自己编译一个Eclipse插件，方便我们在我们本地上调试，经过hadoop1.x的发展，编译hadoop2.x版本的eclipse插件比之前简单多了。接下来我 们开始编译Hadoop-eclipse-plugin插件，并在Eclipse开发Hadoop。

# 二．软件安装并配置 #

##  1.JDK配置 ##

1) 安装jdk

2) 配置环境变量

JAVA\_HOME、CLASSPATH、PATH等设置，这里就不多介绍，网上很多资料

##  2.Eclipse ##

1).下载eclipse-jee-juno-SR2.rar

2).解压到本地磁盘，如图所示：

![Center][]

## 3.Ant ##

1）下载

[http://ant.apache.org/bindownload.cgi][http_ant.apache.org_bindownload.cgi]

apache-ant-1.9.4-bin.zip

2）解压到一个盘，如图所示：

![Center 1][]

3）.环境变量的配置

新建ANT\_HOME=E:\\ant\\apache-ant-1.9.4-bin\\apache-ant-1.9.4

在PATH后面加;%ANT\_HOME%\\bin

4）cmd 测试一下是否配置正确

ant version   如图所示：

![Center 2][]

## 4.Hadoop ##

1).下载hadoop包

hadoop-2.6.0.tar.gz

解压到本地磁盘，如图所示：

![Center 3][]

**下载hadoop2x-eclipse-plugin源代码**

1）目前hadoop2的eclipse-plugins源代码由github脱管，下载地址是[https://github.com/winghc/hadoop2x-eclipse-plugin][https_github.com_winghc_hadoop2x-eclipse-plugin]，然后在右侧的Download ZIP连接点击下载，如图所示：

![Center 4][]

2）下载hadoop2x-eclipse-plugin-master.zip

解压到本地磁盘，如图所示：

![Center 5][]

# 三．编译hadoop-eclipse-plugin插件 #

##  1.hadoop2x-eclipse-plugin-master解压在E：盘打开命令行cmd，切换到E:\\hadoop\\hadoop2x-eclipse-plugin-master\\src\\contrib\\eclipse-plugin 目录，如图所示： ##

![Center 6][]

## 2.执行ant jar ##

antjar -Dversion=2.6.0 -Declipse.home=F:\\tool\\eclipse-jee-juno-SR2\\eclipse-jee-juno-SR2 -Dhadoop.home=E:\\hadoop\\hadoop-2.6.0\\hadoop-2.6.0，如图所示：

![Center 7][]

![Center 8][]

##  3.编译成功生成的hadoop-eclipse-plugin-2.6.0.jar在E:\\hadoop\\hadoop2x-eclipse-plugin-master\\build\\contrib\\eclipse-plugin路径下，如图所示： ##

![Center 9][]

# 四．Eclipse配置hadoop-eclipse-plugin 插件 #

##  1.把hadoop-eclipse-plugin-2.6.0.jar拷贝到F:\\tool\\eclipse-jee-juno-SR2\\eclipse-jee-juno-SR2\\plugins目录下，重启一下Eclipse，然后可以看到DFS Locations,如图所示： ##

![Center 10][]

##  2.打开Window-->Preferens，可以看到Hadoop Map/Reduc选项，然后点击，然后添加hadoop-2.6.0进来，如图所示： ##

![Center 11][]

## 3.配置Map/ReduceLocations ##

1）点击Window-->Show View -->MapReduce Tools  点击Map/ReduceLocation

2）点击Map/ReduceLocation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hdfs-site.xml与core-site.xml的设置一致即可。

![Center 12][]

## 4.查看是否连接成功 ##

![Center 13][]

# 五.运行新建WordCount 项目并运行 #

1.右击New->Map/Reduce Project

2.新建WordCount.java

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;
    
    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();
        Job job = Job.getInstance(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(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
      }
    }

3.在hdfs输入目录创建需要统计的文本

1）没有输入输出目录卡，先在hdfs上建个文件夹

\#bin/hdfs dfs -mkdir –p  /user/root/input

\#bin/hdfs dfs -mkdir -p  /user/root/output

2）.把要统计的文本上传到hdfs的输入目录下

\# bin/hdfs dfs -put/usr/local/hadoop/hadoop-2.6.0/test/\* /user/root/input      //把tes/file01文件上传到hdfs的/user/root/input中

3）.查看

\#bin/hdfs dfs -cat /user/root/input/file01

![Center 14][]

4.点击WordCount.java右击-->Run As-->Run COnfigurations   设置输入和输出目录路径，如图所示：

![Center 15][]

5.点击WordCount.java右击-->Run As-->Run on  Hadoop

![Center 16][]

然后到output/count目录下，有一个统计文件，并查看结果，所以配置成功。

# 五.注意的地方 #

我们在这篇介了，Eclipse连接Linux虚拟机上Hadoop并在Eclipse开发Hadoop的一些问题，[解决Exception: org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Ljava/lang/String;I)Z 等一系列问题][Exception_ org.apache.hadoop.io.nativeio.NativeIO_Windows.access0_Ljava_lang_String_I_Z]

[Center]: /images/20220806/6da489714dda4ea1a2640435bd516b3a.png
[http_ant.apache.org_bindownload.cgi]: http://ant.apache.org/bindownload.cgi
[Center 1]: /images/20220806/8e4f069bb7c0408096a564fc48bab415.png
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[Center 3]: /images/20220806/c46d5347bfc147a6b551e7a48fea6550.png
[https_github.com_winghc_hadoop2x-eclipse-plugin]: https://github.com/winghc/hadoop2x-eclipse-plugin
[Center 4]: /images/20220806/f4cc39edff8047f5be979d21a443d103.png
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[Center 16]: /images/20220806/a46564f2b2b4475c8689e9f03de4c80c.png
[Exception_ org.apache.hadoop.io.nativeio.NativeIO_Windows.access0_Ljava_lang_String_I_Z]: http://blog.csdn.net/congcong68/article/details/42043093