本文主要介绍本地Eclipse开发连接远程阿里云Hadoop的环境搭建
首先需要在远程服务器部署好Hadoop运行环境
可以参考Hadoop伪分布式环境搭建
远程Hadoop环境创建好以后,接下来开始本地Eclipse环境的搭建

一,Eclipse下载和Hadoop插件下载

1,Eclipse官网下载Eclipse

2,接下来解决对应版本的Hadoop插件
示例中Hadoop的环境是2.7.3,这里也需要网上下载hadoop-eclipse-plugin-2.7.3.jar

3,将下载好的插件hadoop-eclipse-plugin-2.7.3.jar放在eclipse/dropins中,然后重启Eclipse

二,插件配置

1,Eclipse重启后将会出现红圈所示的部分,这说明插件加

9B9E2508-5B89-4C3F-AEAF-7ACE3E42AC45.png

2,
选择File->New->Project->Map/Reduce Project
创建一个WordCount工程

3,打开Eclipse的Preferences界面
选择Hadoop Map/Reduce选项
把Hadoop的安装目录选择进去.
这里可能会有一个疑问, Hadoop是在远程阿里云上安装的,这个目录怎么选择?
其实是把远程hadoop的运行程序在本地copy一份,然后解压,选择的是本地的

5FD251A4-D3B3-46F5-8A9B-BE6D3A42A504.png

4,设置Hadoop Tool
点击Window-->Show View -->MapReduce Tools 点击 Map/ReduceLocation
弹出如下界面,然后进

A4DC31C6-B60F-4A0F-95FB-71FB03A0B238.png

设置成功后,会出现如下界面

CB2244E9-A6F6-430B-A3EA-4AEFB1FB3F0D.png

5,设置阿里云
1,修改 hadoop/etc/hadoop/hdfs-site.xml

<configuration>
        <property>
                <name>dfs.replication</name>
                <value>1</value>
        </property>

        <property>
                <name>dfs.permissions</name>
                <value>false</value>
        </property>
</configuration>

2,修改hadoop/etc/hadoop/core-site.xml

<configuration>
        <property>
                <name>fs.defaultFS</name>
                <value>hdfs://120.27.4.193:9000</value>
        </property>
</configuration>

三,创建Demo测试

1,新建WordCount工程,添加WordCount.java类

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);
  }
}

2,在工程下面添加一个输入文件,就是程序的输入数据Input

EFB33C8A-080B-424D-9F4A-985692609A2F.png

3,选中WordCount.java右键->Run As -> Run as configure
执行输入文件名字就是刚才的Input文件,和输出目录名字Out(自动生成),然后点击右下角的run运行

8ECF399A-BA49-4FDE-A44D-0129406FD7CE.png

4,运行完毕后,选中WordCount工程,然后Refresh
运行结果目录就出来了,里面有结果文件

9D156AD1-4454-4B6F-8FCF-68137310FA90.png

至此,本地Eclipse开发连接远程阿里云Hadoop结束