Eclipse环境搭建并且运行wordcount程序

时间:2024-03-02 12:44:03

 一、安装Hadoop插件

  1. 所需环境

      hadoop2.0伪分布式环境平台正常运行

    所需压缩包:eclipse-jee-luna-SR2-linux-gtk-x86_64.tar.gz
          在Linux环境下运行的eclipse软件压缩包,解压后文件名为eclipse
          hadoop2x-eclipse-plugin-master.zip
          在eclipse中需要安装的Hadoop插件,解压后文件名为hadoop2x-eclipse-plugin-master

    如图所示,将所有的压缩包放在同一个文件夹下并解压。

      

  2.编译jar包

      编译hadoop2x-eclipse-plugin-master的plugin 的插件源码,需要先安装ant工具

      

      接着输入命令(注意ant命令在什么路径下使用,具体路径在下一张截图中,不然这个命令会用不了):

ant jar -Dversion=2.6.0 -Declipse.home=\'/home/xiaow/hadoop2.0/eclipse\'  # 刚才放进去的eclipse软件包的路径 -Dversion=2.6.0 hadoop的版本号
              -Dhadoop.home=\'/home/xiaow/hadoop2.0/hadoop-2.6.0\'  # hadoop安装文件的路径

      

      等待一小会时间就好了
      编译成功后,找到放在 /home/xiaow/ hadoop2.0/hadoop2x-eclipse-pluginmaster/build/contrib/eclipse-plugin下, 名为hadoop-eclipse-plugin-2.6.0.jar的jar包, 并将其拷贝到/hadoop2.0/eclipse/plugins下

      输入命令

cp -r /home/xiaow/hadoop2.0/hadoop2x-eclipse-plugin-master/build/contrib/eclipse-plugin/hadoop-eclipse-plugin-2.6.0.jar /home/xiaow/hadoop2.0/eclipse/plugins/

      

      

      

二、Eclipse配置

    接下来打开eclipse软件

    

    一定要出现这个图标,没有出现的话前面步骤可能错了,或者重新启动几次Eclipse

    

    然后按照下面的截图操作:

    

    

    如此,Eclipse环境搭建完成。

 三、wordcount程序

     建工程:

    

    

    

    

    

    

    

    输入如下代码:

package wordcount;

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.LongWritable;  
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.input.TextInputFormat;  
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;  
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;  
import org.apache.hadoop.mapreduce.lib.reduce.IntSumReducer;
import org.apache.hadoop.util.GenericOptionsParser;
  
public class wordcount {  
	
	// 自定义的mapper,继承org.apache.hadoop.mapreduce.Mapper
    public static class WordCountMap extends Mapper<LongWritable, Text, Text, IntWritable> {  
  
        private final IntWritable one = new IntWritable(1);  
        private Text word = new Text();  
        
        //  Mapper<LongWritable, Text, Text, LongWritable>.Context context
        public void map(LongWritable key, Text value, Context context)   throws IOException, InterruptedException {  
            String line = value.toString();  
            System.out.println(line);
            // split 函数是用于按指定字符(串)或正则去分割某个字符串,结果以字符串数组形式返回,这里按照“\t”来分割text文件中字符,即一个制表符
            // ,这就是为什么我在文本中用了空格分割,导致最后的结果有很大的出入
            StringTokenizer token = new StringTokenizer(line);  
            while (token.hasMoreTokens()) {  
                word.set(token.nextToken());  
                context.write(word, one);  
            }  
        }  
    }  
  
    // 自定义的reducer,继承org.apache.hadoop.mapreduce.Reducer
    public static class WordCountReduce extends Reducer<Text, IntWritable, Text, IntWritable> {  
  
    	// Reducer<Text, LongWritable, Text, LongWritable>.Context context
        public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {  
        	System.out.println(key);
        	System.out.println(values);
            int sum = 0;  
            for (IntWritable val : values) {  
                sum += val.get();  
            }  
            context.write(key, new IntWritable(sum));  
        }  
    }  
  
    //  客户端代码,写完交给ResourceManager框架去执行
    public static void main(String[] args) throws Exception {  
        Configuration conf = new Configuration();  
        Job job = new Job(conf,"word count"); 
        
        //  打成jar执行
        job.setJarByClass(wordcount.class);     
        
        //  数据在哪里?
        FileInputFormat.addInputPath(job, new Path(args[0])); 
        
        //  使用哪个mapper处理输入的数据?
        job.setMapperClass(WordCountMap.class); 
        //  map输出的数据类型是什么?
        //job.setMapOutputKeyClass(Text.class);
        //job.setMapOutputValueClass(LongWritable.class);
        
        job.setCombinerClass(IntSumReducer.class);
        
        //  使用哪个reducer处理输入的数据
        job.setReducerClass(WordCountReduce.class); 
        
        //  reduce输出的数据类型是什么?
        job.setOutputKeyClass(Text.class);  
        job.setOutputValueClass(IntWritable.class);  
   
  
//        job.setInputFormatClass(TextInputFormat.class);  
//        job.setOutputFormatClass(TextOutputFormat.class);  
   
        //  数据输出到哪里?
        FileOutputFormat.setOutputPath(job, new Path(args[1]));  
  
        //  交给yarn去执行,直到执行结束才退出本程序
        job.waitForCompletion(true);  
        
        /*
        String[] otherArgs = new GenericOptionsParser(conf,args).getRemainingArgs();
        if(otherArgs.length<2){
        	System.out.println("Usage:wordcount <in> [<in>...] <out>");
        	System.exit(2);
        }
        for(int i=0;i<otherArgs.length-1;i++){
        	FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
        }
        System.exit(job.waitForCompletion(tr0ue)?0:1);
        */
    }  
}  

    

    

    

     

    将准备到的文档导入进去

    

    

    目录结构如下:

    

    运行mapreduce程序

    

    

    

    

    

    OK,搞定收工!!!