windows本地eclispe运行linux上hadoop的maperduce程序

继续上一篇博文：hadoop集群的搭建

1.将linux节点上的hadoop安装包从linux上下载下来（你也可以从网上直接下载压缩包，解压后放到自己电脑上）

我的地址是：

2.配置环境变量：

HADOOP_HOME      D:hadoop-2.6.5

Path中添加：%HADOOP_HOME%in

3.下载hadoop-common-bin-master2.7.1

并且拷贝其中的winutils.exe,libwinutils.lib这两个文件到hadoop安装目录的 bin目录下

拷贝其中hadoop.dll，拷贝到c:windowssystem32；

3.下载eclipse的hadoop插件

4.拷贝到eclispe的plugin文件夹中

 5.eclispe==》window==》Preferences

6.window==》show view==》other

显示面版

7.Map.Reduce Locations 面版中右击

8.选择 第一个New Hadoop location

9.面板中多出来一头小象

并且左侧的Project Explorer窗口中的DFS Locations看到我们刚才新建的hadoop Location。

10.linux上准备测试文件到

/opt中新建文件 hadoop.txt内容如下：

11.上传到hadoop

hadoop fs -put /opt/hadoop.txt /test/input/hadoop.txt

 12.刷新eclipes的Hadoop Location 有我们刚才上传的文件

13.创建项目 File==>New==>Other

14.项目名称

 15.编写源码：

package com.myFirstHadoop;

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 WorkCount {
    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 IOException, ClassNotFoundException, InterruptedException {
        Configuration conf=new Configuration();
        String[] otherArgs=new GenericOptionsParser(conf,args).getRemainingArgs();
        if(otherArgs.length<2){
            System.err.println("Useage:wordCount <in> [<in> ...] <out>");
            System.exit(2);
        }
        Job job=new Job(conf,"word count");
        job.setJarByClass(WorkCount.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);
        }
    }
}

16.运行前的修改

右击==》run as ==》Run Configurations

前面一个hdfs是输入文件；后面一个hdfs是输出目录

17.回到主界面右击==》Run As==》Run on Hadoop 等运行结束后查看Hadoop目录

18.查看运行结果：

19.收工。