文章说明：因Linux平台再GUI页面通过IDE进行Hadoop开发，会导致Linux在GUI上极度消耗资源，对于一些配置不是很高的PC，可能会出现卡顿的情况，非常影响程序编写，本文就详细介绍如何在windows平台进行hadoop开发，希望对各位学习Hadoop的同学优异

工具准备：

hadoop eclipse插件：hadoop-eclipse-plugin-2.7.3.jar
hadoop windows平台支持组件：winutils.exe
hadoop底层依赖库：hadoop.dll

上述工具下载地址：下载地址

Hadoop版本 : hadoop-2.7.3

开发步骤：
1. 启动hadoop : start-yarn.sh、start-dfs.sh (配置hadoop看历史文章)
2. windows本地配置Linux的主机IP映射：（不配置直接使用IP也行）
3. 将hadoop-eclipse-plugin-2.7.3.jar放进eclipse的plugins目录，启动eclipse
4. eclipse配置Hadoop


5. 切换MapReduce视图可以看到HDFS文件系统的信息

运行MapReduce程序配置步骤：
1. 配置HADOOP环境变量：主要将bin、sbin放入PATH路径
2. 将winutils.exe放在bin目录，hadoop.dll放在windows System32目录
3. 测试代码

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.output.FileOutputFormat;

/**
 * 统计文本词频信息
 * @author Zerone1993
 */
public class WordCount {

    static class WordMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
        @Override
        protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
                throws IOException, InterruptedException {

            String str = value.toString();

            StringTokenizer st = new StringTokenizer(str);

            while(st.hasMoreTokens()){

                String temp = st.nextToken();

                context.write(new Text(temp), new IntWritable(1));
            }
        }
    }

    static class WordReducer extends Reducer<Text, IntWritable, Text, IntWritable>{

            @Override
            protected void reduce(Text arg0, Iterable<IntWritable> arg1,
                    Reducer<Text, IntWritable, Text, IntWritable>.Context arg2) throws IOException, InterruptedException {

                     int sum = 0;

                     for(IntWritable temp : arg1){

                         sum = sum + temp.get();

                     }

                     arg2.write(new Text(arg0), new IntWritable(sum));
             }
    }

    public static void main(String[] args) {

        Configuration conf = new Configuration();

        conf.set("mapred.job.tracker", "master:50020");

        try{

        Job job = Job.getInstance(conf, "wordCount");
        job.setJarByClass(WordCount.class); //设置启动作业类
        job.setMapperClass(WordMapper.class); //设置Map类
        job.setReducerClass(WordReducer.class);
        job.setMapOutputKeyClass(Text.class); //设置mapper输出的key类型
        job.setMapOutputValueClass(IntWritable.class); //设置mapper输出的value类型
        job.setNumReduceTasks(1); //设置Reduce Task的数量

        //设置mapreduce的输入和输出目录
        FileInputFormat.addInputPath(job, new Path("hdfs://master:9090/user/squirrel/input/mapreduce/"));
        FileOutputFormat.setOutputPath(job, new Path("hdfs://master:9090/user/squirrel/output/mapreduce/") );

        //等待mapreduce整个过程完成
        System.exit(job.waitForCompletion(true)0:1);

        }catch(Exception e){
            e.printStackTrace();
        }
    }
}

效果：

至此整个WINDOWS HADOOP环境搭建完毕…….