【Hadoop] | 易学教程

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：

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

配置步骤：

启动hadoop : start-yarn.sh、start-dfs.sh
windows本地配置Linux的主机IP映射：（不配置直接使用IP也行）
将hadoop-eclipse-plugin-2.7.3.jar放进eclipse的plugins目录，启动eclipse
eclipse配置Hadoop

切换MapReduce视图可以看到HDFS文件系统的信息

运行MapReduce程序

配置HADOOP环境变量：主要将bin、sbin放入PATH路径
将winutils.exe放在bin目录，hadoop.dll放在windows System32目录
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：

来源：oschina

链接：https://my.oschina.net/u/4074151/blog/3014236

标签

Squirrel

apache

MapReduce

eclipse

Hadoop