搭建Hadoop环境，让其能够在Windows中进行开发
步骤1 关闭防火墙
先关闭防火墙，这样可以让比如Hadoop的50070端口供给外界访问
centOS 6.5关闭防火墙步骤
关闭命令： service iptables stop
永久关闭防火墙：chkconfig iptables off
两个命令同时运行，运行完成后查看防火墙关闭状态
service iptables status
步骤2 搭建伪分布式环境
具体搭建环境请参见Hadoop官网

注意 为了能够让其在Windows中能够通过IDEA访问虚拟机中的Hadoop，那么就需要在core-site.xml等配置文件中使用ip地址，而不是hostname，不然windows端会报Connection Error

执行bin/hadoop namenode -format
执行sbin/start-dfs.sh启动hdfs
执行sbin/start-yarn.sh启动yarn
步骤3 Windows端配置
1， windows端配置Hadoop 环境变量，

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2， Windows为了能够访问Hadoop，需要加入几个包放置到hadoop目录的bin文件夹中

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3， windows 在etc host文件配置能够访问虚拟机hadoop机器的hostname

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4, 打开IDEA开发项目，然后将配置文件放到resources文件中

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步骤4 IDEA开发Hadoop Yarn
这里以WordCount例子为例
package ComponentApp;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobConf;
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.util.Tool;
import org.apache.hadoop.util.ToolRunner;

import java.io.IOException;
/**

• Created by IBM on 2017/7/16.
  */
  public class WordCount2 implements Tool {
  public void setConf(Configuration configuration) {

  }

  public Configuration getConf() {
  return new JobConf(WordCount2.class);
  }

  public int run(String[] strings) throws Exception {
  try {
  Configuration conf = getConf();
  conf.set("mapreduce.job.jar", "D:\java\idea\ComponentApp\out\artifacts\ComponentApp_jar\ComponentApp.jar");
  conf.set("mapreduce.framework.name", "yarn");
  conf.set("yarn.resourcemanager.hostname", "192.168.137.131");
  conf.set("mapreduce.app-submission.cross-platform", "true");

       Job job = Job.getInstance(conf);
       job.setJarByClass(WordCount2.class);
  
       job.setOutputKeyClass(Text.class);
       job.setOutputValueClass(LongWritable.class);
  
       job.setMapperClass(WcMapper.class);
       job.setReducerClass(WcReducer.class);
  
       job.setInputFormatClass(TextInputFormat.class);
       job.setOutputFormatClass(TextOutputFormat.class);
  
       FileInputFormat.setInputPaths(job, "hdfs://192.168.137.131:9000/kason/myid");
       FileOutputFormat.setOutputPath(job, new Path("hdfs://192.168.137.131:9000/kason/out4"));
  
       job.waitForCompletion(true);
   } catch (Exception e) {
       e.printStackTrace();
   }
   return 0;
  

  }

  public static class WcMapper extends Mapper<LongWritable, Text, Text, LongWritable>{
  @Override
  protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
  String mVal = value.toString();
  String[] strs = mVal.split(" ");
  for(String s : strs) {
  System.out.println("data:" + s);
  context.write(new Text(s), new LongWritable(1));
  }
  }
  }
  public static class WcReducer extends Reducer<Text, LongWritable, Text, LongWritable>{
  @Override
  protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
  long sum = 0;
  for(LongWritable lVal : values){
  sum += lVal.get();
  }
  context.write(key, new LongWritable(sum));
  }
  }
  public static void main(String[] args) throws Exception {
  ToolRunner.run(new WordCount2(),args);
  }
  }
  IDEA运行结果

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YARN 页面

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HDFS页面

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