windows10下使用idea远程调试hadoop集群
在windows10环境下，使用idea搭建maven项目链接Linux上的hadoop集群。
注意事项：
保证hadoop集群的用户与Windows的用户一致，不然后报错，错误信息我忘了，反正很麻烦
1. 下载hadoop-2.6.0.tar.gz，解压到本地文件夹：D:\configureSoftWare\hadoop-2.6.0
2. 配置hadoop环境变量： %HADOOP_HOME% = D:\configureSoftWare\hadoop-2.6.0
3. 将winutils.exe文件拷贝到%HADOOP_HOME%/bin 目录下
4. hadoop.dll文件拷贝到C:\Windows\System32目录下
winutils.lb和hadoop.dll的下载地址：http://pan.baidu.com/s/1hrNXq3y
5. 新建一个maven项目，这个比较简单，网上很多创建maven工程的文章，创建好以后项目结构如下：
6. 如图，将hadoop-2.6.0/etc/hadoop文件夹下的core-site.xml和log4j.properties文件拷贝到resources文件夹下
在core-site.xml中添加配置：
fs.defaultFS
hdfs://192.168.0.26:9000
hadoop.proxyuser.hadoop.hosts
*
hadoop.proxyuser.hadoop.groups
*
fs.defaultFS处换为hadoop集群的namenode的IP地址。
7. 要想使用hadoop，害得添加依赖包。修改pom.xml文件
4.0.0
com.fun
hadoop
1.0-SNAPSHOT
apache
http://maven.apache.org
org.apache.hadoop
hadoop-common
2.6.0
org.apache.hadoop
hadoop-client
2.6.0
org.apache.hadoop
hadoop-hdfs
2.6.0
org.apache.hadoop
hadoop-hdfs-client
2.8.0
org.apache.hadoop
hadoop-mapreduce-client-jobclient
2.6.0
commons-cli
commons-cli
1.2
org.apache.spark
spark-core_2.10
1.6.0
com.alibaba
fastjson
1.2.33
maven-dependency-plugin
false
true
./lib
8. 到此为止，所有环境已搭建好，我们来试一试，使用最经典的Wordcount测试一下
package MR;
/**
* Created by hadoop on 2017/5/25.
*/
/**
* Created by jinshilin on 16/12/7.
*/
import java.io.IOException;
import java.net.URI;
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;
public class WordCount {
public static class TokenizerMapper
extends Mapper {
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 {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable 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 Exception {
Configuration conf = new Configuration();
// Path input = new Path("hdfs://192.168.0.26:9000/people");
Path input = new Path(URI.create("hdfs://192.168.0.26:9000/people"));
Path output = new Path(URI.create("hdfs://192.168.0.26:9000/output"));
Job job = Job.getInstance(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, input);
FileOutputFormat.setOutputPath(job, output);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
} 10 . 运行程序：
在HDFS上查看结果：
11 . 大功告成！！