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：

<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://192.168.0.26:9000</value>
</property>
<property>
<name>hadoop.proxyuser.hadoop.hosts</name>
<value>*</value>
</property>
<property>
<name>hadoop.proxyuser.hadoop.groups</name>
<value>*</value>
</property>
</configuration>

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

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion>

<groupId>com.fun</groupId>
<artifactId>hadoop</artifactId>
<version>1.0-SNAPSHOT</version>

<repositories>
<repository>
<id>apache</id>
<url>http://maven.apache.org</url>
</repository>
</repositories>

<dependencies>

<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.6.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.6.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>2.6.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs-client</artifactId>
<version>2.8.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-jobclient</artifactId>
<version>2.6.0</version>
</dependency>
<dependency>
<groupId>commons-cli</groupId>
<artifactId>commons-cli</artifactId>
<version>1.2</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.10</artifactId>
<version>1.6.0</version>
</dependency>
<!-- https://mvnrepository.com/artifact/com.alibaba/fastjson -->
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>fastjson</artifactId>
<version>1.2.33</version>
</dependency>

</dependencies>

<build>
<plugins>
<plugin>
<artifactId>maven-dependency-plugin</artifactId>
<configuration>
<excludeTransitive>false</excludeTransitive>
<stripVersion>true</stripVersion>
<outputDirectory>./lib</outputDirectory>
</configuration>

</plugin>
</plugins>
</build>
</project>

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<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 StringTokeni
a051
zer(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 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！！