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eclipse/intellij idea 远程调试hadoop 2.6.0

很多hadoop初学者估计都我一样,由于没有足够的机器资源,只能在虚拟机里弄一个linux安装hadoop的伪分布,然后在host机上win7里使用eclipse或Intellj idea来写代码测试,那么问题来了,win7下的eclipse或intellij idea如何远程提交map/reduce任务到远程hadoop,并断点调试?

一、准备工作

1.1 在win7中,找一个目录,解压hadoop-2.6.0,本文中是D:\yangjm\Code\study\hadoop\hadoop-2.6.0 (以下用$HADOOP_HOME表示)

1.2 在win7中添加几个环境变量

HADOOP_HOME=D:\yangjm\Code\study\hadoop\hadoop-2.6.0

HADOOP_BIN_PATH=%HADOOP_HOME%\bin

HADOOP_PREFIX=D:\yangjm\Code\study\hadoop\hadoop-2.6.0

另外,PATH变量在最后追加;%HADOOP_HOME%\bin

二、eclipse远程调试

1.1 下载hadoop-eclipse-plugin插件

hadoop-eclipse-plugin是一个专门用于eclipse的hadoop插件,可以直接在IDE环境中查看hdfs的目录和文件内容。其源代码托管于github上,官网地址是 https://github.com/winghc/hadoop2x-eclipse-plugin

有兴趣的可以自己下载源码编译,百度一下N多文章,但如果只是使用 https://github.com/winghc/hadoop2x-eclipse-plugin/tree/master/release%20这里已经提供了各种编译好的版本,直接用就行,将下载后的hadoop-eclipse-plugin-2.6.0.jar复制到eclipse/plugins目录下,然后重启eclipse就完事了

1.2 下载windows64位平台的hadoop2.6插件包(hadoop.dll,winutils.exe)

在hadoop2.6.0源码的hadoop-common-project\hadoop-common\src\main\winutils下,有一个vs.net工程,编译这个工程可以得到这一堆文件,输出的文件中,

hadoop.dll、winutils.exe 这二个最有用,将winutils.exe复制到$HADOOP_HOME\bin目录,将hadoop.dll复制到%windir%\system32目录 (主要是防止插件报各种莫名错误,比如空对象引用啥的)

注:如果不想编译,可直接下载编译好的文件 hadoop2.6(x64)V0.2.rar

1.3 配置hadoop-eclipse-plugin插件

启动eclipse,windows->show view->other

window->preferences->hadoop map/reduce 指定win7上的hadoop根目录(即:$HADOOP_HOME)

然后在Map/Reduce Locations 面板中,点击小象图标

添加一个Location

这个界面灰常重要,解释一下几个参数:

Location name 这里就是起个名字,随便起

Map/Reduce(V2) Master Host 这里就是虚拟机里hadoop master对应的IP地址,下面的端口对应 hdfs-site.xml里dfs.datanode.ipc.address属性所指定的端口

DFS Master Port: 这里的端口,对应core-site.xml里fs.defaultFS所指定的端口

最后的user name要跟虚拟机里运行hadoop的用户名一致,我是用hadoop身份安装运行hadoop 2.6.0的,所以这里填写hadoop,如果你是用root安装的,相应的改成root

这些参数指定好以后,点击Finish,eclipse就知道如何去连接hadoop了,一切顺利的话,在Project Explorer面板中,就能看到hdfs里的目录和文件了

可以在文件上右击,选择删除试下,通常第一次是不成功的,会提示一堆东西,大意是权限不足之类,原因是当前的win7登录用户不是虚拟机里hadoop的运行用户,解决办法有很多,比如你可以在win7上新建一个hadoop的管理员用户,然后切换成hadoop登录win7,再使用eclipse开发,但是这样太烦,最简单的办法:

hdfs-site.xml里添加

<property>
 <name>dfs.permissions</name>
 <value>false</value>
 </property>

然后在虚拟机里,运行hadoop dfsadmin -safemode leave

保险起见,再来一个 hadoop fs -chmod 777 /

总而言之,就是彻底把hadoop的安全检测关掉(学习阶段不需要这些,正式生产上时,不要这么干),最后重启hadoop,再到eclipse里,重复刚才的删除文件操作试下,应该可以了。

1.4 创建WoldCount示例项目

新建一个项目,选择Map/Reduce Project

后面的Next就行了,然后放一上WodCount.java,代码如下:

package yjmyzz;

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.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;
import org.apache.hadoop.util.GenericOptionsParser;

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 StringTokenizer(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(); 
 String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
 if (otherArgs.length < 2) {
  System.err.println("Usage: wordcount <in> [<in>...] <out>");
  System.exit(2);
 }
 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);
 for (int i = 0; i < otherArgs.length - 1; ++i) {
  FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
 }
 FileOutputFormat.setOutputPath(job,
  new Path(otherArgs[otherArgs.length - 1]));
 System.exit(job.waitForCompletion(true) ? 0 : 1);
 }
}

然后再放一个log4j.properties,内容如下:(为了方便运行起来后,查看各种输出)

log4j.rootLogger=INFO, stdout

#log4j.logger.org.springframework=INFO
#log4j.logger.org.apache.activemq=INFO
#log4j.logger.org.apache.activemq.spring=WARN
#log4j.logger.org.apache.activemq.store.journal=INFO
#log4j.logger.org.activeio.journal=INFO

log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d{ABSOLUTE} | %-5.5p | %-16.16t | %-32.32c{1} | %-32.32C %4L | %m%n

最终的目录结构如下:

然后可以Run了,当然是不会成功的,因为没给WordCount输入参数,参考下图:

1.5 设置运行参数

因为WordCount是输入一个文件用于统计单词字,然后输出到另一个文件夹下,所以给二个参数,参考上图,在Program arguments里,输入

hdfs://172.28.20.xxx:9000/jimmy/input/README.txt
hdfs://172.28.20.xxx:9000/jimmy/output/

大家参考这个改一下(主要是把IP换成自己虚拟机里的IP),注意的是,如果input/READM.txt文件没有,请先手动上传,然后/output/ 必须是不存在的,否则程序运行到最后,发现目标目录存在,也会报错,这个弄完后,可以在适当的位置打个断点,终于可以调试了:

三、intellij idea 远程调试hadoop

3.1 创建一个maven的WordCount项目

pom文件如下:

<?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>yjmyzz</groupId>
 <artifactId>mapreduce-helloworld</artifactId>
 <version>1.0-SNAPSHOT</version>

 <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-mapreduce-client-jobclient</artifactId>
  <version>2.6.0</version>
 </dependency>
 <dependency>
  <groupId>commons-cli</groupId>
  <artifactId>commons-cli</artifactId>
  <version>1.2</version>
 </dependency>
 </dependencies>

 <build>
 <finalName>${project.artifactId}</finalName>
 </build>

</project>

项目结构如下:

项目上右击-》Open Module Settings 或按F12,打开模块属性

添加依赖的Libary引用

然后把$HADOOP_HOME下的对应包全导进来

导入的libary可以起个名称,比如hadoop2.6

3.2 设置运行参数

注意二个地方

1是Program aguments,这里跟eclipes类似的做法,指定输入文件和输出文件夹

2是Working Directory,即工作目录,指定为$HADOOP_HOME所在目录

然后就可以调试了

intellij下唯一不爽的,由于没有类似eclipse的hadoop插件,每次运行完wordcount,下次再要运行时,只能手动命令行删除output目录,再行调试。为了解决这个问题,可以将WordCount代码改进一下,在运行前先删除output目录,见下面的代码:

package yjmyzz;

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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;
import org.apache.hadoop.util.GenericOptionsParser;

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 StringTokenizer(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);
 }
 }


 /**
 * 删除指定目录
 *
 * @param conf
 * @param dirPath
 * @throws IOException
 */
 private static void deleteDir(Configuration conf, String dirPath) throws IOException {
 FileSystem fs = FileSystem.get(conf);
 Path targetPath = new Path(dirPath);
 if (fs.exists(targetPath)) {
  boolean delResult = fs.delete(targetPath, true);
  if (delResult) {
  System.out.println(targetPath + " has been deleted sucessfullly.");
  } else {
  System.out.println(targetPath + " deletion failed.");
  }
 }

 }

 public static void main(String[] args) throws Exception {
 Configuration conf = new Configuration();
 String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
 if (otherArgs.length < 2) {
  System.err.println("Usage: wordcount <in> [<in>...] <out>");
  System.exit(2);
 }

 //先删除output目录
 deleteDir(conf, otherArgs[otherArgs.length - 1]);

 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);
 for (int i = 0; i < otherArgs.length - 1; ++i) {
  FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
 }
 FileOutputFormat.setOutputPath(job,
  new Path(otherArgs[otherArgs.length - 1]));
 System.exit(job.waitForCompletion(true) ? 0 : 1);
 }
}

但是光这样还不够,在IDE环境中运行时,IDE需要知道去连哪一个hdfs实例(就好象在db开发中,需要在配置xml中指定DataSource一样的道理),将$HADOOP_HOME\etc\hadoop下的core-site.xml,复制到resouces目录下,类似下面这样:

里面的内容如下:

<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
 <property>
 <name>fs.defaultFS</name>
 <value>hdfs://172.28.20.***:9000</value>
 </property>
</configuration>

上面的IP换成虚拟机里的IP即可。

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