[转]Eclipse中远程调试Hadoop – ppKevin – 博客园

插件

话说Hadoop 1.0.2/src/contrib/eclipse-plugin只有插件的源代码，这里给出一个我打包好的对应的Eclipse插件：
下载地址

注：hadoop 1.0.2以后是需要自己编译的hadoop-eclipse-plugin-1.0.2.jar。

下载后扔到eclipse/dropins目录下即可，当然eclipse/plugins也是可以的，前者更为轻便，推荐；重启Eclipse，即可在透视图(Perspective)中看到Map/Reduce。

配置

点击蓝色的小象图标，新建一个Hadoop连接：

2

注意，一定要填写正确，修改了某些端口，以及默认运行的用户名等

具体的设置，可见

正常情况下，可以在项目区域可以看到

image

这样可以正常的进行HDFS分布式文件系统的管理：上传，删除等操作。

为下面测试做准备，需要先建了一个目录 user/root/input2，然后上传两个txt文件到此目录：

intput1.txt 对应内容：Hello Hadoop Goodbye Hadoop

intput2.txt 对应内容：Hello World Bye World

HDFS的准备工作好了，下面可以开始测试了。

Hadoop工程

新建一个Map/Reduce Project工程，设定好本地的hadoop目录

1

新建一个测试类WordCountTest：

 

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package com.hadoop.learn.test;
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;
import org.apache.log4j.Logger;
/**
 * 运行测试程序
 *
 * @author yongboy
 * @date 2012-04-16
 */
public class WordCountTest {
    private static final Logger log = Logger.getLogger(WordCountTest.class);
    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 {
            log.info("Map key : " + key);
            log.info("Map value : " + value);
            StringTokenizer itr = new StringTokenizer(value.toString());
            while (itr.hasMoreTokens()) {
                String wordStr = itr.nextToken();
                word.set(wordStr);
                log.info("Map word : " + wordStr);
                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 {
            log.info("Reduce key : " + key);
            log.info("Reduce value : " + values);
            int sum = 0;
            for (IntWritable val : values) {
                sum += val.get();
            }
            result.set(sum);
            log.info("Reduce sum : " + 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: WordCountTest <in> <out>");
            System.exit(2);
        }
        Job job = new Job(conf, "word count");
        job.setJarByClass(WordCountTest.class);
        job.setMapperClass(TokenizerMapper.class);
        job.setCombinerClass(IntSumReducer.class);
        job.setReducerClass(IntSumReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

 

右键，选择“Run Configurations”,弹出窗口，点击“Arguments”选项卡,在“Program argumetns”处预先输入参数:

 

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hdfs://master:9000/user/root/input2 dfs://master:9000/user/root/output2

 

备注：参数为了在本地调试使用，而非真实环境。

然后，点击“Apply”，然后“Close”。现在可以右键，选择“Run on Hadoop”，运行。

但此时会出现类似异常信息：

 

12/04/24 15:32:44 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable 12/04/24 15:32:44 ERROR security.UserGroupInformation: PriviledgedActionException as:Administrator cause:java.io.IOException: Failed to set permissions of path: \tmp\hadoop-Administrator\mapred\staging\Administrator-519341271\.staging to 0700 Exception in thread “main” java.io.IOException: Failed to set permissions of path: \tmp\hadoop-Administrator\mapred\staging\Administrator-519341271\.staging to 0700 at org.apache.hadoop.fs.FileUtil.checkReturnValue(FileUtil.java:682) at org.apache.hadoop.fs.FileUtil.setPermission(FileUtil.java:655) at org.apache.hadoop.fs.RawLocalFileSystem.setPermission(RawLocalFileSystem.java:509) at org.apache.hadoop.fs.RawLocalFileSystem.mkdirs(RawLocalFileSystem.java:344) at org.apache.hadoop.fs.FilterFileSystem.mkdirs(FilterFileSystem.java:189) at org.apache.hadoop.mapreduce.JobSubmissionFiles.getStagingDir(JobSubmissionFiles.java:116) at org.apache.hadoop.mapred.JobClient$2.run(JobClient.java:856) at org.apache.hadoop.mapred.JobClient$2.run(JobClient.java:850) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:396) at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1093) at org.apache.hadoop.mapred.JobClient.submitJobInternal(JobClient.java:850) at org.apache.hadoop.mapreduce.Job.submit(Job.java:500) at org.apache.hadoop.mapreduce.Job.waitForCompletion(Job.java:530) at com.hadoop.learn.test.WordCountTest.main(WordCountTest.java:85)

 

这个是Windows下文件权限问题，在Linux下可以正常运行，不存在这样的问题。

解决方法是，修改/hadoop-1.0.2/src/core/org/apache/hadoop/fs/FileUtil.java里面的checkReturnValue，注释掉即可（有些粗暴，在Window下，可以不用检查）：

 

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......
  private static void checkReturnValue(boolean rv, File p,
                                       FsPermission permission
                                       ) throws IOException {
    /**
    if (!rv) {
      throw new IOException("Failed to set permissions of path: " + p +
                            " to " +
                            String.format("%04o", permission.toShort()));
    }
    **/
  }
......

 

重新编译打包hadoop-core-1.0.2.jar，替换掉hadoop-1.0.2根目录下的hadoop-core-1.0.2.jar即可。

这里提供一份修改版的hadoop-core-1.0.2-modified.jar文件，替换原hadoop-core-1.0.2.jar即可。

替换之后，刷新项目，设置好正确的jar包依赖，现在再运行WordCountTest，即可。

成功之后，在Eclipse下刷新HDFS目录，可以看到生成了ouput2目录：

image

点击“ part-r-00000”文件，可以看到排序结果：

 

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Bye    1
Goodbye    1
Hadoop    2
Hello    2
World    2

 

嗯，一样可以正常Debug调试该程序，设置断点（右键 –> Debug As – > Java Application），即可（每次运行之前，都需要收到删除输出目录）。

另外，该插件会在eclipse对应的workspace\.metadata\.plugins\org.apache.hadoop.eclipse下，自动生成jar文件，以及其他文件，包括Haoop的一些具体配置等。

嗯，更多细节，慢慢体验吧。

遇到的异常

 

org.apache.hadoop.ipc.RemoteException: org.apache.hadoop.hdfs.server.namenode.SafeModeException: Cannot create directory /user/root/output2/_temporary. Name node is in safe mode.
The ratio of reported blocks 0.5000 has not reached the threshold 0.9990. Safe mode will be turned off automatically.
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.mkdirsInternal(FSNamesystem.java:2055)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.mkdirs(FSNamesystem.java:2029)
at org.apache.hadoop.hdfs.server.namenode.NameNode.mkdirs(NameNode.java:817)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
at java.lang.reflect.Method.invoke(Method.java:597)
at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:563)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:1388)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:1384)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:396)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1093)
at org.apache.hadoop.ipc.Server$Handler.run(Server.java:1382)

 

在主节点处，关闭掉安全模式：

 

#bin/hadoop dfsadmin –safemode leave

 

如何打包

将创建的Map/Reduce项目打包成jar包，很简单的事情，无需多言。保证jar文件的META-INF/MANIFEST.MF文件中存在Main-Class映射：

 

Main-Class: com.hadoop.learn.test.TestDriver

 

若使用到第三方jar包，那么在MANIFEST.MF中增加Class-Path好了。

另外可使用插件提供的MapReduce Driver向导，可以帮忙我们在Hadoop中运行，直接指定别名，尤其是包含多个Map/Reduce作业时，很有用。

一个MapReduce Driver只要包含一个main函数，指定别名：

 

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package com.hadoop.learn.test;
import org.apache.hadoop.util.ProgramDriver;
/**
 *
 * @author yongboy
 * @time 2012-4-24
 * @version 1.0
 */
public class TestDriver {
    public static void main(String[] args) {
        int exitCode = -1;
        ProgramDriver pgd = new ProgramDriver();
        try {
            pgd.addClass("testcount", WordCountTest.class,
                    "A test map/reduce program that counts the words in the input files.");
            pgd.driver(args);
            exitCode = 0;
        } catch (Throwable e) {
            e.printStackTrace();
        }
        System.exit(exitCode);
    }
}

 

这里有一个小技巧，MapReduce Driver类上面，右键运行，Run on Hadoop，会在Eclipse的workspace\.metadata\.plugins\org.apache.hadoop.eclipse目录下自动生成jar包，上传到HDFS，或者远程hadoop根目录下，运行它:

 

# bin/hadoop jar LearnHadoop_TestDriver.java-460881982912511899.jar testcount input2 output3

 

OK，本文结束。

来源URL：http://www.cnblogs.com/ppkevin/archive/2012/10/10/2718136.html