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          http://www.aygfsteel.com/yongboy/archive/2012/04/26/376486.html

          插件

          話說Hadoop 1.0.2/src/contrib/eclipse-plugin只有插件的源代碼,這里給出一個我打包好的對應的Eclipse插件:
          下載地址

          下載后扔到eclipse/dropins目錄下即可,當然eclipse/plugins也是可以的,前者更為輕便,推薦;重啟Eclipse,即可在透視圖(Perspective)中看到Map/Reduce。

          配置

          點擊藍色的小象圖標,新建一個Hadoop連接:

          2

          注意,一定要填寫正確,修改了某些端口,以及默認運行的用戶名等

          具體的設置,可見

          正常情況下,可以在項目區域可以看到

          image

          這樣可以正常的進行HDFS分布式文件系統的管理:上傳,刪除等操作。

          為下面測試做準備,需要先建了一個目錄 user/root/input2,然后上傳兩個txt文件到此目錄:

          intput1.txt 對應內容:Hello Hadoop Goodbye Hadoop

          intput2.txt 對應內容:Hello World Bye World

          HDFS的準備工作好了,下面可以開始測試了。

          Hadoop工程

          新建一個Map/Reduce Project工程,設定好本地的hadoop目錄

          1

          新建一個測試類WordCountTest:

          1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87
          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;
           
          /**
          * 運行測試程序
          *
          * @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);
          }
          }

          右鍵,選擇“Run Configurations”,彈出窗口,點擊“Arguments”選項卡,在“Program argumetns”處預先輸入參數:

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

          備注:參數為了在本地調試使用,而非真實環境。

          然后,點擊“Apply”,然后“Close”。現在可以右鍵,選擇“Run on Hadoop”,運行。

          但此時會出現類似異常信息:

          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)

          這個是Windows下文件權限問題,在Linux下可以正常運行,不存在這樣的問題。

          解決方法是,修改/hadoop-1.0.2/src/core/org/apache/hadoop/fs/FileUtil.java里面的checkReturnValue,注釋掉即可(有些粗暴,在Window下,可以不用檢查):

          1 2 3 4 5 6 7 8 9 10 11 12 13
          ......
          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()));
          }
          **/
          }
          ......
          view raw FileUtil.java This Gist brought to you by GitHub.

          重新編譯打包hadoop-core-1.0.2.jar,替換掉hadoop-1.0.2根目錄下的hadoop-core-1.0.2.jar即可。

          這里提供一份修改版的hadoop-core-1.0.2-modified.jar文件,替換原hadoop-core-1.0.2.jar即可。

          替換之后,刷新項目,設置好正確的jar包依賴,現在再運行WordCountTest,即可。

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

          image

          點擊“ part-r-00000”文件,可以看到排序結果:

          Bye    1
          Goodbye    1
          Hadoop    2
          Hello    2
          World    2

          嗯,一樣可以正常Debug調試該程序,設置斷點(右鍵 –> Debug As – > Java Application),即可(每次運行之前,都需要收到刪除輸出目錄)。

          另外,該插件會在eclipse對應的workspace\.metadata\.plugins\org.apache.hadoop.eclipse下,自動生成jar文件,以及其他文件,包括Haoop的一些具體配置等。

          嗯,更多細節,慢慢體驗吧。

          遇到的異常

          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)

          在主節點處,關閉掉安全模式:

          #bin/hadoop dfsadmin –safemode leave

          如何打包

          將創建的Map/Reduce項目打包成jar包,很簡單的事情,無需多言。保證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向導,可以幫忙我們在Hadoop中運行,直接指定別名,尤其是包含多個Map/Reduce作業時,很有用。

          一個MapReduce Driver只要包含一個main函數,指定別名:

          1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
          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);
          }
          }

          這里有一個小技巧,MapReduce Driver類上面,右鍵運行,Run on Hadoop,會在Eclipse的workspace\.metadata\.plugins\org.apache.hadoop.eclipse目 錄下自動生成jar包,上傳到HDFS,或者遠程hadoop根目錄下,運行它:

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

          OK,本文結束。

          posted on 2013-02-22 14:06 SIMONE 閱讀(3269) 評論(1)  編輯  收藏 所屬分類: hbase

          FeedBack:
          # re: Hadoop學習筆記之在Eclipse中遠程調試Hadoop
          2013-05-21 09:06 | vigiles
          你好!
          請問如何重新編譯打包hadoop-core-1.0.2.jar?  回復  更多評論
            
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