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From Apache Wiki <wikidi...@apache.org>
Subject [Hadoop Wiki] Trivial Update of "Running Hadoop On Ubuntu Linux (Single-Node Cluster)" by MarkSchnitzius
Date Wed, 02 Jul 2008 07:18:25 GMT
Dear Wiki user,

You have subscribed to a wiki page or wiki category on "Hadoop Wiki" for change notification.

The following page has been changed by MarkSchnitzius:
http://wiki.apache.org/hadoop/Running_Hadoop_On_Ubuntu_Linux_%28Single-Node_Cluster%29

------------------------------------------------------------------------------
    hadoop@ubuntu:~$
  }}}
  
- The second line will create an RSA key pair with an empty password. Generally, using an
empty password is not recommended, but in this case it is needed to unlock the key without
your interaction (you don't want to enter the passphrase everytime Hadoop interacts with its
nodes).
+ The second line will create an RSA key pair with an empty password. Generally, using an
empty password is not recommended, but in this case it is needed to unlock the key without
your interaction (you don't want to enter the passphrase every time Hadoop interacts with
its nodes).
  
  Second, you have to enable SSH access to your local machine with this newly created key.
  
@@ -188, +188 @@

    scheme and authority determine the FileSystem implementation.  The
    uri's scheme determines the config property (fs.SCHEME.impl) naming
    the FileSystem implementation class.  The uri's authority is used to
-   determine the host, port, etc. for a filesystem.</description>
+   determine the host, port, etc. for a FileSystem.</description>
  </property>
  
  <property>
@@ -321, +321 @@

  
  == Running a MapReduce job ==
  
- We will now run your first HadoopMapReduce job. We will use the WordCount example job which
reads text files and counts how often words occur. The input is text files and the output
is text files, each line of which contains a word and the count of how often it occured, separated
by a tab. More information of what happens behind the scenes is available at the WordCount
article.
+ We will now run your first HadoopMapReduce job. We will use the WordCount example job which
reads text files and counts how often words occur. The input is text files and the output
is text files, each line of which contains a word and the count of how often it occurred,
separated by a tab. More information of what happens behind the scenes is available at the
WordCount article.
  
  === Download example input data ===
  
@@ -368, +368 @@

  
  === Run the MapReduce job ===
  
- Now, we actually run the WordCount examle job.
+ Now, we actually run the WordCount example job.
  
  {{{
    hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop jar hadoop-0.14.2-examples.jar wordcount gutenberg
gutenberg-output

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