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<h1> 
      Hadoop On Demand
    </h1>
<div id="minitoc-area">
<ul class="minitoc">
<li>
<a href="#Overview">Overview</a>
</li>
<li>
<a href="#Pre-requisites">Pre-requisites</a>
</li>
<li>
<a href="#Resource+Manager">Resource Manager</a>
</li>
<li>
<a href="#Installing+HOD">Installing HOD</a>
</li>
<li>
<a href="#Configuring+HOD">Configuring HOD</a>
<ul class="minitoc">
<li>
<a href="#Minimal+Configuration+to+get+started">Minimal Configuration to get started</a>
</li>
<li>
<a href="#Advanced+Configuration">Advanced Configuration</a>
</li>
</ul>
</li>
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<a href="#Running+HOD">Running HOD</a>
</li>
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<a href="#Supporting+Tools+and+Utilities">Supporting Tools and Utilities</a>
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<li>
<a href="#logcondense.py+-+Tool+for+removing+log+files+uploaded+to+DFS">logcondense.py - Tool for removing log files uploaded to DFS</a>
<ul class="minitoc">
<li>
<a href="#Running+logcondense.py">Running logcondense.py</a>
</li>
<li>
<a href="#Command+Line+Options+for+logcondense.py">Command Line Options for logcondense.py</a>
</li>
</ul>
</li>
</ul>
</li>
</ul>
</div>

<a name="N1000C"></a><a name="Overview"></a>
<h2 class="h3">Overview</h2>
<div class="section">
<p>The Hadoop On Demand (HOD) project is a system for provisioning and
managing independent Hadoop MapReduce and HDFS instances on a shared cluster 
of nodes. HOD is a tool that makes it easy for administrators and users to 
quickly setup and use Hadoop. It is also a very useful tool for Hadoop developers 
and testers who need to share a physical cluster for testing their own Hadoop 
versions.
</p>
<p>HOD relies on a resource manager (RM) for allocation of nodes that it can use for
running Hadoop instances. At present it runs with the <a href="http://www.clusterresources.com/pages/products/torque-resource-manager.php">Torque
resource manager</a>.
</p>
<p>
The basic system architecture of HOD includes components from:</p>
<ul>
  
<li>A Resource manager (possibly together with a scheduler),</li>
  
<li>HOD components, and </li>
  
<li>Hadoop Map/Reduce and HDFS daemons.</li>

</ul>
<p>
HOD provisions and maintains Hadoop Map/Reduce and, optionally, HDFS instances 
through interaction with the above components on a given cluster of nodes. A cluster of
nodes can be thought of as comprising of two sets of nodes:</p>
<ul>
  
<li>Submit nodes: Users use the HOD client on these nodes to allocate clusters, and then
use the Hadoop client to submit Hadoop jobs. </li>
  
<li>Compute nodes: Using the resource manager, HOD components are run on these nodes to 
provision the Hadoop daemons. After that Hadoop jobs run on them.</li>

</ul>
<p>
Here is a brief description of the sequence of operations in allocating a cluster and
running jobs on them.
</p>
<ul>
  
<li>The user uses the HOD client on the Submit node to allocate a required number of
cluster nodes, and provision Hadoop on them.</li>
  
<li>The HOD client uses a Resource Manager interface, (qsub, in Torque), to submit a HOD
process, called the RingMaster, as a Resource Manager job, requesting the user desired number 
of nodes. This job is submitted to the central server of the Resource Manager (pbs_server, in Torque).</li>
  
<li>On the compute nodes, the resource manager slave daemons, (pbs_moms in Torque), accept
and run jobs that they are given by the central server (pbs_server in Torque). The RingMaster 
process is started on one of the compute nodes (mother superior, in Torque).</li>
  
<li>The Ringmaster then uses another Resource Manager interface, (pbsdsh, in Torque), to run
the second HOD component, HodRing, as distributed tasks on each of the compute
nodes allocated.</li>
  
<li>The Hodrings, after initializing, communicate with the Ringmaster to get Hadoop commands, 
and run them accordingly. Once the Hadoop commands are started, they register with the RingMaster,
giving information about the daemons.</li>
  
<li>All the configuration files needed for Hadoop instances are generated by HOD itself, 
some obtained from options given by user in its own configuration file.</li>
  
<li>The HOD client keeps communicating with the RingMaster to find out the location of the 
JobTracker and HDFS daemons.</li>

</ul>
<p>The rest of the document deals with the steps needed to setup HOD on a physical cluster of nodes.</p>
</div>


<a name="N10056"></a><a name="Pre-requisites"></a>
<h2 class="h3">Pre-requisites</h2>
<div class="section">
<p>Operating System: HOD is currently tested on RHEL4.<br>
Nodes : HOD requires a minimum of 3 nodes configured through a resource manager.<br>
</p>
<p> Software </p>
<p>The following components are to be installed on *ALL* the nodes before using HOD:</p>
<ul>
 
<li>Torque: Resource manager</li>
 
<li>
<a href="http://www.python.org">Python</a> : HOD requires version 2.5.1 of Python.</li>

</ul>
<p>The following components can be optionally installed for getting better
functionality from HOD:</p>
<ul>
 
<li>
<a href="http://twistedmatrix.com/trac/">Twisted Python</a>: This can be
  used for improving the scalability of HOD. If this module is detected to be
  installed, HOD uses it, else it falls back to default modules.</li>
 
<li>
<a href="http://hadoop.apache.org/core/">Hadoop</a>: HOD can automatically
 distribute Hadoop to all nodes in the cluster. However, it can also use a
 pre-installed version of Hadoop, if it is available on all nodes in the cluster.
  HOD currently supports Hadoop 0.15 and above.</li>

</ul>
<p>NOTE: HOD configuration requires the location of installs of these
components to be the same on all nodes in the cluster. It will also
make the configuration simpler to have the same location on the submit
nodes.
</p>
</div>


<a name="N1008A"></a><a name="Resource+Manager"></a>
<h2 class="h3">Resource Manager</h2>
<div class="section">
<p>  Currently HOD works with the Torque resource manager, which it uses for its node
  allocation and job submission. Torque is an open source resource manager from
  <a href="http://www.clusterresources.com">Cluster Resources</a>, a community effort
  based on the PBS project. It provides control over batch jobs and distributed compute nodes. Torque is
  freely available for download from <a href="http://www.clusterresources.com/downloads/torque/">here</a>.
  </p>
<p>  All documentation related to torque can be seen under
  the section TORQUE Resource Manager <a href="http://www.clusterresources.com/pages/resources/documentation.php">here</a>. You can
  get wiki documentation from <a href="http://www.clusterresources.com/wiki/doku.php?id=torque:torque_wiki">here</a>.
  Users may wish to subscribe to TORQUE&rsquo;s mailing list or view the archive for questions,
  comments <a href="http://www.clusterresources.com/pages/resources/mailing-lists.php">here</a>.
</p>
<p>For using HOD with Torque:</p>
<ul>
 
<li>Install Torque components: pbs_server on one node(head node), pbs_mom on all
  compute nodes, and PBS client tools on all compute nodes and submit
  nodes. Perform atleast a basic configuration so that the Torque system is up and
  running i.e pbs_server knows which machines to talk to. Look <a href="http://www.clusterresources.com/wiki/doku.php?id=torque:1.2_basic_configuration">here</a>
  for basic configuration.

  For advanced configuration, see <a href="http://www.clusterresources.com/wiki/doku.php?id=torque:1.3_advanced_configuration">here</a>
</li>
 
<li>Create a queue for submitting jobs on the pbs_server. The name of the queue is the
  same as the HOD configuration parameter, resource-manager.queue. The Hod client uses this queue to
  submit the Ringmaster process as a Torque job.</li>
 
<li>Specify a 'cluster name' as a 'property' for all nodes in the cluster.
  This can be done by using the 'qmgr' command. For example:
  qmgr -c "set node node properties=cluster-name". The name of the cluster is the same as
  the HOD configuration parameter, hod.cluster. </li>
 
<li>Ensure that jobs can be submitted to the nodes. This can be done by
  using the 'qsub' command. For example:
  echo "sleep 30" | qsub -l nodes=3</li>

</ul>
</div>


<a name="N100C4"></a><a name="Installing+HOD"></a>
<h2 class="h3">Installing HOD</h2>
<div class="section">
<p>Now that the resource manager set up is done, we proceed on to obtaining and
installing HOD.</p>
<ul>
 
<li>If you are getting HOD from the Hadoop tarball,it is available under the 
  'contrib' section of Hadoop, under the root  directory 'hod'.</li>
 
<li>If you are building from source, you can run ant tar from the Hadoop root
  directory, to generate the Hadoop tarball, and then pick HOD from there,
  as described in the point above.</li>
 
<li>Distribute the files under this directory to all the nodes in the
  cluster. Note that the location where the files are copied should be
  the same on all the nodes.</li>
  
<li>Note that compiling hadoop would build HOD with appropriate permissions 
  set on all the required script files in HOD.</li>

</ul>
</div>


<a name="N100DD"></a><a name="Configuring+HOD"></a>
<h2 class="h3">Configuring HOD</h2>
<div class="section">
<p>After HOD installation is done, it has to be configured before we start using
it.</p>
<a name="N100E6"></a><a name="Minimal+Configuration+to+get+started"></a>
<h3 class="h4">Minimal Configuration to get started</h3>
<ul>
 
<li>On the node from where you want to run hod, edit the file hodrc
  which can be found in the &lt;install dir&gt;/conf directory. This file
  contains the minimal set of values required for running hod.</li>
 
<li>

<p>Specify values suitable to your environment for the following
  variables defined in the configuration file. Note that some of these
  variables are defined at more than one place in the file.</p>

  
<ul>
   
<li>${JAVA_HOME}: Location of Java for Hadoop. Hadoop supports Sun JDK
    1.5.x and above.</li>
   
<li>${CLUSTER_NAME}: Name of the cluster which is specified in the
    'node property' as mentioned in resource manager configuration.</li>
   
<li>${HADOOP_HOME}: Location of Hadoop installation on the compute and
    submit nodes.</li>
   
<li>${RM_QUEUE}: Queue configured for submiting jobs in the resource
    manager configuration.</li>
   
<li>${RM_HOME}: Location of the resource manager installation on the
    compute and submit nodes.</li>
    
</ul>

</li>


<li>

<p>The following environment variables *may* need to be set depending on
  your environment. These variables must be defined where you run the
  HOD client, and also be specified in the HOD configuration file as the
  value of the key resource_manager.env-vars. Multiple variables can be
  specified as a comma separated list of key=value pairs.</p>

  
<ul>
   
<li>HOD_PYTHON_HOME: If you install python to a non-default location
    of the compute nodes, or submit nodes, then, this variable must be
    defined to point to the python executable in the non-standard
    location.</li>
    
</ul>

</li>

</ul>
<a name="N10117"></a><a name="Advanced+Configuration"></a>
<h3 class="h4">Advanced Configuration</h3>
<p> You can review other configuration options in the file and modify them to suit
 your needs. Refer to the <a href="hod_config_guide.html">Configuration Guide</a> for information about the HOD
 configuration.
    </p>
</div>

  
<a name="N10126"></a><a name="Running+HOD"></a>
<h2 class="h3">Running HOD</h2>
<div class="section">
<p>You can now proceed to <a href="hod_user_guide.html">HOD User Guide</a> for information about how to run HOD,
    what are the various features, options and for help in trouble-shooting.</p>
</div>

  
<a name="N10134"></a><a name="Supporting+Tools+and+Utilities"></a>
<h2 class="h3">Supporting Tools and Utilities</h2>
<div class="section">
<p>This section describes certain supporting tools and utilities that can be used in managing HOD deployments.</p>
<a name="N1013D"></a><a name="logcondense.py+-+Tool+for+removing+log+files+uploaded+to+DFS"></a>
<h3 class="h4">logcondense.py - Tool for removing log files uploaded to DFS</h3>
<p>As mentioned in 
         <a href="hod_user_guide.html#Collecting+and+Viewing+Hadoop+Logs">this section</a> of the
         <a href="hod_user_guide.html">HOD User Guide</a>, HOD can be configured to upload
         Hadoop logs to a statically configured HDFS. Over time, the number of logs uploaded
         to DFS could increase. logcondense.py is a tool that helps administrators to clean-up
         the log files older than a certain number of days. </p>
<a name="N1014E"></a><a name="Running+logcondense.py"></a>
<h4>Running logcondense.py</h4>
<p>logcondense.py is available under hod_install_location/support folder. You can either
        run it using python, for e.g. <em>python logcondense.py</em>, or give execute permissions 
        to the file, and directly run it as <em>logcondense.py</em>. logcondense.py needs to be 
        run by a user who has sufficient permissions to remove files from locations where log 
        files are uploaded in the DFS, if permissions are enabled. For e.g. as mentioned in the
        <a href="hod_config_guide.html#3.7+hodring+options">configuration guide</a>, the logs could
        be configured to come under the user's home directory in HDFS. In that case, the user
        running logcondense.py should have super user privileges to remove the files from under
        all user home directories.</p>
<a name="N10162"></a><a name="Command+Line+Options+for+logcondense.py"></a>
<h4>Command Line Options for logcondense.py</h4>
<p>The following command line options are supported for logcondense.py.</p>
<table class="ForrestTable" cellspacing="1" cellpadding="4">
            
<tr>
              
<td colspan="1" rowspan="1">Short Option</td>
              <td colspan="1" rowspan="1">Long option</td>
              <td colspan="1" rowspan="1">Meaning</td>
              <td colspan="1" rowspan="1">Example</td>
            
</tr>
            
<tr>
              
<td colspan="1" rowspan="1">-p</td>
              <td colspan="1" rowspan="1">--package</td>
              <td colspan="1" rowspan="1">Complete path to the hadoop script. The version of hadoop must be the same as the 
                  one running HDFS.</td>
              <td colspan="1" rowspan="1">/usr/bin/hadoop</td>
            
</tr>
            
<tr>
              
<td colspan="1" rowspan="1">-d</td>
              <td colspan="1" rowspan="1">--days</td>
              <td colspan="1" rowspan="1">Delete log files older than the specified number of days</td>
              <td colspan="1" rowspan="1">7</td>
            
</tr>
            
<tr>
              
<td colspan="1" rowspan="1">-c</td>
              <td colspan="1" rowspan="1">--config</td>
              <td colspan="1" rowspan="1">Path to the Hadoop configuration directory, under which hadoop-site.xml resides.
              The hadoop-site.xml must point to the HDFS NameNode from where logs are to be removed.</td>
              <td colspan="1" rowspan="1">/home/foo/hadoop/conf</td>
            
</tr>
            
<tr>
              
<td colspan="1" rowspan="1">-l</td>
              <td colspan="1" rowspan="1">--logs</td>
              <td colspan="1" rowspan="1">A HDFS path, this must be the same HDFS path as specified for the log-destination-uri,
              as mentioned in the  <a href="hod_config_guide.html#3.7+hodring+options">configuration guide</a>,
              without the hdfs:// URI string</td>
              <td colspan="1" rowspan="1">/user</td>
            
</tr>
            
<tr>
              
<td colspan="1" rowspan="1">-n</td>
              <td colspan="1" rowspan="1">--dynamicdfs</td>
              <td colspan="1" rowspan="1">If true, this will indicate that the logcondense.py script should delete HDFS logs
              in addition to Map/Reduce logs. Otherwise, it only deletes Map/Reduce logs, which is also the
              default if this option is not specified. This option is useful if dynamic DFS installations 
              are being provisioned by HOD, and the static DFS installation is being used only to collect 
              logs - a scenario that may be common in test clusters.</td>
              <td colspan="1" rowspan="1">false</td>
            
</tr>
          
</table>
<p>So, for example, to delete all log files older than 7 days using a hadoop-site.xml stored in
        ~/hadoop-conf, using the hadoop installation under ~/hadoop-0.17.0, you could say:</p>
<p>
<em>python logcondense.py -p ~/hadoop-0.17.0/bin/hadoop -d 7 -c ~/hadoop-conf -l /user</em>
</p>
</div>

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