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<h1>Hadoop Cluster Setup</h1>
<div id="minitoc-area">
<ul class="minitoc">
<li>
<a href="#Purpose">Purpose</a>
</li>
<li>
<a href="#Pre-requisites">Pre-requisites</a>
</li>
<li>
<a href="#Installation">Installation</a>
</li>
<li>
<a href="#Configuration">Configuration</a>
<ul class="minitoc">
<li>
<a href="#Configuration+Files">Configuration Files</a>
</li>
<li>
<a href="#Site+Configuration">Site Configuration</a>
<ul class="minitoc">
<li>
<a href="#Configuring+the+Environment+of+the+Hadoop+Daemons">Configuring the Environment of the Hadoop Daemons</a>
</li>
<li>
<a href="#Configuring+the+Hadoop+Daemons">Configuring the Hadoop Daemons</a>
</li>
<li>
<a href="#Slaves">Slaves</a>
</li>
<li>
<a href="#Logging">Logging</a>
</li>
</ul>
</li>
</ul>
</li>
<li>
<a href="#Hadoop+Rack+Awareness">Hadoop Rack Awareness</a>
</li>
<li>
<a href="#Hadoop+Startup">Hadoop Startup</a>
</li>
<li>
<a href="#Hadoop+Shutdown">Hadoop Shutdown</a>
</li>
</ul>
</div>
  
    
<a name="N1000D"></a><a name="Purpose"></a>
<h2 class="h3">Purpose</h2>
<div class="section">
<p>This document describes how to install, configure and manage non-trivial
      Hadoop clusters ranging from a few nodes to extremely large clusters with 
      thousands of nodes.</p>
<p>If you are looking to install Hadoop on a single machine to play
      with it, you can find relevant details <a href="quickstart.html">here</a>.
      </p>
</div>
    
    
<a name="N1001E"></a><a name="Pre-requisites"></a>
<h2 class="h3">Pre-requisites</h2>
<div class="section">
<ol>
        
<li>
          Make sure all <a href="quickstart.html#PreReqs">requisite</a> software 
          is installed on all nodes in your cluster.
        </li>
        
<li>
          
<a href="quickstart.html#Download">Get</a> the Hadoop software.
        </li>
      
</ol>
</div>
    
    
<a name="N10036"></a><a name="Installation"></a>
<h2 class="h3">Installation</h2>
<div class="section">
<p>Installing a Hadoop cluster typically involves unpacking the software 
      on all the machines in the cluster.</p>
<p>Typically one machine in the cluster is designated as the 
      <span class="codefrag">NameNode</span> and another machine the as <span class="codefrag">JobTracker</span>,
      exclusively. These are the <em>masters</em>. The rest of the machines in 
      the cluster act as both <span class="codefrag">DataNode</span> <em>and</em> 
      <span class="codefrag">TaskTracker</span>. These are the <em>slaves</em>.</p>
<p>The root of the distribution is referred to as 
      <span class="codefrag">HADOOP_HOME</span>. All machines in the cluster usually have the same 
      <span class="codefrag">HADOOP_HOME</span> path.</p>
</div>
    
    
<a name="N10061"></a><a name="Configuration"></a>
<h2 class="h3">Configuration</h2>
<div class="section">
<p>The following sections describe how to configure a Hadoop cluster.</p>
<a name="N1006A"></a><a name="Configuration+Files"></a>
<h3 class="h4">Configuration Files</h3>
<p>Hadoop configuration is driven by two important configuration files
        found in the <span class="codefrag">conf/</span> directory of the distribution:</p>
<ol>
          
<li>
            
<a href="http://hadoop.apache.org/core/docs/current/hadoop-default.html">hadoop-default.xml</a> - Read-only 
            default configuration.
          </li>
          
<li>
            
<em>hadoop-site.xml</em> - Site-specific configuration.
          </li>
        
</ol>
<p>To learn more about how the Hadoop framework is controlled by these 
        configuration files, look 
        <a href="api/org/apache/hadoop/conf/Configuration.html">here</a>.</p>
<p>Additionally, you can control the Hadoop scripts found in the 
        <span class="codefrag">bin/</span> directory of the distribution, by setting site-specific 
        values via the <span class="codefrag">conf/hadoop-env.sh</span>.</p>
<a name="N10097"></a><a name="Site+Configuration"></a>
<h3 class="h4">Site Configuration</h3>
<p>To configure the the Hadoop cluster you will need to configure the
        <em>environment</em> in which the Hadoop daemons execute as well as
        the <em>configuration parameters</em> for the Hadoop daemons.</p>
<p>The Hadoop daemons are <span class="codefrag">NameNode</span>/<span class="codefrag">DataNode</span> 
        and <span class="codefrag">JobTracker</span>/<span class="codefrag">TaskTracker</span>.</p>
<a name="N100B5"></a><a name="Configuring+the+Environment+of+the+Hadoop+Daemons"></a>
<h4>Configuring the Environment of the Hadoop Daemons</h4>
<p>Administrators should use the <span class="codefrag">conf/hadoop-env.sh</span> script
          to do site-specific customization of the Hadoop daemons' process 
          environment.</p>
<p>At the very least you should specify the
          <span class="codefrag">JAVA_HOME</span> so that it is correctly defined on each
          remote node.</p>
<p>Other useful configuration parameters that you can customize 
          include:</p>
<ul>
            
<li>
              
<span class="codefrag">HADOOP_LOG_DIR</span> - The directory where the daemons'
              log files are stored. They are automatically created if they don't
              exist.
            </li>
            
<li>
              
<span class="codefrag">HADOOP_HEAPSIZE</span> - The maximum amount of heapsize 
              to use, in MB e.g. <span class="codefrag">2000MB</span>.
            </li>
          
</ul>
<a name="N100DD"></a><a name="Configuring+the+Hadoop+Daemons"></a>
<h4>Configuring the Hadoop Daemons</h4>
<p>This section deals with important parameters to be specified in the
          <span class="codefrag">conf/hadoop-site.xml</span> for the Hadoop cluster.</p>
<table class="ForrestTable" cellspacing="1" cellpadding="4">
  		    
<tr>
		      
<th colspan="1" rowspan="1">Parameter</th>
		      <th colspan="1" rowspan="1">Value</th> 
		      <th colspan="1" rowspan="1">Notes</th>
		    
</tr>
  		    
<tr>
		      
<td colspan="1" rowspan="1">fs.default.name</td>
  		      <td colspan="1" rowspan="1">Hostname or IP address of <span class="codefrag">NameNode</span>.</td>
		      <td colspan="1" rowspan="1"><em>host:port</em> pair.</td>
		    
</tr>
		    
<tr>
		      
<td colspan="1" rowspan="1">mapred.job.tracker</td>
		      <td colspan="1" rowspan="1">Hostname or IP address of <span class="codefrag">JobTracker</span>.</td>
		      <td colspan="1" rowspan="1"><em>host:port</em> pair.</td>
		    
</tr>
		    
<tr>
		      
<td colspan="1" rowspan="1">dfs.name.dir</td>
		      <td colspan="1" rowspan="1">
		        Path on the local filesystem where the <span class="codefrag">NameNode</span> 
		        stores the namespace and transactions logs persistently.</td>
		      <td colspan="1" rowspan="1">
		        If this is a comma-delimited list of directories then the name 
		        table is replicated in all of the directories, for redundancy.
		      </td>
		    
</tr>
		    
<tr>
		      
<td colspan="1" rowspan="1">dfs.data.dir</td>
		      <td colspan="1" rowspan="1">
		        Comma separated list of paths on the local filesystem of a 
		        <span class="codefrag">DataNode</span> where it should store its blocks.
		      </td>
		      <td colspan="1" rowspan="1">
		        If this is a comma-delimited list of directories, then data will 
		        be stored in all named directories, typically on different 
		        devices.
		      </td>
		    
</tr>
		    
<tr>
		      
<td colspan="1" rowspan="1">mapred.system.dir</td>
		      <td colspan="1" rowspan="1">
		        Path on the HDFS where where the Map-Reduce framework stores 
		        system files e.g. <span class="codefrag">/hadoop/mapred/system/</span>.
		      </td>
		      <td colspan="1" rowspan="1">
		        This is in the default filesystem (HDFS) and must be accessible 
		        from both the server and client machines.
		      </td>
		    
</tr>
		    
<tr>
		      
<td colspan="1" rowspan="1">mapred.local.dir</td>
		      <td colspan="1" rowspan="1">
		        Comma-separated list of paths on the local filesystem where 
		        temporary Map-Reduce data is written.
		      </td>
		      <td colspan="1" rowspan="1">Multiple paths help spread disk i/o.</td>
		    
</tr>
		    
<tr>
		      
<td colspan="1" rowspan="1">mapred.tasktracker.{map|reduce}.tasks.maximum</td>
		      <td colspan="1" rowspan="1">
		        The maximum number of map/reduce tasks, which are run 
		        simultaneously on a given <span class="codefrag">TaskTracker</span>, individually.
		      </td>
		      <td colspan="1" rowspan="1">
		        Defaults to 2 (2 maps and 2 reduces), but vary it depending on 
		        your hardware.
		      </td>
		    
</tr>
		    
<tr>
		      
<td colspan="1" rowspan="1">dfs.hosts/dfs.hosts.exclude</td>
		      <td colspan="1" rowspan="1">List of permitted/excluded DataNodes.</td>
		      <td colspan="1" rowspan="1">
		        If necessary, use these files to control the list of allowable 
		        datanodes.
		      </td>
		    
</tr>
		    
<tr>
		      
<td colspan="1" rowspan="1">mapred.hosts/mapred.hosts.exclude</td>
		      <td colspan="1" rowspan="1">List of permitted/excluded TaskTrackers.</td>
		      <td colspan="1" rowspan="1">
		        If necessary, use these files to control the list of allowable 
		        tasktrackers.
		      </td>
  		    
</tr>
		  
</table>
<p>Typically all the above parameters are marked as 
          <a href="api/org/apache/hadoop/conf/Configuration.html#FinalParams">
          final</a> to ensure that they cannot be overriden by user-applications.
          </p>
<a name="N101BD"></a><a name="Real-World+Cluster+Configurations"></a>
<h5>Real-World Cluster Configurations</h5>
<p>This section lists some non-default configuration parameters which 
            have been used to run the <em>sort</em> benchmark on very large 
            clusters.</p>
<ul>
              
<li>
                
<p>Some non-default configuration values used to run sort900,
                that is 9TB of data sorted on a cluster with 900 nodes:</p>
                
<table class="ForrestTable" cellspacing="1" cellpadding="4">
  		          
<tr>
		            
<th colspan="1" rowspan="1">Parameter</th>
		            <th colspan="1" rowspan="1">Value</th> 
		            <th colspan="1" rowspan="1">Notes</th>
		          
</tr>
                  
<tr>
                    
<td colspan="1" rowspan="1">dfs.block.size</td>
                    <td colspan="1" rowspan="1">134217728</td>
                    <td colspan="1" rowspan="1">HDFS blocksize of 128MB for large file-systems.</td>
                  
</tr>
                  
<tr>
                    
<td colspan="1" rowspan="1">dfs.namenode.handler.count</td>
                    <td colspan="1" rowspan="1">40</td>
                    <td colspan="1" rowspan="1">
                      More NameNode server threads to handle RPCs from large 
                      number of DataNodes.
                    </td>
                  
</tr>
                  
<tr>
                    
<td colspan="1" rowspan="1">mapred.reduce.parallel.copies</td>
                    <td colspan="1" rowspan="1">20</td>
                    <td colspan="1" rowspan="1">
                      Higher number of parallel copies run by reduces to fetch
                      outputs from very large number of maps.
                    </td>
                  
</tr>
                  
<tr>
                    
<td colspan="1" rowspan="1">mapred.child.java.opts</td>
                    <td colspan="1" rowspan="1">-Xmx512M</td>
                    <td colspan="1" rowspan="1">
                      Larger heap-size for child jvms of maps/reduces. Also controls the amount 
                      of virtual memory that a streaming/pipes task gets.
                    </td>
                  
</tr>
                  
<tr>
                    
<td colspan="1" rowspan="1">fs.inmemory.size.mb</td>
                    <td colspan="1" rowspan="1">200</td>
                    <td colspan="1" rowspan="1">
                      Larger amount of memory allocated for the in-memory 
                      file-system used to merge map-outputs at the reduces.
                    </td>
                  
</tr>
                  
<tr>
                    
<td colspan="1" rowspan="1">io.sort.factor</td>
                    <td colspan="1" rowspan="1">100</td>
                    <td colspan="1" rowspan="1">More streams merged at once while sorting files.</td>
                  
</tr>
                  
<tr>
                    
<td colspan="1" rowspan="1">io.sort.mb</td>
                    <td colspan="1" rowspan="1">200</td>
                    <td colspan="1" rowspan="1">Higher memory-limit while sorting data.</td>
                  
</tr>
                  
<tr>
                    
<td colspan="1" rowspan="1">io.file.buffer.size</td>
                    <td colspan="1" rowspan="1">131072</td>
                    <td colspan="1" rowspan="1">Size of read/write buffer used in SequenceFiles.</td>
                  
</tr>
                
</table>
              
</li>
              
<li>
                
<p>Updates to some configuration values to run sort1400 and 
                sort2000, that is 14TB of data sorted on 1400 nodes and 20TB of
                data sorted on 2000 nodes:</p>
                
<table class="ForrestTable" cellspacing="1" cellpadding="4">
  		          
<tr>
		            
<th colspan="1" rowspan="1">Parameter</th>
		            <th colspan="1" rowspan="1">Value</th> 
		            <th colspan="1" rowspan="1">Notes</th>
		          
</tr>
                  
<tr>
                    
<td colspan="1" rowspan="1">mapred.job.tracker.handler.count</td>
                    <td colspan="1" rowspan="1">60</td>
                    <td colspan="1" rowspan="1">
                      More JobTracker server threads to handle RPCs from large 
                      number of TaskTrackers.
                    </td>
                  
</tr>
                  
<tr>
                    
<td colspan="1" rowspan="1">mapred.reduce.parallel.copies</td>
                    <td colspan="1" rowspan="1">50</td>
                    <td colspan="1" rowspan="1"></td>
                  
</tr>
                  
<tr>
                    
<td colspan="1" rowspan="1">tasktracker.http.threads</td>
                    <td colspan="1" rowspan="1">50</td>
                    <td colspan="1" rowspan="1">
                      More worker threads for the TaskTracker's http server. The
                      http server is used by reduces to fetch intermediate 
                      map-outputs.
                    </td>
                  
</tr>
                  
<tr>
                    
<td colspan="1" rowspan="1">mapred.child.java.opts</td>
                    <td colspan="1" rowspan="1">-Xmx1024M</td>
                    <td colspan="1" rowspan="1"></td>
                  
</tr>
                
</table>
              
</li>
            
</ul>
<a name="N102DA"></a><a name="Slaves"></a>
<h4>Slaves</h4>
<p>Typically you choose one machine in the cluster to act as the 
          <span class="codefrag">NameNode</span> and one machine as to act as the 
          <span class="codefrag">JobTracker</span>, exclusively. The rest of the machines act as 
          both a <span class="codefrag">DataNode</span> and <span class="codefrag">TaskTracker</span> and are 
          referred to as <em>slaves</em>.</p>
<p>List all slave hostnames or IP addresses in your 
          <span class="codefrag">conf/slaves</span> file, one per line.</p>
<a name="N102F9"></a><a name="Logging"></a>
<h4>Logging</h4>
<p>Hadoop uses the <a href="http://logging.apache.org/log4j/">Apache 
          log4j</a> via the <a href="http://commons.apache.org/logging/">Apache 
          Commons Logging</a> framework for logging. Edit the 
          <span class="codefrag">conf/log4j.properties</span> file to customize the Hadoop 
          daemons' logging configuration (log-formats and so on).</p>
<a name="N1030D"></a><a name="History+Logging"></a>
<h5>History Logging</h5>
<p> The job history files are stored in central location 
            <span class="codefrag"> hadoop.job.history.location </span> which can be on DFS also,
            whose default value is <span class="codefrag">${HADOOP_LOG_DIR}/history</span>. 
            The history web UI is accessible from job tracker web UI.</p>
<p> The history files are also logged to user specified directory
            <span class="codefrag">hadoop.job.history.user.location</span> 
            which defaults to job output directory. The files are stored in
            "_logs/history/" in the specified directory. Hence, by default 
            they will be in "mapred.output.dir/_logs/history/". User can stop
            logging by giving the value <span class="codefrag">none</span> for 
            <span class="codefrag">hadoop.job.history.user.location</span> 
</p>
<p> User can view the history logs summary in specified directory 
            using the following command <br>
            
<span class="codefrag">$ bin/hadoop job -history output-dir</span>
<br> 
            This command will print job details, failed and killed tip
            details. <br>
            More details about the job such as successful tasks and 
            task attempts made for each task can be viewed using the  
            following command <br>
            
<span class="codefrag">$ bin/hadoop job -history all output-dir</span>
<br>
</p>
<p>Once all the necessary configuration is complete, distribute the files
      to the <span class="codefrag">HADOOP_CONF_DIR</span> directory on all the machines, 
      typically <span class="codefrag">${HADOOP_HOME}/conf</span>.</p>
</div>
    
    
<a name="N10345"></a><a name="Hadoop+Rack+Awareness"></a>
<h2 class="h3">Hadoop Rack Awareness</h2>
<div class="section">
<p>The HDFS and the Map-Reduce components are rack-aware.</p>
<p>The <span class="codefrag">NameNode</span> and the <span class="codefrag">JobTracker</span> obtains the
      <span class="codefrag">rack id</span> of the slaves in the cluster by invoking an API 
      <a href="api/org/apache/hadoop/net/DNSToSwitchMapping.html#resolve(java.util.List)">resolve</a> in an administrator configured
      module. The API resolves the slave's DNS name (also IP address) to a 
      rack id. What module to use can be configured using the configuration
      item <span class="codefrag">topology.node.switch.mapping.impl</span>. The default 
      implementation of the same runs a script/command configured using 
      <span class="codefrag">topology.script.file.name</span>. If topology.script.file.name is
      not set, the rack id <span class="codefrag">/default-rack</span> is returned for any 
      passed IP address. The additional configuration in the Map-Reduce
      part is <span class="codefrag">mapred.cache.task.levels</span> which determines the number
      of levels (in the network topology) of caches. So, for example, if it is
      the default value of 2, two levels of caches will be constructed - 
      one for hosts (host -&gt; task mapping) and another for racks 
      (rack -&gt; task mapping).
      </p>
</div>
    
    
<a name="N1036B"></a><a name="Hadoop+Startup"></a>
<h2 class="h3">Hadoop Startup</h2>
<div class="section">
<p>To start a Hadoop cluster you will need to start both the HDFS and 
      Map-Reduce cluster.</p>
<p>
        Format a new distributed filesystem:<br>
        
<span class="codefrag">$ bin/hadoop namenode -format</span>
      
</p>
<p>
        Start the HDFS with the following command, run on the designated
        <span class="codefrag">NameNode</span>:<br>
        
<span class="codefrag">$ bin/start-dfs.sh</span>
      
</p>
<p>The <span class="codefrag">bin/start-dfs.sh</span> script also consults the 
      <span class="codefrag">${HADOOP_CONF_DIR}/slaves</span> file on the <span class="codefrag">NameNode</span> 
      and starts the <span class="codefrag">DataNode</span> daemon on all the listed slaves.</p>
<p>
        Start Map-Reduce with the following command, run on the designated
        <span class="codefrag">JobTracker</span>:<br>
        
<span class="codefrag">$ bin/start-mapred.sh</span>
      
</p>
<p>The <span class="codefrag">bin/start-mapred.sh</span> script also consults the 
      <span class="codefrag">${HADOOP_CONF_DIR}/slaves</span> file on the <span class="codefrag">JobTracker</span> 
      and starts the <span class="codefrag">TaskTracker</span> daemon on all the listed slaves.
      </p>
</div>
    
    
<a name="N103B1"></a><a name="Hadoop+Shutdown"></a>
<h2 class="h3">Hadoop Shutdown</h2>
<div class="section">
<p>
        Stop HDFS with the following command, run on the designated 
        <span class="codefrag">NameNode</span>:<br>
        
<span class="codefrag">$ bin/stop-dfs.sh</span>
      
</p>
<p>The <span class="codefrag">bin/stop-dfs.sh</span> script also consults the 
      <span class="codefrag">${HADOOP_CONF_DIR}/slaves</span> file on the <span class="codefrag">NameNode</span> 
      and stops the <span class="codefrag">DataNode</span> daemon on all the listed slaves.</p>
<p>
        Stop Map-Reduce with the following command, run on the designated
        the designated <span class="codefrag">JobTracker</span>:<br>
        
<span class="codefrag">$ bin/stop-mapred.sh</span>
<br>
      
</p>
<p>The <span class="codefrag">bin/stop-mapred.sh</span> script also consults the 
      <span class="codefrag">${HADOOP_CONF_DIR}/slaves</span> file on the <span class="codefrag">JobTracker</span> 
      and stops the <span class="codefrag">TaskTracker</span> daemon on all the listed slaves.</p>
</div>
  
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