apache
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hadoop
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<head>
   <title>Hadoop</title>
</head>
<body>

Hadoop is a distributed computing platform.

<p>Hadoop primarily consists of the <a 
href="org/apache/hadoop/hdfs/package-summary.html">Hadoop Distributed FileSystem 
(HDFS)</a> and an 
implementation of the <a href="org/apache/hadoop/mapred/package-summary.html">
Map-Reduce</a> programming paradigm.</p>


<p>Hadoop is a software framework that lets one easily write and run applications 
that process vast amounts of data. Here's what makes Hadoop especially useful:</p>
<ul>
  <li>
    <b>Scalable</b>: Hadoop can reliably store and process petabytes.
  </li>
  <li>
    <b>Economical</b>: It distributes the data and processing across clusters 
    of commonly available computers. These clusters can number into the thousands 
    of nodes.
  </li>
  <li>
    <b>Efficient</b>: By distributing the data, Hadoop can process it in parallel 
    on the nodes where the data is located. This makes it extremely rapid.
  </li>
  <li>
    <b>Reliable</b>: Hadoop automatically maintains multiple copies of data and 
    automatically redeploys computing tasks based on failures.
  </li>
</ul>  

<h2>Requirements</h2>

<h3>Platforms</h3>

<ul>
  <li>
    Hadoop was been demonstrated on GNU/Linux clusters with 2000 nodes.
  </li>
  <li>
    Win32 is supported as a <i>development</i> platform. Distributed operation 
    has not been well tested on Win32, so this is not a <i>production</i> 
    platform.
  </li>  
</ul>
  
<h3>Requisite Software</h3>

<ol>
  <li>
    Java 1.6.x, preferably from 
    <a href="http://java.sun.com/javase/downloads/">Sun</a>. 
    Set <tt>JAVA_HOME</tt> to the root of your Java installation.
  </li>
  <li>
    ssh must be installed and sshd must be running to use Hadoop's
    scripts to manage remote Hadoop daemons.
  </li>
  <li>
    rsync may be installed to use Hadoop's scripts to manage remote
    Hadoop installations.
  </li>
</ol>

<h4>Additional requirements for Windows</h4>

<ol>
  <li>
    <a href="http://www.cygwin.com/">Cygwin</a> - Required for shell support in 
    addition to the required software above.
  </li>
</ol>
  
<h3>Installing Required Software</h3>

<p>If your platform does not have the required software listed above, you
will have to install it.</p>

<p>For example on Ubuntu Linux:</p>
<p><blockquote><pre>
$ sudo apt-get install ssh<br>
$ sudo apt-get install rsync<br>
</pre></blockquote></p>

<p>On Windows, if you did not install the required software when you
installed cygwin, start the cygwin installer and select the packages:</p>
<ul>
  <li>openssh - the "Net" category</li>
  <li>rsync - the "Net" category</li>
</ul>

<h2>Getting Started</h2>

<p>First, you need to get a copy of the Hadoop code.</p>

<p>Edit the file <tt>conf/hadoop-env.sh</tt> to define at least
<tt>JAVA_HOME</tt>.</p>

<p>Try the following command:</p>
<tt>bin/hadoop</tt>
<p>This will display the documentation for the Hadoop command script.</p>

<h2>Standalone operation</h2>

<p>By default, Hadoop is configured to run things in a non-distributed
mode, as a single Java process.  This is useful for debugging, and can
be demonstrated as follows:</p>
<tt>
mkdir input<br>
cp conf/*.xml input<br>
bin/hadoop jar hadoop-*-examples.jar grep input output 'dfs[a-z.]+'<br>
cat output/*
</tt>
<p>This will display counts for each match of the <a
href="http://java.sun.com/j2se/1.4.2/docs/api/java/util/regex/Pattern.html">
regular expression.</a></p>

<p>Note that input is specified as a <em>directory</em> containing input
files and that output is also specified as a directory where parts are
written.</p>

<h2>Distributed operation</h2>

To configure Hadoop for distributed operation you must specify the
following:

<ol>

<li>The NameNode (Distributed Filesystem master) host.  This is
specified with the configuration property <tt><a
 href="../core-default.html#fs.default.name">fs.default.name</a></tt>.
</li>

<li>The {@link org.apache.hadoop.mapred.JobTracker} (MapReduce master)
host and port.  This is specified with the configuration property
<tt><a
href="../mapred-default.html#mapreduce.jobtracker.address">mapreduce.jobtracker.address</a></tt>.
</li>

<li>A <em>slaves</em> file that lists the names of all the hosts in
the cluster.  The default slaves file is <tt>conf/slaves</tt>.

</ol>

<h3>Pseudo-distributed configuration</h3>

You can in fact run everything on a single host.  To run things this
way, put the following in:
<br/>
<br/>
conf/core-site.xml:
<xmp><configuration>

  <property>
    <name>fs.default.name</name>
    <value>hdfs://localhost/</value>
  </property>

</configuration></xmp>

conf/hdfs-site.xml:
<xmp><configuration>

  <property>
    <name>dfs.replication</name>
    <value>1</value>
  </property>

</configuration></xmp>

conf/mapred-site.xml:
<xmp><configuration>

  <property>
    <name>mapreduce.jobtracker.address</name>
    <value>localhost:9001</value>
  </property>

</configuration></xmp>

<p>(We also set the HDFS replication level to 1 in order to
reduce warnings when running on a single node.)</p>

<p>Now check that the command <br><tt>ssh localhost</tt><br> does not
require a password.  If it does, execute the following commands:</p>

<p><tt>ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa<br>
cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys
</tt></p>

<h3>Bootstrapping</h3>

<p>A new distributed filesystem must be formatted with the following
command, run on the master node:</p>

<p><tt>bin/hadoop namenode -format</tt></p>

<p>The Hadoop daemons are started with the following command:</p>

<p><tt>bin/start-all.sh</tt></p>

<p>Daemon log output is written to the <tt>logs/</tt> directory.</p>

<p>Input files are copied into the distributed filesystem as follows:</p>

<p><tt>bin/hadoop fs -put input input</tt></p>

<h3>Distributed execution</h3>

<p>Things are run as before, but output must be copied locally to
examine it:</p>

<tt>
bin/hadoop jar hadoop-*-examples.jar grep input output 'dfs[a-z.]+'<br>
bin/hadoop fs -get output output
cat output/*
</tt>

<p>When you're done, stop the daemons with:</p>

<p><tt>bin/stop-all.sh</tt></p>

<h3>Fully-distributed operation</h3>

<p>Fully distributed operation is just like the pseudo-distributed operation
described above, except, specify:</p>

<ol>

<li>The hostname or IP address of your master server in the value
for <tt><a
href="../core-default.html#fs.default.name">fs.default.name</a></tt>,
  as <tt><em>hdfs://master.example.com/</em></tt> in <tt>conf/core-site.xml</tt>.</li>

<li>The host and port of the your master server in the value
of <tt><a href="../mapred-default.html#mapreduce.jobtracker.address">mapreduce.jobtracker.address</a></tt>
as <tt><em>master.example.com</em>:<em>port</em></tt> in <tt>conf/mapred-site.xml</tt>.</li>

<li>Directories for <tt><a
href="../hdfs-default.html#dfs.name.dir">dfs.name.dir</a></tt> and
<tt><a href="../hdfs-default.html#dfs.data.dir">dfs.data.dir</a> 
in <tt>conf/hdfs-site.xml</tt>.
</tt>These are local directories used to hold distributed filesystem
data on the master node and slave nodes respectively.  Note
that <tt>dfs.data.dir</tt> may contain a space- or comma-separated
list of directory names, so that data may be stored on multiple local
devices.</li>

<li><tt><a href="../mapred-default.html#mapreduce.cluster.local.dir">mapreduce.cluster.local.dir</a></tt>
  in <tt>conf/mapred-site.xml</tt>, the local directory where temporary 
  MapReduce data is stored.  It also may be a list of directories.</li>

<li><tt><a
href="../mapred-default.html#mapreduce.job.maps">mapreduce.job.maps</a></tt>
and <tt><a
href="../mapred-default.html#mapreduce.job.reduces">mapreduce.job.reduces</a></tt> 
in <tt>conf/mapred-site.xml</tt>.
As a rule of thumb, use 10x the
number of slave processors for <tt>mapreduce.job.maps</tt>, and 2x the
number of slave processors for <tt>mapreduce.job.reduces</tt>.</li>

</ol>

<p>Finally, list all slave hostnames or IP addresses in your
<tt>conf/slaves</tt> file, one per line.  Then format your filesystem
and start your cluster on your master node, as above.

</body>
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