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the License. You may obtain a copy of the License at
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<head>
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<title>Hadoop</title>
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</head>
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<body>
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Hadoop is a distributed computing platform.
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2016-06-28 12:53:03 +00:00
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<p>Hadoop primarily consists of the <a
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href="http://hadoop.apache.org/hdfs/">Hadoop Distributed FileSystem
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(HDFS)</a> and an
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implementation of the <a href="http://hadoop.apache.org/mapreduce/">
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Map-Reduce</a> programming paradigm.</p>
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<p>Hadoop is a software framework that lets one easily write and run applications
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that process vast amounts of data. Here's what makes Hadoop especially useful:</p>
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<ul>
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<li>
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<b>Scalable</b>: Hadoop can reliably store and process petabytes.
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</li>
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<li>
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<b>Economical</b>: It distributes the data and processing across clusters
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of commonly available computers. These clusters can number into the thousands
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of nodes.
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</li>
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<li>
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<b>Efficient</b>: By distributing the data, Hadoop can process it in parallel
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on the nodes where the data is located. This makes it extremely rapid.
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</li>
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<li>
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<b>Reliable</b>: Hadoop automatically maintains multiple copies of data and
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automatically redeploys computing tasks based on failures.
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</li>
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</ul>
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<h2>Requirements</h2>
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<h3>Platforms</h3>
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<ul>
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<li>
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Hadoop has been demonstrated on GNU/Linux clusters with more than 4000 nodes.
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</li>
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<li>
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HADOOP-8952. Enhancements to support Hadoop on Windows Server and Windows Azure environments. Contributed by Ivan Mitic, Chuan Liu, Ramya Sunil, Bikas Saha, Kanna Karanam, John Gordon, Brandon Li, Chris Nauroth, David Lao, Sumadhur Reddy Bolli, Arpit Agarwal, Ahmed El Baz, Mike Liddell, Jing Zhao, Thejas Nair, Steve Maine, Ganeshan Iyer, Raja Aluri, Giridharan Kesavan, Ramya Bharathi Nimmagadda.
git-svn-id: https://svn.apache.org/repos/asf/hadoop/common/trunk@1453486 13f79535-47bb-0310-9956-ffa450edef68
2013-03-06 19:15:18 +00:00
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Windows is also a supported platform.
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</li>
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</ul>
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<h3>Requisite Software</h3>
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<ol>
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<li>
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Java 1.6.x, preferably from
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<a href="http://java.sun.com/javase/downloads/">Sun</a>.
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Set <tt>JAVA_HOME</tt> to the root of your Java installation.
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</li>
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<li>
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ssh must be installed and sshd must be running to use Hadoop's
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scripts to manage remote Hadoop daemons.
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</li>
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<li>
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rsync may be installed to use Hadoop's scripts to manage remote
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Hadoop installations.
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</li>
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</ol>
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<h3>Installing Required Software</h3>
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<p>If your platform does not have the required software listed above, you
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will have to install it.</p>
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<p>For example on Ubuntu Linux:</p>
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<p><blockquote><pre>
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$ sudo apt-get install ssh<br>
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$ sudo apt-get install rsync<br>
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</pre></blockquote></p>
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<h2>Getting Started</h2>
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<p>First, you need to get a copy of the Hadoop code.</p>
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<p>Edit the file <tt>conf/hadoop-env.sh</tt> to define at least
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<tt>JAVA_HOME</tt>.</p>
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<p>Try the following command:</p>
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<tt>bin/hadoop</tt>
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<p>This will display the documentation for the Hadoop command script.</p>
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<h2>Standalone operation</h2>
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<p>By default, Hadoop is configured to run things in a non-distributed
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mode, as a single Java process. This is useful for debugging, and can
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be demonstrated as follows:</p>
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<tt>
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mkdir input<br>
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cp conf/*.xml input<br>
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bin/hadoop jar hadoop-*-examples.jar grep input output 'dfs[a-z.]+'<br>
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cat output/*
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</tt>
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<p>This will display counts for each match of the <a
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href="http://java.sun.com/j2se/1.4.2/docs/api/java/util/regex/Pattern.html">
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regular expression.</a></p>
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<p>Note that input is specified as a <em>directory</em> containing input
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files and that output is also specified as a directory where parts are
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written.</p>
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<h2>Distributed operation</h2>
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To configure Hadoop for distributed operation you must specify the
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following:
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<ol>
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<li>The NameNode (Distributed Filesystem master) host. This is
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specified with the configuration property <tt><a
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href="../core-default.html#fs.default.name">fs.default.name</a></tt>.
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</li>
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<li>The org.apache.hadoop.mapred.JobTracker (MapReduce master)
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host and port. This is specified with the configuration property
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<tt><a
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href="../mapred-default.html#mapred.job.tracker">mapred.job.tracker</a></tt>.
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</li>
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<li>A <em>workers</em> file that lists the names of all the hosts in
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the cluster. The default workers file is <tt>conf/workers</tt>.
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</ol>
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<h3>Pseudo-distributed configuration</h3>
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You can in fact run everything on a single host. To run things this
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way, put the following in:
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<br/>
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<br/>
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conf/core-site.xml:
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<xmp><configuration>
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<property>
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<name>fs.default.name</name>
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<value>hdfs://localhost/</value>
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</property>
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</configuration></xmp>
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conf/hdfs-site.xml:
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<xmp><configuration>
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<property>
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<name>dfs.replication</name>
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<value>1</value>
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</property>
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</configuration></xmp>
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conf/mapred-site.xml:
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<xmp><configuration>
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<property>
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<name>mapred.job.tracker</name>
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<value>localhost:9001</value>
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</property>
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</configuration></xmp>
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<p>(We also set the HDFS replication level to 1 in order to
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reduce warnings when running on a single node.)</p>
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<p>Now check that the command <br><tt>ssh localhost</tt><br> does not
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require a password. If it does, execute the following commands:</p>
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<p><tt>ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa<br>
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cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys
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</tt></p>
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<h3>Bootstrapping</h3>
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<p>A new distributed filesystem must be formatted with the following
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command, run on the master node:</p>
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<p><tt>bin/hadoop namenode -format</tt></p>
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<p>The Hadoop daemons are started with the following command:</p>
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<p><tt>bin/start-all.sh</tt></p>
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<p>Daemon log output is written to the <tt>logs/</tt> directory.</p>
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<p>Input files are copied into the distributed filesystem as follows:</p>
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<p><tt>bin/hadoop fs -put input input</tt></p>
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<h3>Distributed execution</h3>
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<p>Things are run as before, but output must be copied locally to
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examine it:</p>
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<tt>
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bin/hadoop jar hadoop-*-examples.jar grep input output 'dfs[a-z.]+'<br>
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bin/hadoop fs -get output output
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cat output/*
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</tt>
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<p>When you're done, stop the daemons with:</p>
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<p><tt>bin/stop-all.sh</tt></p>
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<h3>Fully-distributed operation</h3>
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<p>Fully distributed operation is just like the pseudo-distributed operation
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described above, except, specify:</p>
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<ol>
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<li>The hostname or IP address of your master server in the value
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for <tt><a
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href="../core-default.html#fs.default.name">fs.default.name</a></tt>,
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as <tt><em>hdfs://master.example.com/</em></tt> in <tt>conf/core-site.xml</tt>.</li>
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<li>The host and port of the your master server in the value
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of <tt><a href="../mapred-default.html#mapred.job.tracker">mapred.job.tracker</a></tt>
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as <tt><em>master.example.com</em>:<em>port</em></tt> in <tt>conf/mapred-site.xml</tt>.</li>
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<li>Directories for <tt><a
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href="../hdfs-default.html#dfs.name.dir">dfs.name.dir</a></tt> and
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<tt><a href="../hdfs-default.html#dfs.data.dir">dfs.data.dir</a>
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in <tt>conf/hdfs-site.xml</tt>.
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</tt>These are local directories used to hold distributed filesystem
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data on the master node and worker nodes respectively. Note
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that <tt>dfs.data.dir</tt> may contain a space- or comma-separated
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list of directory names, so that data may be stored on multiple local
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devices.</li>
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<li><tt><a href="../mapred-default.html#mapred.local.dir">mapred.local.dir</a></tt>
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in <tt>conf/mapred-site.xml</tt>, the local directory where temporary
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MapReduce data is stored. It also may be a list of directories.</li>
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<li><tt><a
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href="../mapred-default.html#mapred.map.tasks">mapred.map.tasks</a></tt>
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and <tt><a
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href="../mapred-default.html#mapred.reduce.tasks">mapred.reduce.tasks</a></tt>
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in <tt>conf/mapred-site.xml</tt>.
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As a rule of thumb, use 10x the
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number of worker processors for <tt>mapred.map.tasks</tt>, and 2x the
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number of worker processors for <tt>mapred.reduce.tasks</tt>.</li>
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</ol>
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<p>Finally, list all worker hostnames or IP addresses in your
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<tt>conf/workers</tt> file, one per line. Then format your filesystem
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and start your cluster on your master node, as above.
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</body>
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</html>
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