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| author | Patrick Wendell <pwendell@apache.org> | 2015-07-15 04:17:01 +0000 |
|---|---|---|
| committer | Patrick Wendell <pwendell@apache.org> | 2015-07-15 04:17:01 +0000 |
| commit | 8c2ee8d4aa3eb7fdc66536019d86793e1db2b135 (patch) | |
| tree | 07beeb27472248193736185dfa7567fc078426fa /site/docs/1.4.1/hardware-provisioning.html | |
| parent | 7378439da1198903bbb855218de3d050c611a057 (diff) | |
| download | spark-website-8c2ee8d4aa3eb7fdc66536019d86793e1db2b135.tar.gz spark-website-8c2ee8d4aa3eb7fdc66536019d86793e1db2b135.tar.bz2 spark-website-8c2ee8d4aa3eb7fdc66536019d86793e1db2b135.zip | |
Spark 1.4.1 docs
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| -rw-r--r-- | site/docs/1.4.1/hardware-provisioning.html | 236 |
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diff --git a/site/docs/1.4.1/hardware-provisioning.html b/site/docs/1.4.1/hardware-provisioning.html new file mode 100644 index 000000000..60a472e90 --- /dev/null +++ b/site/docs/1.4.1/hardware-provisioning.html @@ -0,0 +1,236 @@ +<!DOCTYPE html> +<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]--> +<!--[if IE 7]> <html class="no-js lt-ie9 lt-ie8"> <![endif]--> +<!--[if IE 8]> <html class="no-js lt-ie9"> <![endif]--> +<!--[if gt IE 8]><!--> <html class="no-js"> <!--<![endif]--> + <head> + <meta charset="utf-8"> + <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"> + <title>Hardware Provisioning - Spark 1.4.1 Documentation</title> + + + + + <link rel="stylesheet" href="css/bootstrap.min.css"> + <style> + body { + padding-top: 60px; + padding-bottom: 40px; + } + </style> + <meta name="viewport" content="width=device-width"> + <link rel="stylesheet" href="css/bootstrap-responsive.min.css"> + <link rel="stylesheet" href="css/main.css"> + + <script src="js/vendor/modernizr-2.6.1-respond-1.1.0.min.js"></script> + + <link rel="stylesheet" href="css/pygments-default.css"> + + + <!-- Google analytics script --> + <script type="text/javascript"> + var _gaq = _gaq || []; + _gaq.push(['_setAccount', 'UA-32518208-2']); + _gaq.push(['_trackPageview']); + + (function() { + var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; + ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; + var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); + })(); + </script> + + + </head> + <body> + <!--[if lt IE 7]> + <p class="chromeframe">You are using an outdated browser. <a href="http://browsehappy.com/">Upgrade your browser today</a> or <a href="http://www.google.com/chromeframe/?redirect=true">install Google Chrome Frame</a> to better experience this site.</p> + <![endif]--> + + <!-- This code is taken from http://twitter.github.com/bootstrap/examples/hero.html --> + + <div class="navbar navbar-fixed-top" id="topbar"> + <div class="navbar-inner"> + <div class="container"> + <div class="brand"><a href="index.html"> + <img src="img/spark-logo-hd.png" style="height:50px;"/></a><span class="version">1.4.1</span> + </div> + <ul class="nav"> + <!--TODO(andyk): Add class="active" attribute to li some how.--> + <li><a href="index.html">Overview</a></li> + + <li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown">Programming Guides<b class="caret"></b></a> + <ul class="dropdown-menu"> + <li><a href="quick-start.html">Quick Start</a></li> + <li><a href="programming-guide.html">Spark Programming Guide</a></li> + <li class="divider"></li> + <li><a href="streaming-programming-guide.html">Spark Streaming</a></li> + <li><a href="sql-programming-guide.html">DataFrames and SQL</a></li> + <li><a href="mllib-guide.html">MLlib (Machine Learning)</a></li> + <li><a href="graphx-programming-guide.html">GraphX (Graph Processing)</a></li> + <li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li> + <li><a href="sparkr.html">SparkR (R on Spark)</a></li> + </ul> + </li> + + <li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown">API Docs<b class="caret"></b></a> + <ul class="dropdown-menu"> + <li><a href="api/scala/index.html#org.apache.spark.package">Scala</a></li> + <li><a href="api/java/index.html">Java</a></li> + <li><a href="api/python/index.html">Python</a></li> + <li><a href="api/R/index.html">R</a></li> + </ul> + </li> + + <li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown">Deploying<b class="caret"></b></a> + <ul class="dropdown-menu"> + <li><a href="cluster-overview.html">Overview</a></li> + <li><a href="submitting-applications.html">Submitting Applications</a></li> + <li class="divider"></li> + <li><a href="spark-standalone.html">Spark Standalone</a></li> + <li><a href="running-on-mesos.html">Mesos</a></li> + <li><a href="running-on-yarn.html">YARN</a></li> + <li class="divider"></li> + <li><a href="ec2-scripts.html">Amazon EC2</a></li> + </ul> + </li> + + <li class="dropdown"> + <a href="api.html" class="dropdown-toggle" data-toggle="dropdown">More<b class="caret"></b></a> + <ul class="dropdown-menu"> + <li><a href="configuration.html">Configuration</a></li> + <li><a href="monitoring.html">Monitoring</a></li> + <li><a href="tuning.html">Tuning Guide</a></li> + <li><a href="job-scheduling.html">Job Scheduling</a></li> + <li><a href="security.html">Security</a></li> + <li><a href="hardware-provisioning.html">Hardware Provisioning</a></li> + <li><a href="hadoop-third-party-distributions.html">3<sup>rd</sup>-Party Hadoop Distros</a></li> + <li class="divider"></li> + <li><a href="building-spark.html">Building Spark</a></li> + <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">Contributing to Spark</a></li> + <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Supplemental+Spark+Projects">Supplemental Projects</a></li> + </ul> + </li> + </ul> + <!--<p class="navbar-text pull-right"><span class="version-text">v1.4.1</span></p>--> + </div> + </div> + </div> + + <div class="container" id="content"> + + <h1 class="title">Hardware Provisioning</h1> + + + <p>A common question received by Spark developers is how to configure hardware for it. While the right +hardware will depend on the situation, we make the following recommendations.</p> + +<h1 id="storage-systems">Storage Systems</h1> + +<p>Because most Spark jobs will likely have to read input data from an external storage system (e.g. +the Hadoop File System, or HBase), it is important to place it <strong>as close to this system as +possible</strong>. We recommend the following:</p> + +<ul> + <li> + <p>If at all possible, run Spark on the same nodes as HDFS. The simplest way is to set up a Spark +<a href="spark-standalone.html">standalone mode cluster</a> on the same nodes, and configure Spark and +Hadoop’s memory and CPU usage to avoid interference (for Hadoop, the relevant options are +<code>mapred.child.java.opts</code> for the per-task memory and <code>mapred.tasktracker.map.tasks.maximum</code> +and <code>mapred.tasktracker.reduce.tasks.maximum</code> for number of tasks). Alternatively, you can run +Hadoop and Spark on a common cluster manager like <a href="running-on-mesos.html">Mesos</a> or +<a href="running-on-yarn.html">Hadoop YARN</a>.</p> + </li> + <li> + <p>If this is not possible, run Spark on different nodes in the same local-area network as HDFS.</p> + </li> + <li> + <p>For low-latency data stores like HBase, it may be preferrable to run computing jobs on different +nodes than the storage system to avoid interference.</p> + </li> +</ul> + +<h1 id="local-disks">Local Disks</h1> + +<p>While Spark can perform a lot of its computation in memory, it still uses local disks to store +data that doesn’t fit in RAM, as well as to preserve intermediate output between stages. We +recommend having <strong>4-8 disks</strong> per node, configured <em>without</em> RAID (just as separate mount points). +In Linux, mount the disks with the <a href="http://www.centos.org/docs/5/html/Global_File_System/s2-manage-mountnoatime.html"><code>noatime</code> option</a> +to reduce unnecessary writes. In Spark, <a href="configuration.html">configure</a> the <code>spark.local.dir</code> +variable to be a comma-separated list of the local disks. If you are running HDFS, it’s fine to +use the same disks as HDFS.</p> + +<h1 id="memory">Memory</h1> + +<p>In general, Spark can run well with anywhere from <strong>8 GB to hundreds of gigabytes</strong> of memory per +machine. In all cases, we recommend allocating only at most 75% of the memory for Spark; leave the +rest for the operating system and buffer cache.</p> + +<p>How much memory you will need will depend on your application. To determine how much your +application uses for a certain dataset size, load part of your dataset in a Spark RDD and use the +Storage tab of Spark’s monitoring UI (<code>http://<driver-node>:4040</code>) to see its size in memory. +Note that memory usage is greatly affected by storage level and serialization format – see +the <a href="tuning.html">tuning guide</a> for tips on how to reduce it.</p> + +<p>Finally, note that the Java VM does not always behave well with more than 200 GB of RAM. If you +purchase machines with more RAM than this, you can run <em>multiple worker JVMs per node</em>. In +Spark’s <a href="spark-standalone.html">standalone mode</a>, you can set the number of workers per node +with the <code>SPARK_WORKER_INSTANCES</code> variable in <code>conf/spark-env.sh</code>, and the number of cores +per worker with <code>SPARK_WORKER_CORES</code>.</p> + +<h1 id="network">Network</h1> + +<p>In our experience, when the data is in memory, a lot of Spark applications are network-bound. +Using a <strong>10 Gigabit</strong> or higher network is the best way to make these applications faster. +This is especially true for “distributed reduce” applications such as group-bys, reduce-bys, and +SQL joins. In any given application, you can see how much data Spark shuffles across the network +from the application’s monitoring UI (<code>http://<driver-node>:4040</code>).</p> + +<h1 id="cpu-cores">CPU Cores</h1> + +<p>Spark scales well to tens of CPU cores per machine because it performes minimal sharing between +threads. You should likely provision at least <strong>8-16 cores</strong> per machine. Depending on the CPU +cost of your workload, you may also need more: once data is in memory, most applications are +either CPU- or network-bound.</p> + + + </div> <!-- /container --> + + <script src="js/vendor/jquery-1.8.0.min.js"></script> + <script src="js/vendor/bootstrap.min.js"></script> + <script src="js/vendor/anchor.min.js"></script> + <script src="js/main.js"></script> + + <!-- MathJax Section --> + <script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + TeX: { equationNumbers: { autoNumber: "AMS" } } + }); + </script> + <script> + // Note that we load MathJax this way to work with local file (file://), HTTP and HTTPS. + // We could use "//cdn.mathjax...", but that won't support "file://". + (function(d, script) { + script = d.createElement('script'); + script.type = 'text/javascript'; + script.async = true; + script.onload = function(){ + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ["$", "$"], ["\\\\(","\\\\)"] ], + displayMath: [ ["$$","$$"], ["\\[", "\\]"] ], + processEscapes: true, + skipTags: ['script', 'noscript', 'style', 'textarea', 'pre'] + } + }); + }; + script.src = ('https:' == document.location.protocol ? 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