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| author | Patrick Wendell <pwendell@apache.org> | 2014-09-11 05:00:26 +0000 |
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| committer | Patrick Wendell <pwendell@apache.org> | 2014-09-11 05:00:26 +0000 |
| commit | 07461d1269cd6d373630c20fb50c2988af5c21f4 (patch) | |
| tree | 04c4f9a3cbe613b3d3c79a8581e6b83babbc3e0b /site/docs/1.1.0/running-on-mesos.html | |
| parent | 46d52fbb9be4b5b90a7a1ee9ce3e943156d190b9 (diff) | |
| download | spark-website-07461d1269cd6d373630c20fb50c2988af5c21f4.tar.gz spark-website-07461d1269cd6d373630c20fb50c2988af5c21f4.tar.bz2 spark-website-07461d1269cd6d373630c20fb50c2988af5c21f4.zip | |
Adding Spark 1.1.0 docs.
Diffstat (limited to 'site/docs/1.1.0/running-on-mesos.html')
| -rw-r--r-- | site/docs/1.1.0/running-on-mesos.html | 379 |
1 files changed, 379 insertions, 0 deletions
diff --git a/site/docs/1.1.0/running-on-mesos.html b/site/docs/1.1.0/running-on-mesos.html new file mode 100644 index 000000000..8f8816289 --- /dev/null +++ b/site/docs/1.1.0/running-on-mesos.html @@ -0,0 +1,379 @@ +<!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>Running Spark on Mesos - Spark 1.1.0 Documentation</title> + <meta name="description" content=""> + + + + <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-32518208-1']); + _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">1.1.0</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">Spark 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> + </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">Scaladoc</a></li> + <li><a href="api/java/index.html">Javadoc</a></li> + <li><a href="api/python/index.html">Python API</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="ec2-scripts.html">Amazon EC2</a></li> + <li><a href="spark-standalone.html">Standalone Mode</a></li> + <li><a href="running-on-mesos.html">Mesos</a></li> + <li><a href="running-on-yarn.html">YARN</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-with-maven.html">Building Spark with Maven</a></li> + <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">Contributing to Spark</a></li> + </ul> + </li> + </ul> + <!--<p class="navbar-text pull-right"><span class="version-text">v1.1.0</span></p>--> + </div> + </div> + </div> + + <div class="container" id="content"> + + <h1 class="title">Running Spark on Mesos</h1> + + + <p>Spark can run on hardware clusters managed by <a href="http://mesos.apache.org/">Apache Mesos</a>.</p> + +<p>The advantages of deploying Spark with Mesos include:</p> + +<ul> + <li>dynamic partitioning between Spark and other +<a href="https://mesos.apache.org/documentation/latest/mesos-frameworks/">frameworks</a></li> + <li>scalable partitioning between multiple instances of Spark</li> +</ul> + +<h1 id="how-it-works">How it Works</h1> + +<p>In a standalone cluster deployment, the cluster manager in the below diagram is a Spark master +instance. When using Mesos, the Mesos master replaces the Spark master as the cluster manager.</p> + +<p style="text-align: center;"> + <img src="img/cluster-overview.png" title="Spark cluster components" alt="Spark cluster components" /> +</p> + +<p>Now when a driver creates a job and starts issuing tasks for scheduling, Mesos determines what +machines handle what tasks. Because it takes into account other frameworks when scheduling these +many short-lived tasks, multiple frameworks can coexist on the same cluster without resorting to a +static partitioning of resources.</p> + +<p>To get started, follow the steps below to install Mesos and deploy Spark jobs via Mesos.</p> + +<h1 id="installing-mesos">Installing Mesos</h1> + +<p>Spark 1.1.0-SNAPSHOT is designed for use with Mesos 0.18.1 and does not +require any special patches of Mesos.</p> + +<p>If you already have a Mesos cluster running, you can skip this Mesos installation step.</p> + +<p>Otherwise, installing Mesos for Spark is no different than installing Mesos for use by other +frameworks. You can install Mesos either from source or using prebuilt packages.</p> + +<h2 id="from-source">From Source</h2> + +<p>To install Apache Mesos from source, follow these steps:</p> + +<ol> + <li>Download a Mesos release from a +<a href="http://www.apache.org/dyn/closer.cgi/mesos/0.18.1/">mirror</a></li> + <li>Follow the Mesos <a href="http://mesos.apache.org/gettingstarted">Getting Started</a> page for compiling and +installing Mesos</li> +</ol> + +<p><strong>Note:</strong> If you want to run Mesos without installing it into the default paths on your system +(e.g., if you lack administrative privileges to install it), pass the +<code>--prefix</code> option to <code>configure</code> to tell it where to install. For example, pass +<code>--prefix=/home/me/mesos</code>. By default the prefix is <code>/usr/local</code>.</p> + +<h2 id="third-party-packages">Third-Party Packages</h2> + +<p>The Apache Mesos project only publishes source releases, not binary packages. But other +third party projects publish binary releases that may be helpful in setting Mesos up.</p> + +<p>One of those is Mesosphere. To install Mesos using the binary releases provided by Mesosphere:</p> + +<ol> + <li>Download Mesos installation package from <a href="http://mesosphere.io/downloads/">downloads page</a></li> + <li>Follow their instructions for installation and configuration</li> +</ol> + +<p>The Mesosphere installation documents suggest setting up ZooKeeper to handle Mesos master failover, +but Mesos can be run without ZooKeeper using a single master as well.</p> + +<h2 id="verification">Verification</h2> + +<p>To verify that the Mesos cluster is ready for Spark, navigate to the Mesos master webui at port +<code>:5050</code> Confirm that all expected machines are present in the slaves tab.</p> + +<h1 id="connecting-spark-to-mesos">Connecting Spark to Mesos</h1> + +<p>To use Mesos from Spark, you need a Spark binary package available in a place accessible by Mesos, and +a Spark driver program configured to connect to Mesos.</p> + +<h2 id="uploading-spark-package">Uploading Spark Package</h2> + +<p>When Mesos runs a task on a Mesos slave for the first time, that slave must have a Spark binary +package for running the Spark Mesos executor backend. +The Spark package can be hosted at any Hadoop-accessible URI, including HTTP via <code>http://</code>, +<a href="http://aws.amazon.com/s3">Amazon Simple Storage Service</a> via <code>s3n://</code>, or HDFS via <code>hdfs://</code>.</p> + +<p>To use a precompiled package:</p> + +<ol> + <li>Download a Spark binary package from the Spark <a href="https://spark.apache.org/downloads.html">download page</a></li> + <li>Upload to hdfs/http/s3</li> +</ol> + +<p>To host on HDFS, use the Hadoop fs put command: <code>hadoop fs -put spark-1.1.0-SNAPSHOT.tar.gz +/path/to/spark-1.1.0-SNAPSHOT.tar.gz</code></p> + +<p>Or if you are using a custom-compiled version of Spark, you will need to create a package using +the <code>make-distribution.sh</code> script included in a Spark source tarball/checkout.</p> + +<ol> + <li>Download and build Spark using the instructions <a href="index.html">here</a></li> + <li>Create a binary package using <code>make-distribution.sh --tgz</code>.</li> + <li>Upload archive to http/s3/hdfs</li> +</ol> + +<h2 id="using-a-mesos-master-url">Using a Mesos Master URL</h2> + +<p>The Master URLs for Mesos are in the form <code>mesos://host:5050</code> for a single-master Mesos +cluster, or <code>mesos://zk://host:2181</code> for a multi-master Mesos cluster using ZooKeeper.</p> + +<p>The driver also needs some configuration in <code>spark-env.sh</code> to interact properly with Mesos:</p> + +<ol> + <li>In <code>spark-env.sh</code> set some environment variables: + <ul> + <li><code>export MESOS_NATIVE_LIBRARY=<path to libmesos.so></code>. This path is typically +<code><prefix>/lib/libmesos.so</code> where the prefix is <code>/usr/local</code> by default. See Mesos installation +instructions above. On Mac OS X, the library is called <code>libmesos.dylib</code> instead of +<code>libmesos.so</code>.</li> + <li><code>export SPARK_EXECUTOR_URI=<URL of spark-1.1.0-SNAPSHOT.tar.gz uploaded above></code>.</li> + </ul> + </li> + <li>Also set <code>spark.executor.uri</code> to <code><URL of spark-1.1.0-SNAPSHOT.tar.gz></code>.</li> +</ol> + +<p>Now when starting a Spark application against the cluster, pass a <code>mesos://</code> +URL as the master when creating a <code>SparkContext</code>. For example:</p> + +<div class="highlight"><pre><code class="scala"><span class="k">val</span> <span class="n">conf</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkConf</span><span class="o">()</span> + <span class="o">.</span><span class="n">setMaster</span><span class="o">(</span><span class="s">"mesos://HOST:5050"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setAppName</span><span class="o">(</span><span class="s">"My app"</span><span class="o">)</span> + <span class="o">.</span><span class="n">set</span><span class="o">(</span><span class="s">"spark.executor.uri"</span><span class="o">,</span> <span class="s">"<path to spark-1.1.0-SNAPSHOT.tar.gz uploaded above>"</span><span class="o">)</span> +<span class="k">val</span> <span class="n">sc</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">)</span> +</code></pre></div> + +<p>(You can also use <a href="submitting-applications.html"><code>spark-submit</code></a> and configure <code>spark.executor.uri</code> +in the <a href="configuration.html#loading-default-configurations">conf/spark-defaults.conf</a> file. Note +that <code>spark-submit</code> currently only supports deploying the Spark driver in <code>client</code> mode for Mesos.)</p> + +<p>When running a shell, the <code>spark.executor.uri</code> parameter is inherited from <code>SPARK_EXECUTOR_URI</code>, so +it does not need to be redundantly passed in as a system property.</p> + +<div class="highlight"><pre><code class="bash">./bin/spark-shell --master mesos://host:5050 +</code></pre></div> + +<h1 id="mesos-run-modes">Mesos Run Modes</h1> + +<p>Spark can run over Mesos in two modes: “fine-grained” (default) and “coarse-grained”.</p> + +<p>In “fine-grained” mode (default), each Spark task runs as a separate Mesos task. This allows +multiple instances of Spark (and other frameworks) to share machines at a very fine granularity, +where each application gets more or fewer machines as it ramps up and down, but it comes with an +additional overhead in launching each task. This mode may be inappropriate for low-latency +requirements like interactive queries or serving web requests.</p> + +<p>The “coarse-grained” mode will instead launch only <em>one</em> long-running Spark task on each Mesos +machine, and dynamically schedule its own “mini-tasks” within it. The benefit is much lower startup +overhead, but at the cost of reserving the Mesos resources for the complete duration of the +application.</p> + +<p>To run in coarse-grained mode, set the <code>spark.mesos.coarse</code> property in your +<a href="configuration.html#spark-properties">SparkConf</a>:</p> + +<div class="highlight"><pre><code class="scala"><span class="n">conf</span><span class="o">.</span><span class="n">set</span><span class="o">(</span><span class="s">"spark.mesos.coarse"</span><span class="o">,</span> <span class="s">"true"</span><span class="o">)</span> +</code></pre></div> + +<p>In addition, for coarse-grained mode, you can control the maximum number of resources Spark will +acquire. By default, it will acquire <em>all</em> cores in the cluster (that get offered by Mesos), which +only makes sense if you run just one application at a time. You can cap the maximum number of cores +using <code>conf.set("spark.cores.max", "10")</code> (for example).</p> + +<h1 id="running-alongside-hadoop">Running Alongside Hadoop</h1> + +<p>You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them as a +separate service on the machines. To access Hadoop data from Spark, a full <code>hdfs://</code> URL is required +(typically <code>hdfs://<namenode>:9000/path</code>, but you can find the right URL on your Hadoop Namenode web +UI).</p> + +<p>In addition, it is possible to also run Hadoop MapReduce on Mesos for better resource isolation and +sharing between the two. In this case, Mesos will act as a unified scheduler that assigns cores to +either Hadoop or Spark, as opposed to having them share resources via the Linux scheduler on each +node. Please refer to <a href="https://github.com/mesos/hadoop">Hadoop on Mesos</a>.</p> + +<p>In either case, HDFS runs separately from Hadoop MapReduce, without being scheduled through Mesos.</p> + +<h1 id="troubleshooting-and-debugging">Troubleshooting and Debugging</h1> + +<p>A few places to look during debugging:</p> + +<ul> + <li>Mesos master on port <code>:5050</code> + <ul> + <li>Slaves should appear in the slaves tab</li> + <li>Spark applications should appear in the frameworks tab</li> + <li>Tasks should appear in the details of a framework</li> + <li>Check the stdout and stderr of the sandbox of failed tasks</li> + </ul> + </li> + <li>Mesos logs + <ul> + <li>Master and slave logs are both in <code>/var/log/mesos</code> by default</li> + </ul> + </li> +</ul> + +<p>And common pitfalls:</p> + +<ul> + <li>Spark assembly not reachable/accessible + <ul> + <li>Slaves must be able to download the Spark binary package from the <code>http://</code>, <code>hdfs://</code> or <code>s3n://</code> URL you gave</li> + </ul> + </li> + <li>Firewall blocking communications + <ul> + <li>Check for messages about failed connections</li> + <li>Temporarily disable firewalls for debugging and then poke appropriate holes</li> + </ul> + </li> +</ul> + + + </div> <!-- /container --> + + <script src="js/vendor/jquery-1.8.0.min.js"></script> + <script src="js/vendor/bootstrap.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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