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| author | Patrick Wendell <pwendell@apache.org> | 2014-07-11 17:23:23 +0000 |
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| committer | Patrick Wendell <pwendell@apache.org> | 2014-07-11 17:23:23 +0000 |
| commit | 0beac4e243f85e71554fe04093b09eb1745fea82 (patch) | |
| tree | bc20d10426c5d57e2f189305865dc2bbec447923 /site/docs/1.0.1/job-scheduling.html | |
| parent | ddec2123ba6ab95543d1b250d4f20fb811c48f09 (diff) | |
| download | spark-website-0beac4e243f85e71554fe04093b09eb1745fea82.tar.gz spark-website-0beac4e243f85e71554fe04093b09eb1745fea82.tar.bz2 spark-website-0beac4e243f85e71554fe04093b09eb1745fea82.zip | |
Updating docs for 1.0.1 release
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diff --git a/site/docs/1.0.1/job-scheduling.html b/site/docs/1.0.1/job-scheduling.html new file mode 100644 index 000000000..974678343 --- /dev/null +++ b/site/docs/1.0.1/job-scheduling.html @@ -0,0 +1,332 @@ +<!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>Job Scheduling - Spark 1.0.1 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 ? 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First, recall that, as described +in the <a href="cluster-overview.html">cluster mode overview</a>, each Spark application (instance of SparkContext) +runs an independent set of executor processes. The cluster managers that Spark runs on provide +facilities for <a href="#scheduling-across-applications">scheduling across applications</a>. Second, +<em>within</em> each Spark application, multiple “jobs” (Spark actions) may be running concurrently +if they were submitted by different threads. This is common if your application is serving requests +over the network; for example, the <a href="http://shark.cs.berkeley.edu">Shark</a> server works this way. Spark +includes a <a href="#scheduling-within-an-application">fair scheduler</a> to schedule resources within each SparkContext.</p> + +<h1 id="scheduling-across-applications">Scheduling Across Applications</h1> + +<p>When running on a cluster, each Spark application gets an independent set of executor JVMs that only +run tasks and store data for that application. If multiple users need to share your cluster, there are +different options to manage allocation, depending on the cluster manager.</p> + +<p>The simplest option, available on all cluster managers, is <em>static partitioning</em> of resources. With +this approach, each application is given a maximum amount of resources it can use, and holds onto them +for its whole duration. This is the approach used in Spark’s <a href="spark-standalone.html">standalone</a> +and <a href="running-on-yarn.html">YARN</a> modes, as well as the +<a href="running-on-mesos.html#mesos-run-modes">coarse-grained Mesos mode</a>. +Resource allocation can be configured as follows, based on the cluster type:</p> + +<ul> + <li><strong>Standalone mode:</strong> By default, applications submitted to the standalone mode cluster will run in +FIFO (first-in-first-out) order, and each application will try to use all available nodes. You can limit +the number of nodes an application uses by setting the <code>spark.cores.max</code> configuration property in it, +or change the default for applications that don’t set this setting through <code>spark.deploy.defaultCores</code>. +Finally, in addition to controlling cores, each application’s <code>spark.executor.memory</code> setting controls +its memory use.</li> + <li><strong>Mesos:</strong> To use static partitioning on Mesos, set the <code>spark.mesos.coarse</code> configuration property to <code>true</code>, +and optionally set <code>spark.cores.max</code> to limit each application’s resource share as in the standalone mode. +You should also set <code>spark.executor.memory</code> to control the executor memory.</li> + <li><strong>YARN:</strong> The <code>--num-executors</code> option to the Spark YARN client controls how many executors it will allocate +on the cluster, while <code>--executor-memory</code> and <code>--executor-cores</code> control the resources per executor.</li> +</ul> + +<p>A second option available on Mesos is <em>dynamic sharing</em> of CPU cores. In this mode, each Spark application +still has a fixed and independent memory allocation (set by <code>spark.executor.memory</code>), but when the +application is not running tasks on a machine, other applications may run tasks on those cores. This mode +is useful when you expect large numbers of not overly active applications, such as shell sessions from +separate users. However, it comes with a risk of less predictable latency, because it may take a while for +an application to gain back cores on one node when it has work to do. To use this mode, simply use a +<code>mesos://</code> URL without setting <code>spark.mesos.coarse</code> to true.</p> + +<p>Note that none of the modes currently provide memory sharing across applications. If you would like to share +data this way, we recommend running a single server application that can serve multiple requests by querying +the same RDDs. For example, the <a href="http://shark.cs.berkeley.edu">Shark</a> JDBC server works this way for SQL +queries. In future releases, in-memory storage systems such as <a href="http://tachyon-project.org">Tachyon</a> will +provide another approach to share RDDs.</p> + +<h1 id="scheduling-within-an-application">Scheduling Within an Application</h1> + +<p>Inside a given Spark application (SparkContext instance), multiple parallel jobs can run simultaneously if +they were submitted from separate threads. By “job”, in this section, we mean a Spark action (e.g. <code>save</code>, +<code>collect</code>) and any tasks that need to run to evaluate that action. Spark’s scheduler is fully thread-safe +and supports this use case to enable applications that serve multiple requests (e.g. queries for +multiple users).</p> + +<p>By default, Spark’s scheduler runs jobs in FIFO fashion. Each job is divided into “stages” (e.g. map and +reduce phases), and the first job gets priority on all available resources while its stages have tasks to +launch, then the second job gets priority, etc. If the jobs at the head of the queue don’t need to use +the whole cluster, later jobs can start to run right away, but if the jobs at the head of the queue are +large, then later jobs may be delayed significantly.</p> + +<p>Starting in Spark 0.8, it is also possible to configure fair sharing between jobs. Under fair sharing, +Spark assigns tasks between jobs in a “round robin” fashion, so that all jobs get a roughly equal share +of cluster resources. This means that short jobs submitted while a long job is running can start receiving +resources right away and still get good response times, without waiting for the long job to finish. This +mode is best for multi-user settings.</p> + +<p>To enable the fair scheduler, simply set the <code>spark.scheduler.mode</code> property to <code>FAIR</code> when configuring +a SparkContext:</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="n">setMaster</span><span class="o">(...).</span><span class="n">setAppName</span><span class="o">(...)</span> +<span class="n">conf</span><span class="o">.</span><span class="n">set</span><span class="o">(</span><span class="s">"spark.scheduler.mode"</span><span class="o">,</span> <span class="s">"FAIR"</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> + +<h2 id="fair-scheduler-pools">Fair Scheduler Pools</h2> + +<p>The fair scheduler also supports grouping jobs into <em>pools</em>, and setting different scheduling options +(e.g. weight) for each pool. This can be useful to create a “high-priority” pool for more important jobs, +for example, or to group the jobs of each user together and give <em>users</em> equal shares regardless of how +many concurrent jobs they have instead of giving <em>jobs</em> equal shares. This approach is modeled after the +<a href="http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/FairScheduler.html">Hadoop Fair Scheduler</a>.</p> + +<p>Without any intervention, newly submitted jobs go into a <em>default pool</em>, but jobs’ pools can be set by +adding the <code>spark.scheduler.pool</code> “local property” to the SparkContext in the thread that’s submitting them. +This is done as follows:</p> + +<div class="highlight"><pre><code class="scala"><span class="c1">// Assuming sc is your SparkContext variable</span> +<span class="n">sc</span><span class="o">.</span><span class="n">setLocalProperty</span><span class="o">(</span><span class="s">"spark.scheduler.pool"</span><span class="o">,</span> <span class="s">"pool1"</span><span class="o">)</span> +</code></pre></div> + +<p>After setting this local property, <em>all</em> jobs submitted within this thread (by calls in this thread +to <code>RDD.save</code>, <code>count</code>, <code>collect</code>, etc) will use this pool name. The setting is per-thread to make +it easy to have a thread run multiple jobs on behalf of the same user. If you’d like to clear the +pool that a thread is associated with, simply call:</p> + +<div class="highlight"><pre><code class="scala"><span class="n">sc</span><span class="o">.</span><span class="n">setLocalProperty</span><span class="o">(</span><span class="s">"spark.scheduler.pool"</span><span class="o">,</span> <span class="kc">null</span><span class="o">)</span> +</code></pre></div> + +<h2 id="default-behavior-of-pools">Default Behavior of Pools</h2> + +<p>By default, each pool gets an equal share of the cluster (also equal in share to each job in the default +pool), but inside each pool, jobs run in FIFO order. For example, if you create one pool per user, this +means that each user will get an equal share of the cluster, and that each user’s queries will run in +order instead of later queries taking resources from that user’s earlier ones.</p> + +<h2 id="configuring-pool-properties">Configuring Pool Properties</h2> + +<p>Specific pools’ properties can also be modified through a configuration file. Each pool supports three +properties:</p> + +<ul> + <li><code>schedulingMode</code>: This can be FIFO or FAIR, to control whether jobs within the pool queue up behind +each other (the default) or share the pool’s resources fairly.</li> + <li><code>weight</code>: This controls the pool’s share of the cluster relative to other pools. By default, all pools +have a weight of 1. If you give a specific pool a weight of 2, for example, it will get 2x more +resources as other active pools. Setting a high weight such as 1000 also makes it possible to implement +<em>priority</em> between pools—in essence, the weight-1000 pool will always get to launch tasks first +whenever it has jobs active.</li> + <li><code>minShare</code>: Apart from an overall weight, each pool can be given a <em>minimum shares</em> (as a number of +CPU cores) that the administrator would like it to have. The fair scheduler always attempts to meet +all active pools’ minimum shares before redistributing extra resources according to the weights. +The <code>minShare</code> property can therefore be another way to ensure that a pool can always get up to a +certain number of resources (e.g. 10 cores) quickly without giving it a high priority for the rest +of the cluster. By default, each pool’s <code>minShare</code> is 0.</li> +</ul> + +<p>The pool properties can be set by creating an XML file, similar to <code>conf/fairscheduler.xml.template</code>, +and setting a <code>spark.scheduler.allocation.file</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.scheduler.allocation.file"</span><span class="o">,</span> <span class="s">"/path/to/file"</span><span class="o">)</span> +</code></pre></div> + +<p>The format of the XML file is simply a <code><pool></code> element for each pool, with different elements +within it for the various settings. For example:</p> + +<div class="highlight"><pre><code class="xml"><span class="cp"><?xml version="1.0"?></span> +<span class="nt"><allocations></span> + <span class="nt"><pool</span> <span class="na">name=</span><span class="s">"production"</span><span class="nt">></span> + <span class="nt"><schedulingMode></span>FAIR<span class="nt"></schedulingMode></span> + <span class="nt"><weight></span>1<span class="nt"></weight></span> + <span class="nt"><minShare></span>2<span class="nt"></minShare></span> + <span class="nt"></pool></span> + <span class="nt"><pool</span> <span class="na">name=</span><span class="s">"test"</span><span class="nt">></span> + <span class="nt"><schedulingMode></span>FIFO<span class="nt"></schedulingMode></span> + <span class="nt"><weight></span>2<span class="nt"></weight></span> + <span class="nt"><minShare></span>3<span class="nt"></minShare></span> + <span class="nt"></pool></span> +<span class="nt"></allocations></span> +</code></pre></div> + +<p>A full example is also available in <code>conf/fairscheduler.xml.template</code>. Note that any pools not +configured in the XML file will simply get default values for all settings (scheduling mode FIFO, +weight 1, and minShare 0).</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/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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