diff options
| author | Patrick Wendell <pwendell@apache.org> | 2015-06-11 15:32:59 +0000 |
|---|---|---|
| committer | Patrick Wendell <pwendell@apache.org> | 2015-06-11 15:32:59 +0000 |
| commit | 840d9f3df35e66c0032f7ac0c284ae4675a4f818 (patch) | |
| tree | 9742b1bd3aa597e8f2c845959d42eb91170177a7 /site/docs/1.4.0/job-scheduling.html | |
| parent | 303c247e4604098518acaa2d8ebe63fc891706f0 (diff) | |
| download | spark-website-840d9f3df35e66c0032f7ac0c284ae4675a4f818.tar.gz spark-website-840d9f3df35e66c0032f7ac0c284ae4675a4f818.tar.bz2 spark-website-840d9f3df35e66c0032f7ac0c284ae4675a4f818.zip | |
Adding release 1.4.0
Diffstat (limited to 'site/docs/1.4.0/job-scheduling.html')
| -rw-r--r-- | site/docs/1.4.0/job-scheduling.html | 438 |
1 files changed, 438 insertions, 0 deletions
diff --git a/site/docs/1.4.0/job-scheduling.html b/site/docs/1.4.0/job-scheduling.html new file mode 100644 index 000000000..f481888b5 --- /dev/null +++ b/site/docs/1.4.0/job-scheduling.html @@ -0,0 +1,438 @@ +<!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 1.4.0 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"> + + + + </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">1.4.0</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">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">v1.4.0</span></p>--> + </div> + </div> + </div> + + <div class="container" id="content"> + + <h1 class="title">Job Scheduling</h1> + + + <ul id="markdown-toc"> + <li><a href="#overview" id="markdown-toc-overview">Overview</a></li> + <li><a href="#scheduling-across-applications" id="markdown-toc-scheduling-across-applications">Scheduling Across Applications</a> <ul> + <li><a href="#dynamic-resource-allocation" id="markdown-toc-dynamic-resource-allocation">Dynamic Resource Allocation</a> <ul> + <li><a href="#configuration-and-setup" id="markdown-toc-configuration-and-setup">Configuration and Setup</a></li> + <li><a href="#resource-allocation-policy" id="markdown-toc-resource-allocation-policy">Resource Allocation Policy</a> <ul> + <li><a href="#request-policy" id="markdown-toc-request-policy">Request Policy</a></li> + <li><a href="#remove-policy" id="markdown-toc-remove-policy">Remove Policy</a></li> + </ul> + </li> + <li><a href="#graceful-decommission-of-executors" id="markdown-toc-graceful-decommission-of-executors">Graceful Decommission of Executors</a></li> + </ul> + </li> + </ul> + </li> + <li><a href="#scheduling-within-an-application" id="markdown-toc-scheduling-within-an-application">Scheduling Within an Application</a> <ul> + <li><a href="#fair-scheduler-pools" id="markdown-toc-fair-scheduler-pools">Fair Scheduler Pools</a></li> + <li><a href="#default-behavior-of-pools" id="markdown-toc-default-behavior-of-pools">Default Behavior of Pools</a></li> + <li><a href="#configuring-pool-properties" id="markdown-toc-configuring-pool-properties">Configuring Pool Properties</a></li> + </ul> + </li> +</ul> + +<h1 id="overview">Overview</h1> + +<p>Spark has several facilities for scheduling resources between computations. 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. 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. 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> + +<h2 id="dynamic-resource-allocation">Dynamic Resource Allocation</h2> + +<p>Spark 1.2 introduces the ability to dynamically scale the set of cluster resources allocated to +your application up and down based on the workload. This means that your application may give +resources back to the cluster if they are no longer used and request them again later when there +is demand. This feature is particularly useful if multiple applications share resources in your +Spark cluster. If a subset of the resources allocated to an application becomes idle, it can be +returned to the cluster’s pool of resources and acquired by other applications. In Spark, dynamic +resource allocation is performed on the granularity of the executor and can be enabled through +<code>spark.dynamicAllocation.enabled</code>.</p> + +<p>This feature is currently disabled by default and available only on <a href="running-on-yarn.html">YARN</a>. +A future release will extend this to <a href="spark-standalone.html">standalone mode</a> and +<a href="running-on-mesos.html#mesos-run-modes">Mesos coarse-grained mode</a>. Note that although Spark on +Mesos already has a similar notion of dynamic resource sharing in fine-grained mode, enabling +dynamic allocation allows your Mesos application to take advantage of coarse-grained low-latency +scheduling while sharing cluster resources efficiently.</p> + +<h3 id="configuration-and-setup">Configuration and Setup</h3> + +<p>All configurations used by this feature live under the <code>spark.dynamicAllocation.*</code> namespace. +To enable this feature, your application must set <code>spark.dynamicAllocation.enabled</code> to <code>true</code>. +Other relevant configurations are described on the +<a href="configuration.html#dynamic-allocation">configurations page</a> and in the subsequent sections in +detail.</p> + +<p>Additionally, your application must use an external shuffle service. The purpose of the service is +to preserve the shuffle files written by executors so the executors can be safely removed (more +detail described <a href="job-scheduling.html#graceful-decommission-of-executors">below</a>). To enable +this service, set <code>spark.shuffle.service.enabled</code> to <code>true</code>. In YARN, this external shuffle service +is implemented in <code>org.apache.spark.yarn.network.YarnShuffleService</code> that runs in each <code>NodeManager</code> +in your cluster. To start this service, follow these steps:</p> + +<ol> + <li>Build Spark with the <a href="building-spark.html">YARN profile</a>. Skip this step if you are using a +pre-packaged distribution.</li> + <li>Locate the <code>spark-<version>-yarn-shuffle.jar</code>. This should be under +<code>$SPARK_HOME/network/yarn/target/scala-<version></code> if you are building Spark yourself, and under +<code>lib</code> if you are using a distribution.</li> + <li>Add this jar to the classpath of all <code>NodeManager</code>s in your cluster.</li> + <li>In the <code>yarn-site.xml</code> on each node, add <code>spark_shuffle</code> to <code>yarn.nodemanager.aux-services</code>, +then set <code>yarn.nodemanager.aux-services.spark_shuffle.class</code> to +<code>org.apache.spark.network.yarn.YarnShuffleService</code>. Additionally, set all relevant +<code>spark.shuffle.service.*</code> <a href="configuration.html">configurations</a>.</li> + <li>Restart all <code>NodeManager</code>s in your cluster.</li> +</ol> + +<h3 id="resource-allocation-policy">Resource Allocation Policy</h3> + +<p>At a high level, Spark should relinquish executors when they are no longer used and acquire +executors when they are needed. Since there is no definitive way to predict whether an executor +that is about to be removed will run a task in the near future, or whether a new executor that is +about to be added will actually be idle, we need a set of heuristics to determine when to remove +and request executors.</p> + +<h4 id="request-policy">Request Policy</h4> + +<p>A Spark application with dynamic allocation enabled requests additional executors when it has +pending tasks waiting to be scheduled. This condition necessarily implies that the existing set +of executors is insufficient to simultaneously saturate all tasks that have been submitted but +not yet finished.</p> + +<p>Spark requests executors in rounds. The actual request is triggered when there have been pending +tasks for <code>spark.dynamicAllocation.schedulerBacklogTimeout</code> seconds, and then triggered again +every <code>spark.dynamicAllocation.sustainedSchedulerBacklogTimeout</code> seconds thereafter if the queue +of pending tasks persists. Additionally, the number of executors requested in each round increases +exponentially from the previous round. For instance, an application will add 1 executor in the +first round, and then 2, 4, 8 and so on executors in the subsequent rounds.</p> + +<p>The motivation for an exponential increase policy is twofold. First, an application should request +executors cautiously in the beginning in case it turns out that only a few additional executors is +sufficient. This echoes the justification for TCP slow start. Second, the application should be +able to ramp up its resource usage in a timely manner in case it turns out that many executors are +actually needed.</p> + +<h4 id="remove-policy">Remove Policy</h4> + +<p>The policy for removing executors is much simpler. A Spark application removes an executor when +it has been idle for more than <code>spark.dynamicAllocation.executorIdleTimeout</code> seconds. Note that, +under most circumstances, this condition is mutually exclusive with the request condition, in that +an executor should not be idle if there are still pending tasks to be scheduled.</p> + +<h3 id="graceful-decommission-of-executors">Graceful Decommission of Executors</h3> + +<p>Before dynamic allocation, a Spark executor exits either on failure or when the associated +application has also exited. In both scenarios, all state associated with the executor is no +longer needed and can be safely discarded. With dynamic allocation, however, the application +is still running when an executor is explicitly removed. If the application attempts to access +state stored in or written by the executor, it will have to perform a recompute the state. Thus, +Spark needs a mechanism to decommission an executor gracefully by preserving its state before +removing it.</p> + +<p>This requirement is especially important for shuffles. During a shuffle, the Spark executor first +writes its own map outputs locally to disk, and then acts as the server for those files when other +executors attempt to fetch them. In the event of stragglers, which are tasks that run for much +longer than their peers, dynamic allocation may remove an executor before the shuffle completes, +in which case the shuffle files written by that executor must be recomputed unnecessarily.</p> + +<p>The solution for preserving shuffle files is to use an external shuffle service, also introduced +in Spark 1.2. This service refers to a long-running process that runs on each node of your cluster +independently of your Spark applications and their executors. If the service is enabled, Spark +executors will fetch shuffle files from the service instead of from each other. This means any +shuffle state written by an executor may continue to be served beyond the executor’s lifetime.</p> + +<p>In addition to writing shuffle files, executors also cache data either on disk or in memory. +When an executor is removed, however, all cached data will no longer be accessible. There is +currently not yet a solution for this in Spark 1.2. In future releases, the cached data may be +preserved through an off-heap storage similar in spirit to how shuffle files are preserved through +the external shuffle service.</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="language-scala" data-lang="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="language-scala" data-lang="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="language-scala" data-lang="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="language-scala" data-lang="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="language-xml" data-lang="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 ? 'https://' : 'http://') + + 'cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'; + d.getElementsByTagName('head')[0].appendChild(script); + }(document)); + </script> + </body> +</html> |