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| <h1 class="title">Job Scheduling</h1> |
| |
| |
| <ul id="markdown-toc"> |
| <li><a href="#overview">Overview</a></li> |
| <li><a href="#scheduling-across-applications">Scheduling Across Applications</a></li> |
| <li><a href="#scheduling-within-an-application">Scheduling Within an Application</a> <ul> |
| <li><a href="#fair-scheduler-pools">Fair Scheduler Pools</a></li> |
| <li><a href="#default-behavior-of-pools">Default Behavior of Pools</a></li> |
| <li><a href="#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; 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> |
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