diff options
author | Patrick Wendell <pwendell@apache.org> | 2013-12-19 23:20:24 +0000 |
---|---|---|
committer | Patrick Wendell <pwendell@apache.org> | 2013-12-19 23:20:24 +0000 |
commit | 5641130a8ed2a2917e64298d6832ec3b201e49a0 (patch) | |
tree | 2f4ccf0c3b4f27e834596b21b2da922f3109d03e /site/docs/0.8.1/spark-standalone.html | |
parent | 9791cb95ab6aee9f67dc2d0ff95525e8ca079c23 (diff) | |
download | spark-website-5641130a8ed2a2917e64298d6832ec3b201e49a0.tar.gz spark-website-5641130a8ed2a2917e64298d6832ec3b201e49a0.tar.bz2 spark-website-5641130a8ed2a2917e64298d6832ec3b201e49a0.zip |
Updating docs to point to 0.8.1.
Diffstat (limited to 'site/docs/0.8.1/spark-standalone.html')
-rw-r--r-- | site/docs/0.8.1/spark-standalone.html | 441 |
1 files changed, 441 insertions, 0 deletions
diff --git a/site/docs/0.8.1/spark-standalone.html b/site/docs/0.8.1/spark-standalone.html new file mode 100644 index 000000000..e98c23e7d --- /dev/null +++ b/site/docs/0.8.1/spark-standalone.html @@ -0,0 +1,441 @@ +<!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>Spark Standalone Mode - Spark 0.8.1 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 ? '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">0.8.1</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="scala-programming-guide.html">Spark in Scala</a></li> + <li><a href="java-programming-guide.html">Spark in Java</a></li> + <li><a href="python-programming-guide.html">Spark in Python</a></li> + <li class="divider"></li> + <li><a href="streaming-programming-guide.html">Spark Streaming</a></li> + <li><a href="mllib-guide.html">MLlib (Machine Learning)</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/core/index.html#org.apache.spark.package">Spark Core for Java/Scala</a></li> + <li><a href="api/pyspark/index.html">Spark Core for Python</a></li> + <li class="divider"></li> + <li><a href="api/streaming/index.html#org.apache.spark.streaming.package">Spark Streaming</a></li> + <li><a href="api/mllib/index.html#org.apache.spark.mllib.package">MLlib (Machine Learning)</a></li> + <li><a href="api/bagel/index.html#org.apache.spark.bagel.package">Bagel (Pregel on Spark)</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="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="hadoop-third-party-distributions.html">Running with CDH/HDP</a></li> + <li><a href="hardware-provisioning.html">Hardware Provisioning</a></li> + <li><a href="job-scheduling.html">Job Scheduling</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">v0.8.1</span></p>--> + </div> + </div> + </div> + + <div class="container" id="content"> + <h1 class="title">Spark Standalone Mode</h1> + + <ul id="markdown-toc"> + <li><a href="#installing-spark-standalone-to-a-cluster">Installing Spark Standalone to a Cluster</a></li> + <li><a href="#starting-a-cluster-manually">Starting a Cluster Manually</a></li> + <li><a href="#cluster-launch-scripts">Cluster Launch Scripts</a></li> + <li><a href="#connecting-an-application-to-the-cluster">Connecting an Application to the Cluster</a></li> + <li><a href="#resource-scheduling">Resource Scheduling</a></li> + <li><a href="#monitoring-and-logging">Monitoring and Logging</a></li> + <li><a href="#running-alongside-hadoop">Running Alongside Hadoop</a></li> + <li><a href="#high-availability">High Availability</a> <ul> + <li><a href="#standby-masters-with-zookeeper">Standby Masters with ZooKeeper</a></li> + <li><a href="#single-node-recovery-with-local-file-system">Single-Node Recovery with Local File System</a></li> + </ul> + </li> +</ul> + +<p>In addition to running on the Mesos or YARN cluster managers, Spark also provides a simple standalone deploy mode. You can launch a standalone cluster either manually, by starting a master and workers by hand, or use our provided <a href="#cluster-launch-scripts">launch scripts</a>. It is also possible to run these daemons on a single machine for testing.</p> + +<h1 id="installing-spark-standalone-to-a-cluster">Installing Spark Standalone to a Cluster</h1> + +<p>The easiest way to deploy Spark is by running the <code>./make-distribution.sh</code> script to create a binary distribution. +This distribution can be deployed to any machine with the Java runtime installed; there is no need to install Scala.</p> + +<p>The recommended procedure is to deploy and start the master on one node first, get the master spark URL, +then modify <code>conf/spark-env.sh</code> in the <code>dist/</code> directory before deploying to all the other nodes.</p> + +<h1 id="starting-a-cluster-manually">Starting a Cluster Manually</h1> + +<p>You can start a standalone master server by executing:</p> + +<pre><code>./bin/start-master.sh +</code></pre> + +<p>Once started, the master will print out a <code>spark://HOST:PORT</code> URL for itself, which you can use to connect workers to it, +or pass as the “master” argument to <code>SparkContext</code>. You can also find this URL on +the master’s web UI, which is <a href="http://localhost:8080">http://localhost:8080</a> by default.</p> + +<p>Similarly, you can start one or more workers and connect them to the master via:</p> + +<pre><code>./spark-class org.apache.spark.deploy.worker.Worker spark://IP:PORT +</code></pre> + +<p>Once you have started a worker, look at the master’s web UI (<a href="http://localhost:8080">http://localhost:8080</a> by default). +You should see the new node listed there, along with its number of CPUs and memory (minus one gigabyte left for the OS).</p> + +<p>Finally, the following configuration options can be passed to the master and worker:</p> + +<table class="table"> + <tr><th style="width:21%">Argument</th><th>Meaning</th></tr> + <tr> + <td><code>-i IP</code>, <code>--ip IP</code></td> + <td>IP address or DNS name to listen on</td> + </tr> + <tr> + <td><code>-p PORT</code>, <code>--port PORT</code></td> + <td>Port for service to listen on (default: 7077 for master, random for worker)</td> + </tr> + <tr> + <td><code>--webui-port PORT</code></td> + <td>Port for web UI (default: 8080 for master, 8081 for worker)</td> + </tr> + <tr> + <td><code>-c CORES</code>, <code>--cores CORES</code></td> + <td>Total CPU cores to allow Spark applicatons to use on the machine (default: all available); only on worker</td> + </tr> + <tr> + <td><code>-m MEM</code>, <code>--memory MEM</code></td> + <td>Total amount of memory to allow Spark applicatons to use on the machine, in a format like 1000M or 2G (default: your machine's total RAM minus 1 GB); only on worker</td> + </tr> + <tr> + <td><code>-d DIR</code>, <code>--work-dir DIR</code></td> + <td>Directory to use for scratch space and job output logs (default: SPARK_HOME/work); only on worker</td> + </tr> +</table> + +<h1 id="cluster-launch-scripts">Cluster Launch Scripts</h1> + +<p>To launch a Spark standalone cluster with the launch scripts, you need to create a file called <code>conf/slaves</code> in your Spark directory, which should contain the hostnames of all the machines where you would like to start Spark workers, one per line. The master machine must be able to access each of the slave machines via password-less <code>ssh</code> (using a private key). For testing, you can just put <code>localhost</code> in this file.</p> + +<p>Once you’ve set up this file, you can launch or stop your cluster with the following shell scripts, based on Hadoop’s deploy scripts, and available in <code>SPARK_HOME/bin</code>:</p> + +<ul> + <li><code>bin/start-master.sh</code> - Starts a master instance on the machine the script is executed on.</li> + <li><code>bin/start-slaves.sh</code> - Starts a slave instance on each machine specified in the <code>conf/slaves</code> file.</li> + <li><code>bin/start-all.sh</code> - Starts both a master and a number of slaves as described above.</li> + <li><code>bin/stop-master.sh</code> - Stops the master that was started via the <code>bin/start-master.sh</code> script.</li> + <li><code>bin/stop-slaves.sh</code> - Stops the slave instances that were started via <code>bin/start-slaves.sh</code>.</li> + <li><code>bin/stop-all.sh</code> - Stops both the master and the slaves as described above.</li> +</ul> + +<p>Note that these scripts must be executed on the machine you want to run the Spark master on, not your local machine.</p> + +<p>You can optionally configure the cluster further by setting environment variables in <code>conf/spark-env.sh</code>. Create this file by starting with the <code>conf/spark-env.sh.template</code>, and <em>copy it to all your worker machines</em> for the settings to take effect. The following settings are available:</p> + +<table class="table"> + <tr><th style="width:21%">Environment Variable</th><th>Meaning</th></tr> + <tr> + <td><code>SPARK_MASTER_IP</code></td> + <td>Bind the master to a specific IP address, for example a public one.</td> + </tr> + <tr> + <td><code>SPARK_MASTER_PORT</code></td> + <td>Start the master on a different port (default: 7077).</td> + </tr> + <tr> + <td><code>SPARK_MASTER_WEBUI_PORT</code></td> + <td>Port for the master web UI (default: 8080).</td> + </tr> + <tr> + <td><code>SPARK_WORKER_PORT</code></td> + <td>Start the Spark worker on a specific port (default: random).</td> + </tr> + <tr> + <td><code>SPARK_WORKER_DIR</code></td> + <td>Directory to run applications in, which will include both logs and scratch space (default: SPARK_HOME/work).</td> + </tr> + <tr> + <td><code>SPARK_WORKER_CORES</code></td> + <td>Total number of cores to allow Spark applications to use on the machine (default: all available cores).</td> + </tr> + <tr> + <td><code>SPARK_WORKER_MEMORY</code></td> + <td>Total amount of memory to allow Spark applications to use on the machine, e.g. <code>1000m</code>, <code>2g</code> (default: total memory minus 1 GB); note that each application's <i>individual</i> memory is configured using its <code>spark.executor.memory</code> property.</td> + </tr> + <tr> + <td><code>SPARK_WORKER_WEBUI_PORT</code></td> + <td>Port for the worker web UI (default: 8081).</td> + </tr> + <tr> + <td><code>SPARK_WORKER_INSTANCES</code></td> + <td> + Number of worker instances to run on each machine (default: 1). You can make this more than 1 if + you have have very large machines and would like multiple Spark worker processes. If you do set + this, make sure to also set <code>SPARK_WORKER_CORES</code> explicitly to limit the cores per worker, + or else each worker will try to use all the cores. + </td> + </tr> + <tr> + <td><code>SPARK_DAEMON_MEMORY</code></td> + <td>Memory to allocate to the Spark master and worker daemons themselves (default: 512m).</td> + </tr> + <tr> + <td><code>SPARK_DAEMON_JAVA_OPTS</code></td> + <td>JVM options for the Spark master and worker daemons themselves (default: none).</td> + </tr> +</table> + +<p><strong>Note:</strong> The launch scripts do not currently support Windows. To run a Spark cluster on Windows, start the master and workers by hand.</p> + +<h1 id="connecting-an-application-to-the-cluster">Connecting an Application to the Cluster</h1> + +<p>To run an application on the Spark cluster, simply pass the <code>spark://IP:PORT</code> URL of the master as to the <a href="scala-programming-guide.html#initializing-spark"><code>SparkContext</code> +constructor</a>.</p> + +<p>To run an interactive Spark shell against the cluster, run the following command:</p> + +<pre><code>MASTER=spark://IP:PORT ./spark-shell +</code></pre> + +<p>Note that if you are running spark-shell from one of the spark cluster machines, the <code>spark-shell</code> script will +automatically set MASTER from the <code>SPARK_MASTER_IP</code> and <code>SPARK_MASTER_PORT</code> variables in <code>conf/spark-env.sh</code>.</p> + +<p>You can also pass an option <code>-c <numCores></code> to control the number of cores that spark-shell uses on the cluster.</p> + +<h1 id="resource-scheduling">Resource Scheduling</h1> + +<p>The standalone cluster mode currently only supports a simple FIFO scheduler across applications. +However, to allow multiple concurrent users, you can control the maximum number of resources each +application will acquire. +By default, it will acquire <em>all</em> cores in the cluster, which only makes sense if you just run one +application at a time. You can cap the number of cores using +<code>System.setProperty("spark.cores.max", "10")</code> (for example). +This value must be set <em>before</em> initializing your SparkContext.</p> + +<h1 id="monitoring-and-logging">Monitoring and Logging</h1> + +<p>Spark’s standalone mode offers a web-based user interface to monitor the cluster. The master and each worker has its own web UI that shows cluster and job statistics. By default you can access the web UI for the master at port 8080. The port can be changed either in the configuration file or via command-line options.</p> + +<p>In addition, detailed log output for each job is also written to the work directory of each slave node (<code>SPARK_HOME/work</code> by default). You will see two files for each job, <code>stdout</code> and <code>stderr</code>, with all output it wrote to its console.</p> + +<h1 id="running-alongside-hadoop">Running Alongside Hadoop</h1> + +<p>You can run Spark alongside your existing Hadoop cluster by just launching it as a separate service on the same machines. To access Hadoop data from Spark, just use a hdfs:// URL (typically <code>hdfs://<namenode>:9000/path</code>, but you can find the right URL on your Hadoop Namenode’s web UI). Alternatively, you can set up a separate cluster for Spark, and still have it access HDFS over the network; this will be slower than disk-local access, but may not be a concern if you are still running in the same local area network (e.g. you place a few Spark machines on each rack that you have Hadoop on).</p> + +<h1 id="high-availability">High Availability</h1> + +<p>By default, standalone scheduling clusters are resilient to Worker failures (insofar as Spark itself is resilient to losing work by moving it to other workers). However, the scheduler uses a Master to make scheduling decisions, and this (by default) creates a single point of failure: if the Master crashes, no new applications can be created. In order to circumvent this, we have two high availability schemes, detailed below.</p> + +<h2 id="standby-masters-with-zookeeper">Standby Masters with ZooKeeper</h2> + +<p><strong>Overview</strong></p> + +<p>Utilizing ZooKeeper to provide leader election and some state storage, you can launch multiple Masters in your cluster connected to the same ZooKeeper instance. One will be elected “leader” and the others will remain in standby mode. If the current leader dies, another Master will be elected, recover the old Master’s state, and then resume scheduling. The entire recovery process (from the time the the first leader goes down) should take between 1 and 2 minutes. Note that this delay only affects scheduling <em>new</em> applications – applications that were already running during Master failover are unaffected.</p> + +<p>Learn more about getting started with ZooKeeper <a href="http://zookeeper.apache.org/doc/trunk/zookeeperStarted.html">here</a>.</p> + +<p><strong>Configuration</strong></p> + +<p>In order to enable this recovery mode, you can set SPARK_DAEMON_JAVA_OPTS in spark-env using this configuration:</p> + +<table class="table"> + <tr><th style="width:21%">System property</th><th>Meaning</th></tr> + <tr> + <td><code>spark.deploy.recoveryMode</code></td> + <td>Set to ZOOKEEPER to enable standby Master recovery mode (default: NONE).</td> + </tr> + <tr> + <td><code>spark.deploy.zookeeper.url</code></td> + <td>The ZooKeeper cluster url (e.g., 192.168.1.100:2181,192.168.1.101:2181).</td> + </tr> + <tr> + <td><code>spark.deploy.zookeeper.dir</code></td> + <td>The directory in ZooKeeper to store recovery state (default: /spark).</td> + </tr> +</table> + +<p>Possible gotcha: If you have multiple Masters in your cluster but fail to correctly configure the Masters to use ZooKeeper, the Masters will fail to discover each other and think they’re all leaders. This will not lead to a healthy cluster state (as all Masters will schedule independently).</p> + +<p><strong>Details</strong></p> + +<p>After you have a ZooKeeper cluster set up, enabling high availability is straightforward. Simply start multiple Master processes on different nodes with the same ZooKeeper configuration (ZooKeeper URL and directory). Masters can be added and removed at any time.</p> + +<p>In order to schedule new applications or add Workers to the cluster, they need to know the IP address of the current leader. This can be accomplished by simply passing in a list of Masters where you used to pass in a single one. For example, you might start your SparkContext pointing to <code>spark://host1:port1,host2:port2</code>. This would cause your SparkContext to try registering with both Masters – if <code>host1</code> goes down, this configuration would still be correct as we’d find the new leader, <code>host2</code>.</p> + +<p>There’s an important distinction to be made between “registering with a Master” and normal operation. When starting up, an application or Worker needs to be able to find and register with the current lead Master. Once it successfully registers, though, it is “in the system” (i.e., stored in ZooKeeper). If failover occurs, the new leader will contact all previously registered applications and Workers to inform them of the change in leadership, so they need not even have known of the existence of the new Master at startup.</p> + +<p>Due to this property, new Masters can be created at any time, and the only thing you need to worry about is that <em>new</em> applications and Workers can find it to register with in case it becomes the leader. Once registered, you’re taken care of.</p> + +<h2 id="single-node-recovery-with-local-file-system">Single-Node Recovery with Local File System</h2> + +<p><strong>Overview</strong></p> + +<p>ZooKeeper is the best way to go for production-level high availability, but if you just want to be able to restart the Master if it goes down, FILESYSTEM mode can take care of it. When applications and Workers register, they have enough state written to the provided directory so that they can be recovered upon a restart of the Master process.</p> + +<p><strong>Configuration</strong></p> + +<p>In order to enable this recovery mode, you can set SPARK_DAEMON_JAVA_OPTS in spark-env using this configuration:</p> + +<table class="table"> + <tr><th style="width:21%">System property</th><th>Meaning</th></tr> + <tr> + <td><code>spark.deploy.recoveryMode</code></td> + <td>Set to FILESYSTEM to enable single-node recovery mode (default: NONE).</td> + </tr> + <tr> + <td><code>spark.deploy.recoveryDirectory</code></td> + <td>The directory in which Spark will store recovery state, accessible from the Master's perspective.</td> + </tr> +</table> + +<p><strong>Details</strong></p> + +<ul> + <li>This solution can be used in tandem with a process monitor/manager like <a href="http://mmonit.com/monit/">monit</a>, or just to enable manual recovery via restart.</li> + <li>While filesystem recovery seems straightforwardly better than not doing any recovery at all, this mode may be suboptimal for certain development or experimental purposes. In particular, killing a master via stop-master.sh does not clean up its recovery state, so whenever you start a new Master, it will enter recovery mode. This could increase the startup time by up to 1 minute if it needs to wait for all previously-registered Workers/clients to timeout.</li> + <li>While it’s not officially supported, you could mount an NFS directory as the recovery directory. If the original Master node dies completely, you could then start a Master on a different node, which would correctly recover all previously registered Workers/applications (equivalent to ZooKeeper recovery). Future applications will have to be able to find the new Master, however, in order to register.</li> +</ul> + + <!-- Main hero unit for a primary marketing message or call to action --> + <!--<div class="hero-unit"> + <h1>Hello, world!</h1> + <p>This is a template for a simple marketing or informational website. It includes a large callout called the hero unit and three supporting pieces of content. Use it as a starting point to create something more unique.</p> + <p><a class="btn btn-primary btn-large">Learn more »</a></p> + </div>--> + + <!-- Example row of columns --> + <!--<div class="row"> + <div class="span4"> + <h2>Heading</h2> + <p>Donec id elit non mi porta gravida at eget metus. Fusce dapibus, tellus ac cursus commodo, tortor mauris condimentum nibh, ut fermentum massa justo sit amet risus. Etiam porta sem malesuada magna mollis euismod. Donec sed odio dui. </p> + <p><a class="btn" href="#">View details »</a></p> + </div> + <div class="span4"> + <h2>Heading</h2> + <p>Donec id elit non mi porta gravida at eget metus. Fusce dapibus, tellus ac cursus commodo, tortor mauris condimentum nibh, ut fermentum massa justo sit amet risus. Etiam porta sem malesuada magna mollis euismod. Donec sed odio dui. </p> + <p><a class="btn" href="#">View details »</a></p> + </div> + <div class="span4"> + <h2>Heading</h2> + <p>Donec sed odio dui. Cras justo odio, dapibus ac facilisis in, egestas eget quam. Vestibulum id ligula porta felis euismod semper. Fusce dapibus, tellus ac cursus commodo, tortor mauris condimentum nibh, ut fermentum massa justo sit amet risus.</p> + <p><a class="btn" href="#">View details »</a></p> + </div> + </div> + + <hr>--> + + <footer> + <hr> + <p style="text-align: center; veritcal-align: middle; color: #999;"> + Apache Spark is an effort undergoing incubation at the Apache Software Foundation. + <a href="http://incubator.apache.org"> + <img style="margin-left: 20px;" src="img/incubator-logo.png" /> + </a> + </p> + </footer> + + </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> + + <!-- A script to fix internal hash links because we have an overlapping top bar. + Based on https://github.com/twitter/bootstrap/issues/193#issuecomment-2281510 --> + <script> + $(function() { + function maybeScrollToHash() { + if (window.location.hash && $(window.location.hash).length) { + var newTop = $(window.location.hash).offset().top - $('#topbar').height() - 5; + $(window).scrollTop(newTop); + } + } + $(window).bind('hashchange', function() { + maybeScrollToHash(); + }); + // Scroll now too in case we had opened the page on a hash, but wait 1 ms because some browsers + // will try to do *their* initial scroll after running the onReady handler. + setTimeout(function() { maybeScrollToHash(); }, 1) + }) + </script> + + </body> +</html> |