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| author | Patrick Wendell <pwendell@apache.org> | 2014-02-03 06:29:51 +0000 |
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| committer | Patrick Wendell <pwendell@apache.org> | 2014-02-03 06:29:51 +0000 |
| commit | 61203830f49cf7f5ce82f70ead2a155e256c02ee (patch) | |
| tree | 67d83dfa860156196303eaa1f4873e2fefffe904 /site/docs/0.9.0/cluster-overview.html | |
| parent | 49ab3963af9b1d79c73f749ad200f7be80785f9f (diff) | |
| download | spark-website-61203830f49cf7f5ce82f70ead2a155e256c02ee.tar.gz spark-website-61203830f49cf7f5ce82f70ead2a155e256c02ee.tar.bz2 spark-website-61203830f49cf7f5ce82f70ead2a155e256c02ee.zip | |
Bumping version in docs.
Diffstat (limited to 'site/docs/0.9.0/cluster-overview.html')
| -rw-r--r-- | site/docs/0.9.0/cluster-overview.html | 328 |
1 files changed, 328 insertions, 0 deletions
diff --git a/site/docs/0.9.0/cluster-overview.html b/site/docs/0.9.0/cluster-overview.html new file mode 100644 index 000000000..5cc2cbd30 --- /dev/null +++ b/site/docs/0.9.0/cluster-overview.html @@ -0,0 +1,328 @@ +<!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>Cluster Mode Overview - Spark 0.9.0 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 ? 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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> + <li><a href="graphx-programming-guide.html">GraphX (Graph Processing)</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> + <li><a href="api/graphx/index.html#org.apache.spark.graphx.package">GraphX (Graph Processing)</a></li> + <li class="divider"></li> + <li class="dropdown-submenu"> + <a tabindex="-1" href="#">External Data Sources</a> + <ul class="dropdown-menu"> + <li><a href="api/external/kafka/index.html#org.apache.spark.streaming.kafka.KafkaUtils$">Kafka</a></li> + <li><a href="api/external/flume/index.html#org.apache.spark.streaming.flume.FlumeUtils$">Flume</a></li> + <li><a href="api/external/twitter/index.html#org.apache.spark.streaming.twitter.TwitterUtils$">Twitter</a></li> + <li><a href="api/external/zeromq/index.html#org.apache.spark.streaming.zeromq.ZeroMQUtils$">ZeroMQ</a></li> + <li><a href="api/external/mqtt/index.html#org.apache.spark.streaming.mqtt.MQTTUtils$">MQTT</a></li> + </ul> + </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">v0.9.0</span></p>--> + </div> + </div> + </div> + + <div class="container" id="content"> + <h1 class="title">Cluster Mode Overview</h1> + + <p>This document gives a short overview of how Spark runs on clusters, to make it easier to understand +the components involved.</p> + +<h1 id="components">Components</h1> + +<p>Spark applications run as independent sets of processes on a cluster, coordinated by the SparkContext +object in your main program (called the <em>driver program</em>). +Specifically, to run on a cluster, the SparkContext can connect to several types of <em>cluster managers</em> +(either Spark’s own standalone cluster manager or Mesos/YARN), which allocate resources across +applications. Once connected, Spark acquires <em>executors</em> on nodes in the cluster, which are +worker processes that run computations and store data for your application. +Next, it sends your application code (defined by JAR or Python files passed to SparkContext) to +the executors. Finally, SparkContext sends <em>tasks</em> for the executors to run.</p> + +<p style="text-align: center;"> + <img src="img/cluster-overview.png" title="Spark cluster components" alt="Spark cluster components" /> +</p> + +<p>There are several useful things to note about this architecture:</p> + +<ol> + <li>Each application gets its own executor processes, which stay up for the duration of the whole +application and run tasks in multiple threads. This has the benefit of isolating applications +from each other, on both the scheduling side (each driver schedules its own tasks) and executor +side (tasks from different applications run in different JVMs). However, it also means that +data cannot be shared across different Spark applications (instances of SparkContext) without +writing it to an external storage system.</li> + <li>Spark is agnostic to the underlying cluster manager. As long as it can acquire executor +processes, and these communicate with each other, it is relatively easy to run it even on a +cluster manager that also supports other applications (e.g. Mesos/YARN).</li> + <li>Because the driver schedules tasks on the cluster, it should be run close to the worker +nodes, preferably on the same local area network. If you’d like to send requests to the +cluster remotely, it’s better to open an RPC to the driver and have it submit operations +from nearby than to run a driver far away from the worker nodes.</li> +</ol> + +<h1 id="cluster-manager-types">Cluster Manager Types</h1> + +<p>The system currently supports three cluster managers:</p> + +<ul> + <li><a href="spark-standalone.html">Standalone</a> – a simple cluster manager included with Spark that makes it +easy to set up a cluster.</li> + <li><a href="running-on-mesos.html">Apache Mesos</a> – a general cluster manager that can also run Hadoop MapReduce +and service applications.</li> + <li><a href="running-on-yarn.html">Hadoop YARN</a> – the resource manager in Hadoop 2.</li> +</ul> + +<p>In addition, Spark’s <a href="ec2-scripts.html">EC2 launch scripts</a> make it easy to launch a standalone +cluster on Amazon EC2.</p> + +<h1 id="shipping-code-to-the-cluster">Shipping Code to the Cluster</h1> + +<p>The recommended way to ship your code to the cluster is to pass it through SparkContext’s constructor, +which takes a list of JAR files (Java/Scala) or .egg and .zip libraries (Python) to disseminate to +worker nodes. You can also dynamically add new files to be sent to executors with <code>SparkContext.addJar</code> +and <code>addFile</code>.</p> + +<h2 id="uris-for-addjar--addfile">URIs for addJar / addFile</h2> + +<ul> + <li><strong>file:</strong> - Absolute paths and <code>file:/</code> URIs are served by the driver’s HTTP file server, and every executor +pulls the file from the driver HTTP server</li> + <li><strong>hdfs:</strong>, <strong>http:</strong>, <strong>https:</strong>, <strong>ftp:</strong> - these pull down files and JARs from the URI as expected</li> + <li><strong>local:</strong> - a URI starting with local:/ is expected to exist as a local file on each worker node. This +means that no network IO will be incurred, and works well for large files/JARs that are pushed to each worker, +or shared via NFS, GlusterFS, etc.</li> +</ul> + +<p>Note that JARs and files are copied to the working directory for each SparkContext on the executor nodes. +Over time this can use up a significant amount of space and will need to be cleaned up.</p> + +<h1 id="monitoring">Monitoring</h1> + +<p>Each driver program has a web UI, typically on port 4040, that displays information about running +tasks, executors, and storage usage. Simply go to <code>http://<driver-node>:4040</code> in a web browser to +access this UI. The <a href="monitoring.html">monitoring guide</a> also describes other monitoring options.</p> + +<h1 id="job-scheduling">Job Scheduling</h1> + +<p>Spark gives control over resource allocation both <em>across</em> applications (at the level of the cluster +manager) and <em>within</em> applications (if multiple computations are happening on the same SparkContext). +The <a href="job-scheduling.html">job scheduling overview</a> describes this in more detail.</p> + +<h1 id="glossary">Glossary</h1> + +<p>The following table summarizes terms you’ll see used to refer to cluster concepts:</p> + +<table class="table"> + <thead> + <tr><th style="width: 130px;">Term</th><th>Meaning</th></tr> + </thead> + <tbody> + <tr> + <td>Application</td> + <td>User program built on Spark. Consists of a <em>driver program</em> and <em>executors</em> on the cluster.</td> + </tr> + <tr> + <td>Driver program</td> + <td>The process running the main() function of the application and creating the SparkContext</td> + </tr> + <tr> + <td>Cluster manager</td> + <td>An external service for acquiring resources on the cluster (e.g. standalone manager, Mesos, YARN)</td> + </tr> + <tr> + <td>Worker node</td> + <td>Any node that can run application code in the cluster</td> + </tr> + <tr> + <td>Executor</td> + <td>A process launched for an application on a worker node, that runs tasks and keeps data in memory + or disk storage across them. Each application has its own executors.</td> + </tr> + <tr> + <td>Task</td> + <td>A unit of work that will be sent to one executor</td> + </tr> + <tr> + <td>Job</td> + <td>A parallel computation consisting of multiple tasks that gets spawned in response to a Spark action + (e.g. <code>save</code>, <code>collect</code>); you'll see this term used in the driver's logs.</td> + </tr> + <tr> + <td>Stage</td> + <td>Each job gets divided into smaller sets of tasks called <em>stages</em> that depend on each other + (similar to the map and reduce stages in MapReduce); you'll see this term used in the driver's logs.</td> + </tr> + </tbody> +</table> + + <!-- 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. 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