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| author | Xiangrui Meng <meng@apache.org> | 2014-07-23 22:08:20 +0000 |
|---|---|---|
| committer | Xiangrui Meng <meng@apache.org> | 2014-07-23 22:08:20 +0000 |
| commit | 300a95cc9c7b141fd243fee8275e47d8b2afd5c3 (patch) | |
| tree | ba4a76b7c5d5dc9dffd2e6f63fe8c5b4f5604d63 /site/docs/0.9.2/ec2-scripts.html | |
| parent | f8b391a470bae7a7d29195c61a79c73a78eb43b5 (diff) | |
| download | spark-website-300a95cc9c7b141fd243fee8275e47d8b2afd5c3.tar.gz spark-website-300a95cc9c7b141fd243fee8275e47d8b2afd5c3.tar.bz2 spark-website-300a95cc9c7b141fd243fee8275e47d8b2afd5c3.zip | |
add spark 0.9.2
Diffstat (limited to 'site/docs/0.9.2/ec2-scripts.html')
| -rw-r--r-- | site/docs/0.9.2/ec2-scripts.html | 355 |
1 files changed, 355 insertions, 0 deletions
diff --git a/site/docs/0.9.2/ec2-scripts.html b/site/docs/0.9.2/ec2-scripts.html new file mode 100644 index 000000000..a140b682a --- /dev/null +++ b/site/docs/0.9.2/ec2-scripts.html @@ -0,0 +1,355 @@ +<!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>Running Spark on EC2 - Spark 0.9.2 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">0.9.2</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> + <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">v0.9.2</span></p>--> + </div> + </div> + </div> + + <div class="container" id="content"> + <h1 class="title">Running Spark on EC2</h1> + + <p>The <code>spark-ec2</code> script, located in Spark’s <code>ec2</code> directory, allows you +to launch, manage and shut down Spark clusters on Amazon EC2. It automatically +sets up Spark, Shark and HDFS on the cluster for you. This guide describes +how to use <code>spark-ec2</code> to launch clusters, how to run jobs on them, and how +to shut them down. It assumes you’ve already signed up for an EC2 account +on the <a href="http://aws.amazon.com/">Amazon Web Services site</a>.</p> + +<p><code>spark-ec2</code> is designed to manage multiple named clusters. You can +launch a new cluster (telling the script its size and giving it a name), +shutdown an existing cluster, or log into a cluster. Each cluster is +identified by placing its machines into EC2 security groups whose names +are derived from the name of the cluster. For example, a cluster named +<code>test</code> will contain a master node in a security group called +<code>test-master</code>, and a number of slave nodes in a security group called +<code>test-slaves</code>. The <code>spark-ec2</code> script will create these security groups +for you based on the cluster name you request. You can also use them to +identify machines belonging to each cluster in the Amazon EC2 Console.</p> + +<h1 id="before-you-start">Before You Start</h1> + +<ul> + <li>Create an Amazon EC2 key pair for yourself. This can be done by +logging into your Amazon Web Services account through the <a href="http://aws.amazon.com/console/">AWS +console</a>, clicking Key Pairs on the +left sidebar, and creating and downloading a key. Make sure that you +set the permissions for the private key file to <code>600</code> (i.e. only you +can read and write it) so that <code>ssh</code> will work.</li> + <li>Whenever you want to use the <code>spark-ec2</code> script, set the environment +variables <code>AWS_ACCESS_KEY_ID</code> and <code>AWS_SECRET_ACCESS_KEY</code> to your +Amazon EC2 access key ID and secret access key. These can be +obtained from the <a href="http://aws.amazon.com/">AWS homepage</a> by clicking +Account > Security Credentials > Access Credentials.</li> +</ul> + +<h1 id="launching-a-cluster">Launching a Cluster</h1> + +<ul> + <li>Go into the <code>ec2</code> directory in the release of Spark you downloaded.</li> + <li>Run +<code>./spark-ec2 -k <keypair> -i <key-file> -s <num-slaves> launch <cluster-name></code>, +where <code><keypair></code> is the name of your EC2 key pair (that you gave it +when you created it), <code><key-file></code> is the private key file for your +key pair, <code><num-slaves></code> is the number of slave nodes to launch (try +1 at first), and <code><cluster-name></code> is the name to give to your +cluster.</li> + <li>After everything launches, check that the cluster scheduler is up and sees +all the slaves by going to its web UI, which will be printed at the end of +the script (typically <code>http://<master-hostname>:8080</code>).</li> +</ul> + +<p>You can also run <code>./spark-ec2 --help</code> to see more usage options. The +following options are worth pointing out:</p> + +<ul> + <li><code>--instance-type=<INSTANCE_TYPE></code> can be used to specify an EC2 +instance type to use. For now, the script only supports 64-bit instance +types, and the default type is <code>m1.large</code> (which has 2 cores and 7.5 GB +RAM). Refer to the Amazon pages about <a href="http://aws.amazon.com/ec2/instance-types">EC2 instance +types</a> and <a href="http://aws.amazon.com/ec2/#pricing">EC2 +pricing</a> for information about other +instance types. </li> + <li><code>--region=<EC2_REGION></code> specifies an EC2 region in which to launch +instances. The default region is <code>us-east-1</code>.</li> + <li><code>--zone=<EC2_ZONE></code> can be used to specify an EC2 availability zone +to launch instances in. Sometimes, you will get an error because there +is not enough capacity in one zone, and you should try to launch in +another.</li> + <li><code>--ebs-vol-size=GB</code> will attach an EBS volume with a given amount +of space to each node so that you can have a persistent HDFS cluster +on your nodes across cluster restarts (see below).</li> + <li><code>--spot-price=PRICE</code> will launch the worker nodes as +<a href="http://aws.amazon.com/ec2/spot-instances/">Spot Instances</a>, +bidding for the given maximum price (in dollars).</li> + <li><code>--spark-version=VERSION</code> will pre-load the cluster with the +specified version of Spark. VERSION can be a version number +(e.g. “0.7.3”) or a specific git hash. By default, a recent +version will be used.</li> + <li>If one of your launches fails due to e.g. not having the right +permissions on your private key file, you can run <code>launch</code> with the +<code>--resume</code> option to restart the setup process on an existing cluster.</li> +</ul> + +<h1 id="running-applications">Running Applications</h1> + +<ul> + <li>Go into the <code>ec2</code> directory in the release of Spark you downloaded.</li> + <li>Run <code>./spark-ec2 -k <keypair> -i <key-file> login <cluster-name></code> to +SSH into the cluster, where <code><keypair></code> and <code><key-file></code> are as +above. (This is just for convenience; you could also use +the EC2 console.)</li> + <li>To deploy code or data within your cluster, you can log in and use the +provided script <code>~/spark-ec2/copy-dir</code>, which, +given a directory path, RSYNCs it to the same location on all the slaves.</li> + <li>If your application needs to access large datasets, the fastest way to do +that is to load them from Amazon S3 or an Amazon EBS device into an +instance of the Hadoop Distributed File System (HDFS) on your nodes. +The <code>spark-ec2</code> script already sets up a HDFS instance for you. It’s +installed in <code>/root/ephemeral-hdfs</code>, and can be accessed using the +<code>bin/hadoop</code> script in that directory. Note that the data in this +HDFS goes away when you stop and restart a machine.</li> + <li>There is also a <em>persistent HDFS</em> instance in +<code>/root/persistent-hdfs</code> that will keep data across cluster restarts. +Typically each node has relatively little space of persistent data +(about 3 GB), but you can use the <code>--ebs-vol-size</code> option to +<code>spark-ec2</code> to attach a persistent EBS volume to each node for +storing the persistent HDFS.</li> + <li>Finally, if you get errors while running your application, look at the slave’s logs +for that application inside of the scheduler work directory (/root/spark/work). You can +also view the status of the cluster using the web UI: <code>http://<master-hostname>:8080</code>.</li> +</ul> + +<h1 id="configuration">Configuration</h1> + +<p>You can edit <code>/root/spark/conf/spark-env.sh</code> on each machine to set Spark configuration options, such +as JVM options. This file needs to be copied to <strong>every machine</strong> to reflect the change. The easiest way to +do this is to use a script we provide called <code>copy-dir</code>. First edit your <code>spark-env.sh</code> file on the master, +then run <code>~/spark-ec2/copy-dir /root/spark/conf</code> to RSYNC it to all the workers.</p> + +<p>The <a href="configuration.html">configuration guide</a> describes the available configuration options.</p> + +<h1 id="terminating-a-cluster">Terminating a Cluster</h1> + +<p><strong><em>Note that there is no way to recover data on EC2 nodes after shutting +them down! Make sure you have copied everything important off the nodes +before stopping them.</em></strong></p> + +<ul> + <li>Go into the <code>ec2</code> directory in the release of Spark you downloaded.</li> + <li>Run <code>./spark-ec2 destroy <cluster-name></code>.</li> +</ul> + +<h1 id="pausing-and-restarting-clusters">Pausing and Restarting Clusters</h1> + +<p>The <code>spark-ec2</code> script also supports pausing a cluster. In this case, +the VMs are stopped but not terminated, so they +<strong><em>lose all data on ephemeral disks</em></strong> but keep the data in their +root partitions and their <code>persistent-hdfs</code>. Stopped machines will not +cost you any EC2 cycles, but <strong><em>will</em></strong> continue to cost money for EBS +storage.</p> + +<ul> + <li>To stop one of your clusters, go into the <code>ec2</code> directory and run +<code>./spark-ec2 stop <cluster-name></code>.</li> + <li>To restart it later, run +<code>./spark-ec2 -i <key-file> start <cluster-name></code>.</li> + <li>To ultimately destroy the cluster and stop consuming EBS space, run +<code>./spark-ec2 destroy <cluster-name></code> as described in the previous +section.</li> +</ul> + +<h1 id="limitations">Limitations</h1> + +<ul> + <li>Support for “cluster compute” nodes is limited – there’s no way to specify a +locality group. However, you can launch slave nodes in your +<code><clusterName>-slaves</code> group manually and then use <code>spark-ec2 launch +--resume</code> to start a cluster with them.</li> +</ul> + +<p>If you have a patch or suggestion for one of these limitations, feel free to +<a href="contributing-to-spark.html">contribute</a> it!</p> + +<h1 id="accessing-data-in-s3">Accessing Data in S3</h1> + +<p>Spark’s file interface allows it to process data in Amazon S3 using the same URI formats that are supported for Hadoop. You can specify a path in S3 as input through a URI of the form <code>s3n://<bucket>/path</code>. You will also need to set your Amazon security credentials, either by setting the environment variables <code>AWS_ACCESS_KEY_ID</code> and <code>AWS_SECRET_ACCESS_KEY</code> before your program or through <code>SparkContext.hadoopConfiguration</code>. Full instructions on S3 access using the Hadoop input libraries can be found on the <a href="http://wiki.apache.org/hadoop/AmazonS3">Hadoop S3 page</a>.</p> + +<p>In addition to using a single input file, you can also use a directory of files as input by simply giving the path to the directory.</p> + + <!-- 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. 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