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| author | Reynold Xin <rxin@apache.org> | 2015-09-17 22:07:42 +0000 |
|---|---|---|
| committer | Reynold Xin <rxin@apache.org> | 2015-09-17 22:07:42 +0000 |
| commit | ee9ffe89d608e7640a2487406b618d27e58026d6 (patch) | |
| tree | 50ec819abb41a9a769d7f64eed1f0ab2084aa6ff /site/docs/1.5.0/running-on-mesos.html | |
| parent | c7104724b279f09486ea62f4a24252e8d06f5c96 (diff) | |
| download | spark-website-ee9ffe89d608e7640a2487406b618d27e58026d6.tar.gz spark-website-ee9ffe89d608e7640a2487406b618d27e58026d6.tar.bz2 spark-website-ee9ffe89d608e7640a2487406b618d27e58026d6.zip | |
delete 1.5.0
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diff --git a/site/docs/1.5.0/running-on-mesos.html b/site/docs/1.5.0/running-on-mesos.html deleted file mode 100644 index 5e80712d2..000000000 --- a/site/docs/1.5.0/running-on-mesos.html +++ /dev/null @@ -1,591 +0,0 @@ -<!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 Mesos - Spark 1.5.0 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"> - - - <!-- Google analytics script --> - <script type="text/javascript"> - var _gaq = _gaq || []; - _gaq.push(['_setAccount', 'UA-32518208-2']); - _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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</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">v1.5.0</span></p>--> - </div> - </div> - </div> - - <div class="container" id="content"> - - <h1 class="title">Running Spark on Mesos</h1> - - - <ul id="markdown-toc"> - <li><a href="#how-it-works">How it Works</a></li> - <li><a href="#installing-mesos">Installing Mesos</a> <ul> - <li><a href="#from-source">From Source</a></li> - <li><a href="#third-party-packages">Third-Party Packages</a></li> - <li><a href="#verification">Verification</a></li> - </ul> - </li> - <li><a href="#connecting-spark-to-mesos">Connecting Spark to Mesos</a> <ul> - <li><a href="#uploading-spark-package">Uploading Spark Package</a></li> - <li><a href="#using-a-mesos-master-url">Using a Mesos Master URL</a></li> - <li><a href="#client-mode">Client Mode</a></li> - <li><a href="#cluster-mode">Cluster mode</a></li> - </ul> - </li> - <li><a href="#mesos-run-modes">Mesos Run Modes</a></li> - <li><a href="#mesos-docker-support">Mesos Docker Support</a></li> - <li><a href="#running-alongside-hadoop">Running Alongside Hadoop</a></li> - <li><a href="#dynamic-resource-allocation-with-mesos">Dynamic Resource Allocation with Mesos</a></li> - <li><a href="#configuration">Configuration</a> <ul> - <li><a href="#spark-properties">Spark Properties</a></li> - </ul> - </li> - <li><a href="#troubleshooting-and-debugging">Troubleshooting and Debugging</a></li> -</ul> - -<p>Spark can run on hardware clusters managed by <a href="http://mesos.apache.org/">Apache Mesos</a>.</p> - -<p>The advantages of deploying Spark with Mesos include:</p> - -<ul> - <li>dynamic partitioning between Spark and other -<a href="https://mesos.apache.org/documentation/latest/mesos-frameworks/">frameworks</a></li> - <li>scalable partitioning between multiple instances of Spark</li> -</ul> - -<h1 id="how-it-works">How it Works</h1> - -<p>In a standalone cluster deployment, the cluster manager in the below diagram is a Spark master -instance. When using Mesos, the Mesos master replaces the Spark master as the cluster manager.</p> - -<p style="text-align: center;"> - <img src="img/cluster-overview.png" title="Spark cluster components" alt="Spark cluster components" /> -</p> - -<p>Now when a driver creates a job and starts issuing tasks for scheduling, Mesos determines what -machines handle what tasks. Because it takes into account other frameworks when scheduling these -many short-lived tasks, multiple frameworks can coexist on the same cluster without resorting to a -static partitioning of resources.</p> - -<p>To get started, follow the steps below to install Mesos and deploy Spark jobs via Mesos.</p> - -<h1 id="installing-mesos">Installing Mesos</h1> - -<p>Spark 1.5.0 is designed for use with Mesos 0.21.0 and does not -require any special patches of Mesos.</p> - -<p>If you already have a Mesos cluster running, you can skip this Mesos installation step.</p> - -<p>Otherwise, installing Mesos for Spark is no different than installing Mesos for use by other -frameworks. You can install Mesos either from source or using prebuilt packages.</p> - -<h2 id="from-source">From Source</h2> - -<p>To install Apache Mesos from source, follow these steps:</p> - -<ol> - <li>Download a Mesos release from a -<a href="http://www.apache.org/dyn/closer.lua/mesos/0.21.0/">mirror</a></li> - <li>Follow the Mesos <a href="http://mesos.apache.org/gettingstarted">Getting Started</a> page for compiling and -installing Mesos</li> -</ol> - -<p><strong>Note:</strong> If you want to run Mesos without installing it into the default paths on your system -(e.g., if you lack administrative privileges to install it), pass the -<code>--prefix</code> option to <code>configure</code> to tell it where to install. For example, pass -<code>--prefix=/home/me/mesos</code>. By default the prefix is <code>/usr/local</code>.</p> - -<h2 id="third-party-packages">Third-Party Packages</h2> - -<p>The Apache Mesos project only publishes source releases, not binary packages. But other -third party projects publish binary releases that may be helpful in setting Mesos up.</p> - -<p>One of those is Mesosphere. To install Mesos using the binary releases provided by Mesosphere:</p> - -<ol> - <li>Download Mesos installation package from <a href="http://mesosphere.io/downloads/">downloads page</a></li> - <li>Follow their instructions for installation and configuration</li> -</ol> - -<p>The Mesosphere installation documents suggest setting up ZooKeeper to handle Mesos master failover, -but Mesos can be run without ZooKeeper using a single master as well.</p> - -<h2 id="verification">Verification</h2> - -<p>To verify that the Mesos cluster is ready for Spark, navigate to the Mesos master webui at port -<code>:5050</code> Confirm that all expected machines are present in the slaves tab.</p> - -<h1 id="connecting-spark-to-mesos">Connecting Spark to Mesos</h1> - -<p>To use Mesos from Spark, you need a Spark binary package available in a place accessible by Mesos, and -a Spark driver program configured to connect to Mesos.</p> - -<p>Alternatively, you can also install Spark in the same location in all the Mesos slaves, and configure -<code>spark.mesos.executor.home</code> (defaults to SPARK_HOME) to point to that location.</p> - -<h2 id="uploading-spark-package">Uploading Spark Package</h2> - -<p>When Mesos runs a task on a Mesos slave for the first time, that slave must have a Spark binary -package for running the Spark Mesos executor backend. -The Spark package can be hosted at any Hadoop-accessible URI, including HTTP via <code>http://</code>, -<a href="http://aws.amazon.com/s3">Amazon Simple Storage Service</a> via <code>s3n://</code>, or HDFS via <code>hdfs://</code>.</p> - -<p>To use a precompiled package:</p> - -<ol> - <li>Download a Spark binary package from the Spark <a href="https://spark.apache.org/downloads.html">download page</a></li> - <li>Upload to hdfs/http/s3</li> -</ol> - -<p>To host on HDFS, use the Hadoop fs put command: <code>hadoop fs -put spark-1.5.0.tar.gz -/path/to/spark-1.5.0.tar.gz</code></p> - -<p>Or if you are using a custom-compiled version of Spark, you will need to create a package using -the <code>make-distribution.sh</code> script included in a Spark source tarball/checkout.</p> - -<ol> - <li>Download and build Spark using the instructions <a href="index.html">here</a></li> - <li>Create a binary package using <code>make-distribution.sh --tgz</code>.</li> - <li>Upload archive to http/s3/hdfs</li> -</ol> - -<h2 id="using-a-mesos-master-url">Using a Mesos Master URL</h2> - -<p>The Master URLs for Mesos are in the form <code>mesos://host:5050</code> for a single-master Mesos -cluster, or <code>mesos://zk://host:2181</code> for a multi-master Mesos cluster using ZooKeeper.</p> - -<h2 id="client-mode">Client Mode</h2> - -<p>In client mode, a Spark Mesos framework is launched directly on the client machine and waits for the driver output.</p> - -<p>The driver needs some configuration in <code>spark-env.sh</code> to interact properly with Mesos:</p> - -<ol> - <li>In <code>spark-env.sh</code> set some environment variables: - <ul> - <li><code>export MESOS_NATIVE_JAVA_LIBRARY=<path to libmesos.so></code>. This path is typically -<code><prefix>/lib/libmesos.so</code> where the prefix is <code>/usr/local</code> by default. See Mesos installation -instructions above. On Mac OS X, the library is called <code>libmesos.dylib</code> instead of -<code>libmesos.so</code>.</li> - <li><code>export SPARK_EXECUTOR_URI=<URL of spark-1.5.0.tar.gz uploaded above></code>.</li> - </ul> - </li> - <li>Also set <code>spark.executor.uri</code> to <code><URL of spark-1.5.0.tar.gz></code>.</li> -</ol> - -<p>Now when starting a Spark application against the cluster, pass a <code>mesos://</code> -URL as the master when creating a <code>SparkContext</code>. For example:</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="o">.</span><span class="n">setMaster</span><span class="o">(</span><span class="s">"mesos://HOST:5050"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setAppName</span><span class="o">(</span><span class="s">"My app"</span><span class="o">)</span> - <span class="o">.</span><span class="n">set</span><span class="o">(</span><span class="s">"spark.executor.uri"</span><span class="o">,</span> <span class="s">"<path to spark-1.5.0.tar.gz uploaded above>"</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> - -<p>(You can also use <a href="submitting-applications.html"><code>spark-submit</code></a> and configure <code>spark.executor.uri</code> -in the <a href="configuration.html#loading-default-configurations">conf/spark-defaults.conf</a> file.)</p> - -<p>When running a shell, the <code>spark.executor.uri</code> parameter is inherited from <code>SPARK_EXECUTOR_URI</code>, so -it does not need to be redundantly passed in as a system property.</p> - -<div class="highlight"><pre><code class="language-bash" data-lang="bash">./bin/spark-shell --master mesos://host:5050</code></pre></div> - -<h2 id="cluster-mode">Cluster mode</h2> - -<p>Spark on Mesos also supports cluster mode, where the driver is launched in the cluster and the client -can find the results of the driver from the Mesos Web UI.</p> - -<p>To use cluster mode, you must start the MesosClusterDispatcher in your cluster via the <code>sbin/start-mesos-dispatcher.sh</code> script, -passing in the Mesos master url (e.g: mesos://host:5050).</p> - -<p>From the client, you can submit a job to Mesos cluster by running <code>spark-submit</code> and specifying the master url -to the url of the MesosClusterDispatcher (e.g: mesos://dispatcher:7077). You can view driver statuses on the -Spark cluster Web UI.</p> - -<h1 id="mesos-run-modes">Mesos Run Modes</h1> - -<p>Spark can run over Mesos in two modes: “fine-grained” (default) and “coarse-grained”.</p> - -<p>In “fine-grained” mode (default), each Spark task runs as a separate Mesos task. This allows -multiple instances of Spark (and other frameworks) to share machines at a very fine granularity, -where each application gets more or fewer machines as it ramps up and down, but it comes with an -additional overhead in launching each task. This mode may be inappropriate for low-latency -requirements like interactive queries or serving web requests.</p> - -<p>The “coarse-grained” mode will instead launch only <em>one</em> long-running Spark task on each Mesos -machine, and dynamically schedule its own “mini-tasks” within it. The benefit is much lower startup -overhead, but at the cost of reserving the Mesos resources for the complete duration of the -application.</p> - -<p>To run in coarse-grained mode, set the <code>spark.mesos.coarse</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.mesos.coarse"</span><span class="o">,</span> <span class="s">"true"</span><span class="o">)</span></code></pre></div> - -<p>In addition, for coarse-grained mode, you can control the maximum number of resources Spark will -acquire. By default, it will acquire <em>all</em> cores in the cluster (that get offered by Mesos), which -only makes sense if you run just one application at a time. You can cap the maximum number of cores -using <code>conf.set("spark.cores.max", "10")</code> (for example).</p> - -<p>You may also make use of <code>spark.mesos.constraints</code> to set attribute based constraints on mesos resource offers. By default, all resource offers will be accepted.</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.mesos.constraints"</span><span class="o">,</span> <span class="s">"tachyon=true;us-east-1=false"</span><span class="o">)</span></code></pre></div> - -<p>For example, Let’s say <code>spark.mesos.constraints</code> is set to <code>tachyon=true;us-east-1=false</code>, then the resource offers will be checked to see if they meet both these constraints and only then will be accepted to start new executors.</p> - -<h1 id="mesos-docker-support">Mesos Docker Support</h1> - -<p>Spark can make use of a Mesos Docker containerizer by setting the property <code>spark.mesos.executor.docker.image</code> -in your <a href="configuration.html#spark-properties">SparkConf</a>.</p> - -<p>The Docker image used must have an appropriate version of Spark already part of the image, or you can -have Mesos download Spark via the usual methods.</p> - -<p>Requires Mesos version 0.20.1 or later.</p> - -<h1 id="running-alongside-hadoop">Running Alongside Hadoop</h1> - -<p>You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them as a -separate service on the machines. To access Hadoop data from Spark, a full <code>hdfs://</code> URL is required -(typically <code>hdfs://<namenode>:9000/path</code>, but you can find the right URL on your Hadoop Namenode web -UI).</p> - -<p>In addition, it is possible to also run Hadoop MapReduce on Mesos for better resource isolation and -sharing between the two. In this case, Mesos will act as a unified scheduler that assigns cores to -either Hadoop or Spark, as opposed to having them share resources via the Linux scheduler on each -node. Please refer to <a href="https://github.com/mesos/hadoop">Hadoop on Mesos</a>.</p> - -<p>In either case, HDFS runs separately from Hadoop MapReduce, without being scheduled through Mesos.</p> - -<h1 id="dynamic-resource-allocation-with-mesos">Dynamic Resource Allocation with Mesos</h1> - -<p>Mesos supports dynamic allocation only with coarse grain mode, which can resize the number of executors based on statistics -of the application. While dynamic allocation supports both scaling up and scaling down the number of executors, the coarse grain scheduler only supports scaling down -since it is already designed to run one executor per slave with the configured amount of resources. However, after scaling down the number of executors the coarse grain scheduler -can scale back up to the same amount of executors when Spark signals more executors are needed.</p> - -<p>Users that like to utilize this feature should launch the Mesos Shuffle Service that -provides shuffle data cleanup functionality on top of the Shuffle Service since Mesos doesn’t yet support notifying another framework’s -termination. To launch/stop the Mesos Shuffle Service please use the provided sbin/start-mesos-shuffle-service.sh and sbin/stop-mesos-shuffle-service.sh -scripts accordingly.</p> - -<p>The Shuffle Service is expected to be running on each slave node that will run Spark executors. One way to easily achieve this with Mesos -is to launch the Shuffle Service with Marathon with a unique host constraint.</p> - -<h1 id="configuration">Configuration</h1> - -<p>See the <a href="configuration.html">configuration page</a> for information on Spark configurations. The following configs are specific for Spark on Mesos.</p> - -<h4 id="spark-properties">Spark Properties</h4> - -<table class="table"> -<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr> -<tr> - <td><code>spark.mesos.coarse</code></td> - <td>false</td> - <td> - If set to "true", runs over Mesos clusters in - <a href="running-on-mesos.html#mesos-run-modes">"coarse-grained" sharing mode</a>, - where Spark acquires one long-lived Mesos task on each machine instead of one Mesos task per - Spark task. This gives lower-latency scheduling for short queries, but leaves resources in use - for the whole duration of the Spark job. - </td> -</tr> -<tr> - <td><code>spark.mesos.extra.cores</code></td> - <td>0</td> - <td> - Set the extra amount of cpus to request per task. This setting is only used for Mesos coarse grain mode. - The total amount of cores requested per task is the number of cores in the offer plus the extra cores configured. - Note that total amount of cores the executor will request in total will not exceed the spark.cores.max setting. - </td> -</tr> -<tr> - <td><code>spark.mesos.mesosExecutor.cores</code></td> - <td>1.0</td> - <td> - (Fine-grained mode only) Number of cores to give each Mesos executor. This does not - include the cores used to run the Spark tasks. In other words, even if no Spark task - is being run, each Mesos executor will occupy the number of cores configured here. - The value can be a floating point number. - </td> -</tr> -<tr> - <td><code>spark.mesos.executor.docker.image</code></td> - <td>(none)</td> - <td> - Set the name of the docker image that the Spark executors will run in. The selected - image must have Spark installed, as well as a compatible version of the Mesos library. - The installed path of Spark in the image can be specified with <code>spark.mesos.executor.home</code>; - the installed path of the Mesos library can be specified with <code>spark.executorEnv.MESOS_NATIVE_LIBRARY</code>. - </td> -</tr> -<tr> - <td><code>spark.mesos.executor.docker.volumes</code></td> - <td>(none)</td> - <td> - Set the list of volumes which will be mounted into the Docker image, which was set using - <code>spark.mesos.executor.docker.image</code>. The format of this property is a comma-separated list of - mappings following the form passed to <tt>docker run -v</tt>. That is they take the form: - - <pre>[host_path:]container_path[:ro|:rw]</pre> - </td> -</tr> -<tr> - <td><code>spark.mesos.executor.docker.portmaps</code></td> - <td>(none)</td> - <td> - Set the list of incoming ports exposed by the Docker image, which was set using - <code>spark.mesos.executor.docker.image</code>. The format of this property is a comma-separated list of - mappings which take the form: - - <pre>host_port:container_port[:tcp|:udp]</pre> - </td> -</tr> -<tr> - <td><code>spark.mesos.executor.home</code></td> - <td>driver side <code>SPARK_HOME</code></td> - <td> - Set the directory in which Spark is installed on the executors in Mesos. By default, the - executors will simply use the driver's Spark home directory, which may not be visible to - them. Note that this is only relevant if a Spark binary package is not specified through - <code>spark.executor.uri</code>. - </td> -</tr> -<tr> - <td><code>spark.mesos.executor.memoryOverhead</code></td> - <td>executor memory * 0.10, with minimum of 384</td> - <td> - The amount of additional memory, specified in MB, to be allocated per executor. By default, - the overhead will be larger of either 384 or 10% of `spark.executor.memory`. If it's set, - the final overhead will be this value. - </td> -</tr> -<tr> - <td><code>spark.mesos.uris</code></td> - <td>(none)</td> - <td> - A list of URIs to be downloaded to the sandbox when driver or executor is launched by Mesos. - This applies to both coarse-grain and fine-grain mode. - </td> -</tr> -<tr> - <td><code>spark.mesos.principal</code></td> - <td>Framework principal to authenticate to Mesos</td> - <td> - Set the principal with which Spark framework will use to authenticate with Mesos. - </td> -</tr> -<tr> - <td><code>spark.mesos.secret</code></td> - <td>Framework secret to authenticate to Mesos</td> - <td> - Set the secret with which Spark framework will use to authenticate with Mesos. - </td> -</tr> -<tr> - <td><code>spark.mesos.role</code></td> - <td>Role for the Spark framework</td> - <td> - Set the role of this Spark framework for Mesos. Roles are used in Mesos for reservations - and resource weight sharing. - </td> -</tr> -<tr> - <td><code>spark.mesos.constraints</code></td> - <td>Attribute based constraints to be matched against when accepting resource offers.</td> - <td> - Attribute based constraints on mesos resource offers. By default, all resource offers will be accepted. Refer to <a href="http://mesos.apache.org/documentation/attributes-resources/">Mesos Attributes & Resources</a> for more information on attributes. - <ul> - <li>Scalar constraints are matched with "less than equal" semantics i.e. value in the constraint must be less than or equal to the value in the resource offer.</li> - <li>Range constraints are matched with "contains" semantics i.e. value in the constraint must be within the resource offer's value.</li> - <li>Set constraints are matched with "subset of" semantics i.e. value in the constraint must be a subset of the resource offer's value.</li> - <li>Text constraints are metched with "equality" semantics i.e. value in the constraint must be exactly equal to the resource offer's value.</li> - <li>In case there is no value present as a part of the constraint any offer with the corresponding attribute will be accepted (without value check).</li> - </ul> - </td> -</tr> -</table> - -<h1 id="troubleshooting-and-debugging">Troubleshooting and Debugging</h1> - -<p>A few places to look during debugging:</p> - -<ul> - <li>Mesos master on port <code>:5050</code> - <ul> - <li>Slaves should appear in the slaves tab</li> - <li>Spark applications should appear in the frameworks tab</li> - <li>Tasks should appear in the details of a framework</li> - <li>Check the stdout and stderr of the sandbox of failed tasks</li> - </ul> - </li> - <li>Mesos logs - <ul> - <li>Master and slave logs are both in <code>/var/log/mesos</code> by default</li> - </ul> - </li> -</ul> - -<p>And common pitfalls:</p> - -<ul> - <li>Spark assembly not reachable/accessible - <ul> - <li>Slaves must be able to download the Spark binary package from the <code>http://</code>, <code>hdfs://</code> or <code>s3n://</code> URL you gave</li> - </ul> - </li> - <li>Firewall blocking communications - <ul> - <li>Check for messages about failed connections</li> - <li>Temporarily disable firewalls for debugging and then poke appropriate holes</li> - </ul> - </li> -</ul> - - - </div> <!-- /container --> - - <script src="js/vendor/jquery-1.8.0.min.js"></script> - <script src="js/vendor/bootstrap.min.js"></script> - <script src="js/vendor/anchor.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 ? 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