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| <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. Read through the <a href="submitting-applications.html">application submission guide</a> |
| to learn about launching applications on a cluster.</p> |
| |
| <h1 id="components">Components</h1> |
| |
| <p>Spark applications run as independent sets of processes on a cluster, coordinated by the <code class="language-plaintext highlighter-rouge">SparkContext</code> |
| object in your main program (called the <em>driver program</em>).</p> |
| |
| <p>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, Mesos, YARN or Kubernetes), which allocate resources across |
| applications. Once connected, Spark acquires <em>executors</em> on nodes in the cluster, which are |
| 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> to 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/Kubernetes).</li> |
| <li>The driver program must listen for and accept incoming connections from its executors throughout |
| its lifetime (e.g., see <a href="configuration.html#networking">spark.driver.port in the network config |
| section</a>). As such, the driver program must be network |
| addressable from the worker nodes.</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 several 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. (Deprecated)</li> |
| <li><a href="running-on-yarn.html">Hadoop YARN</a> – the resource manager in Hadoop 2 and 3.</li> |
| <li><a href="running-on-kubernetes.html">Kubernetes</a> – an open-source system for automating deployment, scaling, |
| and management of containerized applications.</li> |
| </ul> |
| |
| <h1 id="submitting-applications">Submitting Applications</h1> |
| |
| <p>Applications can be submitted to a cluster of any type using the <code class="language-plaintext highlighter-rouge">spark-submit</code> script. |
| The <a href="submitting-applications.html">application submission guide</a> describes how to do this.</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 class="language-plaintext highlighter-rouge">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>Application jar</td> |
| <td> |
| A jar containing the user's Spark application. In some cases users will want to create |
| an "uber jar" containing their application along with its dependencies. The user's jar |
| should never include Hadoop or Spark libraries, however, these will be added at runtime. |
| </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, Kubernetes)</td> |
| </tr> |
| <tr> |
| <td>Deploy mode</td> |
| <td>Distinguishes where the driver process runs. In "cluster" mode, the framework launches |
| the driver inside of the cluster. In "client" mode, the submitter launches the driver |
| outside of the cluster.</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> |
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