OSCL-LXR spark-3.0.0/​docs/​cluster-overview.md	Source navigation Diff markup Identifier search General search
	Version: spark-3.0.0 hadoop-3.2.0-src hadoop-3.3.0-src spark-2.4.7 linux-4.15.13 Revert   Change

 ---
 layout: global
 title: Cluster Mode Overview
 license: |
   Licensed to the Apache Software Foundation (ASF) under one or more
   contributor license agreements.  See the NOTICE file distributed with
   this work for additional information regarding copyright ownership.
   The ASF licenses this file to You under the Apache License, Version 2.0
   (the "License"); you may not use this file except in compliance with
   the License.  You may obtain a copy of the License at
  
      http://www.apache.org/licenses/LICENSE-2.0
  
   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.
 ---
 
 This document gives a short overview of how Spark runs on clusters, to make it easier to understand
 the components involved. Read through the [application submission guide](submitting-applications.html)
 to learn about launching applications on a cluster.
 
 # Components
 
 Spark applications run as independent sets of processes on a cluster, coordinated by the `SparkContext`
 object in your main program (called the _driver program_).
 
 Specifically, to run on a cluster, the SparkContext can connect to several types of _cluster managers_
 (either Spark's own standalone cluster manager, Mesos or YARN), which allocate resources across
 applications. Once connected, Spark acquires *executors* 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 *tasks* to the executors to run.
 
 <p style="text-align: center;">
   <img src="img/cluster-overview.png" title="Spark cluster components" alt="Spark cluster components" />
 </p>
 
 There are several useful things to note about this architecture:
 
 1. 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.
 2. 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).
 3. The driver program must listen for and accept incoming connections from its executors throughout
    its lifetime (e.g., see [spark.driver.port in the network config
    section](configuration.html#networking)). As such, the driver program must be network
    addressable from the worker nodes.
 4. 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.
 
 # Cluster Manager Types
 
 The system currently supports several cluster managers:
 
 * [Standalone](spark-standalone.html) -- a simple cluster manager included with Spark that makes it
   easy to set up a cluster.
 * [Apache Mesos](running-on-mesos.html) -- a general cluster manager that can also run Hadoop MapReduce
   and service applications.
 * [Hadoop YARN](running-on-yarn.html) -- the resource manager in Hadoop 2.
 * [Kubernetes](running-on-kubernetes.html) -- an open-source system for automating deployment, scaling,
   and management of containerized applications.
 
 A third-party project (not supported by the Spark project) exists to add support for
 [Nomad](https://github.com/hashicorp/nomad-spark) as a cluster manager.
 
 # Submitting Applications
 
 Applications can be submitted to a cluster of any type using the `spark-submit` script.
 The [application submission guide](submitting-applications.html) describes how to do this.
 
 # Monitoring
 
 Each driver program has a web UI, typically on port 4040, that displays information about running
 tasks, executors, and storage usage. Simply go to `http://<driver-node>:4040` in a web browser to
 access this UI. The [monitoring guide](monitoring.html) also describes other monitoring options.
 
 # Job Scheduling
 
 Spark gives control over resource allocation both _across_ applications (at the level of the cluster
 manager) and _within_ applications (if multiple computations are happening on the same SparkContext).
 The [job scheduling overview](job-scheduling.html) describes this in more detail.
 
 # Glossary
 
 The following table summarizes terms you'll see used to refer to cluster concepts:
 
 <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)</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>

This page was automatically generated by the 2.1.0 LXR engine. The LXR team