73d3b230f3
## What changes were proposed in this pull request? Docs changes: - Adding a warning that the backend is experimental. - Removing a defunct internal-only option from documentation - Clarifying that node selectors can be used right away, and other minor cosmetic changes ## How was this patch tested? Docs only change Author: foxish <ramanathana@google.com> Closes #20314 from foxish/ambiguous-docs.
138 lines
6.4 KiB
Markdown
138 lines
6.4 KiB
Markdown
---
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layout: global
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title: Cluster Mode Overview
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---
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This document gives a short overview of how Spark runs on clusters, to make it easier to understand
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the components involved. Read through the [application submission guide](submitting-applications.html)
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to learn about launching applications on a cluster.
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# Components
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Spark applications run as independent sets of processes on a cluster, coordinated by the `SparkContext`
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object in your main program (called the _driver program_).
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Specifically, to run on a cluster, the SparkContext can connect to several types of _cluster managers_
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(either Spark's own standalone cluster manager, Mesos or YARN), which allocate resources across
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applications. Once connected, Spark acquires *executors* on nodes in the cluster, which are
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processes that run computations and store data for your application.
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Next, it sends your application code (defined by JAR or Python files passed to SparkContext) to
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the executors. Finally, SparkContext sends *tasks* to the executors to run.
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<p style="text-align: center;">
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<img src="img/cluster-overview.png" title="Spark cluster components" alt="Spark cluster components" />
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</p>
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There are several useful things to note about this architecture:
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1. Each application gets its own executor processes, which stay up for the duration of the whole
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application and run tasks in multiple threads. This has the benefit of isolating applications
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from each other, on both the scheduling side (each driver schedules its own tasks) and executor
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side (tasks from different applications run in different JVMs). However, it also means that
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data cannot be shared across different Spark applications (instances of SparkContext) without
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writing it to an external storage system.
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2. Spark is agnostic to the underlying cluster manager. As long as it can acquire executor
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processes, and these communicate with each other, it is relatively easy to run it even on a
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cluster manager that also supports other applications (e.g. Mesos/YARN).
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3. The driver program must listen for and accept incoming connections from its executors throughout
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its lifetime (e.g., see [spark.driver.port in the network config
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section](configuration.html#networking)). As such, the driver program must be network
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addressable from the worker nodes.
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4. Because the driver schedules tasks on the cluster, it should be run close to the worker
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nodes, preferably on the same local area network. If you'd like to send requests to the
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cluster remotely, it's better to open an RPC to the driver and have it submit operations
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from nearby than to run a driver far away from the worker nodes.
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# Cluster Manager Types
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The system currently supports three cluster managers:
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* [Standalone](spark-standalone.html) -- a simple cluster manager included with Spark that makes it
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easy to set up a cluster.
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* [Apache Mesos](running-on-mesos.html) -- a general cluster manager that can also run Hadoop MapReduce
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and service applications.
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* [Hadoop YARN](running-on-yarn.html) -- the resource manager in Hadoop 2.
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* [Kubernetes](running-on-kubernetes.html) -- an open-source system for automating deployment, scaling,
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and management of containerized applications.
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A third-party project (not supported by the Spark project) exists to add support for
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[Nomad](https://github.com/hashicorp/nomad-spark) as a cluster manager.
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# Submitting Applications
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Applications can be submitted to a cluster of any type using the `spark-submit` script.
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The [application submission guide](submitting-applications.html) describes how to do this.
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# Monitoring
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Each driver program has a web UI, typically on port 4040, that displays information about running
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tasks, executors, and storage usage. Simply go to `http://<driver-node>:4040` in a web browser to
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access this UI. The [monitoring guide](monitoring.html) also describes other monitoring options.
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# Job Scheduling
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Spark gives control over resource allocation both _across_ applications (at the level of the cluster
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manager) and _within_ applications (if multiple computations are happening on the same SparkContext).
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The [job scheduling overview](job-scheduling.html) describes this in more detail.
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# Glossary
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The following table summarizes terms you'll see used to refer to cluster concepts:
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<table class="table">
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<thead>
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<tr><th style="width: 130px;">Term</th><th>Meaning</th></tr>
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</thead>
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<tbody>
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<tr>
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<td>Application</td>
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<td>User program built on Spark. Consists of a <em>driver program</em> and <em>executors</em> on the cluster.</td>
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</tr>
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<tr>
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<td>Application jar</td>
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<td>
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A jar containing the user's Spark application. In some cases users will want to create
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an "uber jar" containing their application along with its dependencies. The user's jar
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should never include Hadoop or Spark libraries, however, these will be added at runtime.
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</td>
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</tr>
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<tr>
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<td>Driver program</td>
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<td>The process running the main() function of the application and creating the SparkContext</td>
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</tr>
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<tr>
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<td>Cluster manager</td>
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<td>An external service for acquiring resources on the cluster (e.g. standalone manager, Mesos, YARN)</td>
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</tr>
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<tr>
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<td>Deploy mode</td>
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<td>Distinguishes where the driver process runs. In "cluster" mode, the framework launches
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the driver inside of the cluster. In "client" mode, the submitter launches the driver
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outside of the cluster.</td>
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</tr>
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<tr>
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<td>Worker node</td>
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<td>Any node that can run application code in the cluster</td>
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</tr>
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<tr>
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<td>Executor</td>
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<td>A process launched for an application on a worker node, that runs tasks and keeps data in memory
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or disk storage across them. Each application has its own executors.</td>
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</tr>
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<tr>
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<td>Task</td>
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<td>A unit of work that will be sent to one executor</td>
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</tr>
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<tr>
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<td>Job</td>
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<td>A parallel computation consisting of multiple tasks that gets spawned in response to a Spark action
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(e.g. <code>save</code>, <code>collect</code>); you'll see this term used in the driver's logs.</td>
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</tr>
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<tr>
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<td>Stage</td>
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<td>Each job gets divided into smaller sets of tasks called <em>stages</em> that depend on each other
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(similar to the map and reduce stages in MapReduce); you'll see this term used in the driver's logs.</td>
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</tr>
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</tbody>
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</table>
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