Project: Apache Spark (App Definition and Development / Streaming & Messaging)
File: index.md

layout: global displayTitle: Apache Spark Overview title: Overview description: Apache Spark SPARKVERSIONSHORT documentation homepage 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. Get Spark from the of the project website. This documentation is for Spark version {{site.SPARK_VERSION}}. Spark uses Hadoop's client libraries for HDFS and YARN. Downloads are pre-packaged for a handful of popular Hadoop versions. Users can also download a "Hadoop free" binary and run Spark with any Hadoop version . Scala and Java users can include Spark in their projects using its Maven coordinates and Python users can install Spark from PyPI. If you'd like to build Spark from source, visit . Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS), and it should run on any platform that runs a supported version of Java. This should include JVMs on x8664 and ARM64. It's easy to run locally on one machine all you need is to have `java` installed on your system `PATH`, or the `JAVAHOME` environment variable pointing to a Java installation. Spark runs on Java 17/21, Scala 2.13, Python 3.8+, and R 3.5+. When using the Scala API, it is necessary for applications to use the same version of Scala that Spark was compiled for. For example, when using Scala 2.13, use Spark compiled for 2.13, and compile code/applications for Scala 2.13 as well. Spark comes with several sample programs. Python, Scala, Java, and R examples are in the `examples/src/main` directory. To run Spark interactively in a Python interpreter, use `bin/pyspark`: ./bin/pyspark --master "local[2]" Sample applications are provided in Python. For example: ./bin/spark-submit examples/src/main/python/pi.py 10 To run one of the Scala or Java sample programs, use `bin/run-example <class> [params]` in the top-level Spark directory. (Behind the scenes, this invokes the more general for launching applications). For example, ./bin/run-example SparkPi 10 You can also run Spark interactively through a modified version of the Scala shell. This is a great way to learn the framework. ./bin/spark-shell --master "local[2]" The `--master` option specifies the , or `local` to run locally with one thread, or `local[N]` to run locally with N

You should start by using `local` for testing. For a full list of options, run the Spark shell with the `--help` option. Since version 1.4, Spark has provided an (only the DataFrame APIs are included). To run Spark interactively in an R interpreter, use `bin/sparkR`: ./bin/sparkR --master "local[2]" Example applications are also provided in R. For example: ./bin/spark-submit examples/src/main/r/dataframe.R Spark Connect is a new client-server architecture introduced in Spark 3.4 that decouples Spark client applications and allows remote connectivity to Spark clusters. The separation between client and server allows Spark and its open ecosystem to be leveraged from anywhere, embedded in any application. In Spark 3.4, Spark Connect provides DataFrame API coverage for PySpark and DataFrame/Dataset API support in Scala. To learn more about Spark Connect and how to use it, see . The Spark explains the key concepts in running on a cluster. Spark can run both by itself, or over several existing cluster managers. It currently provides several options for deployment: : simplest way to deploy Spark on a private cluster Programming Guides: : a quick introduction to the Spark API; start here! : overview of Spark basics - RDDs (core but old API), accumulators, and broadcast variables : processing structured data with relational queries (newer API than RDDs) : processing structured data streams with relation queries (using Datasets and DataFrames, newer API than DStreams) : processing data streams using DStreams (old API) : applying machine learning algorithms : processing graphs : processing data with Spark in R : processing data with Spark in Python : processing data with SQL on the command line API Docs: Deployment Guides: : overview of concepts and components when running on a cluster : packaging and deploying applications Deployment modes: : scripts that let you launch a cluster on EC2 in about 5 minutes : launch a standalone cluster quickly without a third-party cluster manager : deploy Spark on top of Hadoop NextGen (YARN) : deploy Spark on top of Kubernetes Other Documents: : customize Spark via its configuration system : track the behavior of your applications : view useful information about your applications : best practices to optimize performance and memory use : scheduling resources across and within Spark applications : Spark security support : recommendations for cluster hardware Integration with other storage systems: * : migration guides for Spark components : build Spark using the Maven system : related third party Spark projects External Resources: resources, including local meetups : ask questions about Spark here AMP Camps: a series of training camps at UC Berkeley that featured talks and exercises about Spark, Spark Streaming, Mesos, and more. , are available online for free. : more are also available in the `examples` subfolder of Spark (, , , )