Spark Overview

Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for incremental computation and stream processing.


Security in Spark is OFF by default. This could mean you are vulnerable to attack by default. Please see Spark Security before downloading and running Spark.


Get Spark from the downloads page of the project website. This documentation is for Spark version 3.1.1. 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 by augmenting Spark’s classpath. 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 Building Spark.

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 x86_64 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 JAVA_HOME environment variable pointing to a Java installation.

Spark runs on Java 8/11, Scala 2.12, Python 3.6+ and R 3.5+. Java 8 prior to version 8u92 support is deprecated as of Spark 3.0.0. For the Scala API, Spark 3.1.1 uses Scala 2.12. You will need to use a compatible Scala version (2.12.x).

For Python 3.9, Arrow optimization and pandas UDFs might not work due to the supported Python versions in Apache Arrow. Please refer to the latest Python Compatibility page. For Java 11, -Dio.netty.tryReflectionSetAccessible=true is required additionally for Apache Arrow library. This prevents java.lang.UnsupportedOperationException: sun.misc.Unsafe or java.nio.DirectByteBuffer.(long, int) not available when Apache Arrow uses Netty internally.

Running the Examples and Shell

Spark comes with several sample programs. Scala, Java, Python and R examples are in the examples/src/main directory. To run one of the Java or Scala sample programs, use bin/run-example <class> [params] in the top-level Spark directory. (Behind the scenes, this invokes the more general spark-submit script 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 master URL for a distributed cluster, or local to run locally with one thread, or local[N] to run locally with N threads. You should start by using local for testing. For a full list of options, run Spark shell with the --help option.

Spark also provides a Python API. To run Spark interactively in a Python interpreter, use bin/pyspark:

./bin/pyspark --master local[2]

Example applications are also provided in Python. For example,

./bin/spark-submit examples/src/main/python/ 10

Spark also provides an R API since 1.4 (only DataFrames APIs 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

Launching on a Cluster

The Spark cluster mode overview 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:

Where to Go from Here

Programming Guides:

API Docs:

Deployment Guides:

Other Documents:

External Resources: