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, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for incremental computation and stream processing.
Get Spark from the downloads page of the project website. This documentation is for Spark version 3.3.0. 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 8/11/17, Scala 2.12/2.13, Python 3.7+ and R 3.5+. Java 8 prior to version 8u201 support is deprecated as of Spark 3.2.0. For the Scala API, Spark 3.3.0 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
--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
Spark also provides a Python API. To run Spark interactively in a Python interpreter, use
./bin/pyspark --master local
Example applications are also provided in Python. For example,
./bin/spark-submit examples/src/main/python/pi.py 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 --master local
Example applications are also provided in R. For example,
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:
- Standalone Deploy Mode: simplest way to deploy Spark on a private cluster
- Apache Mesos (deprecated)
- Hadoop YARN
Where to Go from Here
- Quick Start: a quick introduction to the Spark API; start here!
- RDD Programming Guide: overview of Spark basics - RDDs (core but old API), accumulators, and broadcast variables
- Spark SQL, Datasets, and DataFrames: processing structured data with relational queries (newer API than RDDs)
- Structured Streaming: processing structured data streams with relation queries (using Datasets and DataFrames, newer API than DStreams)
- Spark Streaming: processing data streams using DStreams (old API)
- MLlib: applying machine learning algorithms
- GraphX: processing graphs
- SparkR: processing data with Spark in R
- PySpark: processing data with Spark in Python
- Spark SQL CLI: processing data with SQL on the command line
- Spark Scala API (Scaladoc)
- Spark Java API (Javadoc)
- Spark Python API (Sphinx)
- Spark R API (Roxygen2)
- Spark SQL, Built-in Functions (MkDocs)
- Cluster Overview: overview of concepts and components when running on a cluster
- Submitting Applications: packaging and deploying applications
- Deployment modes:
- Amazon EC2: scripts that let you launch a cluster on EC2 in about 5 minutes
- Standalone Deploy Mode: launch a standalone cluster quickly without a third-party cluster manager
- Mesos: deploy a private cluster using Apache Mesos
- YARN: deploy Spark on top of Hadoop NextGen (YARN)
- Kubernetes: deploy Spark on top of Kubernetes
- Configuration: customize Spark via its configuration system
- Monitoring: track the behavior of your applications
- Tuning Guide: best practices to optimize performance and memory use
- Job Scheduling: scheduling resources across and within Spark applications
- Security: Spark security support
- Hardware Provisioning: recommendations for cluster hardware
- Integration with other storage systems:
- Migration Guide: Migration guides for Spark components
- Building Spark: build Spark using the Maven system
- Contributing to Spark
- Third Party Projects: related third party Spark projects
- Spark Homepage
- Spark Community resources, including local meetups
- StackOverflow tag
- Mailing Lists: 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. Videos, are available online for free.
- Code Examples: more are also available in the
examplessubfolder of Spark (Scala, Java, Python, R)