Apache Spark - A Unified engine for large-scale data analytics

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.

Downloading

Get Spark from the downloads page of the project website. This documentation is for Spark version 4.0.0-preview1. 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 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.

Running the Examples and Shell

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 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 the Spark shell with the --help option.

Since version 1.4, Spark has provided an R API (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

Running Spark Client Applications Anywhere with Spark Connect

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 Spark Connect Overview.

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: