3.0.
Overview
Programming Guides
API Docs
Deploying
More
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
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.
Downloading
Get Spark from the downloads page of the project website. This documentation is for Spark version
3.0.1. 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 2.7+/3.4+ and R 3.5+. Java 8 prior to version 8u92
support is deprecated as of Spark 3.0.0. Python 2 and Python 3 prior to version 3.6 support is
deprecated as of Spark 3.0.0. For the Scala API, Spark 3.0.1 uses Scala 2.12. You will need to use
a compatible Scala version (2.12.x).
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/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:
./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:
Standalone Deploy Mode: simplest way to deploy Spark on a private cluster
Apache Mesos
Hadoop YARN
Kubernetes
Where to Go from Here
Programming Guides:
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
API Docs:
Spark Scala API (Scaladoc)
Spark Java API (Javadoc)
Spark Python API (Sphinx)
Spark R API (Roxygen2)
Spark SQL, Built-in Functions (MkDocs)
Deployment Guides:
Cluster Overview: overview of concepts and components when running on a cluster
Submitting Applications: packaging and deploying applications
Deployment modes:
o Amazon EC2: scripts that let you launch a cluster on EC2 in about 5 minutes
o Standalone Deploy Mode: launch a standalone cluster quickly without a third-
party cluster manager
o Mesos: deploy a private cluster using Apache Mesos
o YARN: deploy Spark on top of Hadoop NextGen (YARN)
o Kubernetes: deploy Spark on top of Kubernetes
Other Documents:
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:
o Cloud Infrastructures
o OpenStack Swift
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
External Resources:
Spark Homepage
Spark Community resources, including local meetups
StackOverflow tag apache-spark
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, slides and exercises are
available online for free.
Code Examples: more are also available in the examples subfolder of Spark
(Scala, Java, Python, R)
