Apache Spark is a fast and general-purpose cluster computing system. 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 Spark Streaming.
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 {{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 by augmenting Spark's classpath. Scala and Java users can include Spark in their projects using its Maven coordinates and in the future Python users can also 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). 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, Python 2.7+/3.4+ and R 3.5+. For the Scala API, Spark {{site.SPARK_VERSION}} uses Scala {{site.SCALA_BINARY_VERSION}}. You will need to use a compatible Scala version ({{site.SCALA_BINARY_VERSION}}.x).
Note that support for Java 7, Python 2.6 and old Hadoop versions before 2.6.5 were removed as of Spark 2.2.0. Support for Scala 2.10 was removed as of 2.3.0. Support for Scala 2.11 is deprecated as of Spark 2.4.1 and will be removed in Spark 3.0.
For Java 8u251+, HTTP2_DISABLE=true
and spark.kubernetes.driverEnv.HTTP2_DISABLE=true
are required additionally for fabric8 kubernetes-client
library to talk to Kubernetes clusters. This prevents KubernetesClientException
when kubernetes-client
library uses okhttp
library internally.
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 experimental R API since 1.4 (only DataFrames APIs included). To run Spark interactively in a 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
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:
Programming Guides:
API Docs:
Deployment Guides:
Other Documents:
External Resources:
apache-spark
examples
subfolder of Spark (Scala, Java, Python, R)