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.
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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 2.4.4. 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 versionby 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 runlocally 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 3.1+. For the Scala API, Spark 2.4.4uses Scala 2.12. You will need to use a compatible Scala version(2.12.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 2.4.1and will be removed in Spark 3.0.
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, usebin/run-example <class> [params] in the top-level Spark directory. (Behind the scenes, thisinvokes the more generalspark-submit script forlaunching applications). For example,
You can also run Spark interactively through a modified version of the Scala shell. This is agreat way to learn the framework.
The
--master option specifies themaster URL for a distributed cluster, or local to runlocally with one thread, or local[N] to run locally with N threads. You should start by usinglocal 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 :
Example applications are also provided in Python. For example,
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 :
Example applications are also provided in R. For example,
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 severaloptions for deployment:
Programming Guides:
API Docs:
Deployment Guides:
Other Documents:
External Resources:
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