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|Last commit||over 1 year ago|
|Repo Created||over 5 years ago|
|Repo Last Updated||over 1 year ago|
|Organization / Author||apache|
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It's blazing fast.
Modern Big data framework
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
Spark is built using Apache Maven. To build Spark and its example programs, run:
build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
For general development tips, including info on developing Spark using an IDE, see
Useful Developer Tools.
The easiest way to start using Spark is through the Scala shell:
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
Alternatively, if you prefer Python, you can use the Python shell:
And run the following command, which should also return 1000:
Spark also comes with several sample programs in the
To run one of them, use
./bin/run-example <class> [params]. For example:
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
yarn to run on YARN, and
local to run
locally with one thread, or
local[N] to run locally with N threads. You
can also use an abbreviated class name if the class is in the
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Testing first requires building Spark. Once Spark is built, tests can be run using:
Please see the guidance on how to run tests for a module, or individual tests.
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at
Specifying the Hadoop Version
for detailed guidance on building for a particular distribution of Hadoop, including
building for particular Hive and Hive Thriftserver distributions.
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Please review the Contribution to Spark guide for information on how to get started contributing to the project.