名称与所有者	apache/spark
主编程语言	Scala
编程语言	Scala (语言数: 21)
平台
许可证	Apache License 2.0

创建于	2014-02-25 08:00:08
推送于	2025-03-22 16:05:48
最后一次提交	2025-03-22 19:05:36
发布数	248
最新版本名称	v4.0.0-rc3 (发布于 )
第一版名称	alpha-0.1 (发布于 )

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Apache Spark

Spark is a unified analytics engine for large-scale data processing. 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 Structured Streaming for stream processing.

https://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming
guide, on the project web page.
This README file only contains basic setup instructions.

Building Spark

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.)

More detailed documentation is available from the project site, at
"Building Spark".

For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".

Interactive Scala Shell

The easiest way to start using Spark is through the Scala shell:

./bin/spark-shell

Try the following command, which should return 1,000,000,000:

scala> spark.range(1000 * 1000 * 1000).count()

Interactive Python Shell

Alternatively, if you prefer Python, you can use the Python shell:

./bin/pyspark

And run the following command, which should also return 1,000,000,000:

>>> spark.range(1000 * 1000 * 1000).count()

Example Programs

Spark also comes with several sample programs in the examples directory.
To run one of them, use ./bin/run-example <class> [params]. For example:

./bin/run-example SparkPi

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 examples
package. For instance:

MASTER=spark://host:7077 ./bin/run-example SparkPi

Many of the example programs print usage help if no params are given.

Running Tests

Testing first requires building Spark. Once Spark is built, tests
can be run using:

./dev/run-tests

Please see the guidance on how to
run tests for a module, or individual tests.

There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md

A Note About Hadoop Versions

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 and Enabling YARN"
for detailed guidance on building for a particular distribution of Hadoop, including
building for particular Hive and Hive Thriftserver distributions.

Configuration

Please refer to the Configuration Guide
in the online documentation for an overview on how to configure Spark.

Contributing

Please review the Contribution to Spark guide
for information on how to get started contributing to the project.

Apache Spark

Github星跟踪图

主要指标

Apache Spark

Online Documentation

Building Spark

Interactive Scala Shell

Interactive Python Shell

Example Programs

Running Tests

A Note About Hadoop Versions

Configuration

Contributing