Apache Spark是一种快速和通用的集群计算系统。 它提供Java、Scala、Python和R中的高级API，以及支持通用执行图的优化引擎。 它还支持一系列更高级的工具，包括用于SQL和结构化数据处理的Spark SQL，用于机器学习的MLlib，用于图形处理的GraphX和Spark Streaming。
Apache Spark
Apache Spark™是用于大规模数据处理的快速和通用引擎。(Apache Spark™ is a fast and general engine for large-scale data processing. )
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Name With Owner apache/spark Primary Language Scala Program language Scala (Language Count: 21) Platform License: Apache License 2.0 - 所有者活动
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Created At 2014-02-25 08:00:08 Pushed At 2025-03-22 16:05:48 Last Commit At 2025-03-22 19:05:36 Release Count 248 Last Release Name v4.0.0-rc3 (Posted on ) First Release Name alpha-0.1 (Posted on ) - 用户参与
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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.
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.