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apache / spark / refs/heads/master
commita0f62393d69a40ddd49b034b3ce332e6fa6bfb13[log] [tgz]
authorDongjoon Hyun <dhyun@apple.com>Sat May 04 22:55:04 2024 -0700
committerDongjoon Hyun <dhyun@apple.com>Sat May 04 22:55:04 2024 -0700
tree6fb000ad57003cb58ff9b9075109798f1bc2aa5a
parentb924e689942d735f165d31660d26efad057f4827 [diff]
[SPARK-48132][INFRA] Run `k8s-integration-tests` only in PR builder and Daily CIs

### What changes were proposed in this pull request?

This PR aims to run `k8s-integration-tests` only in PR builder and Daily Python CIs. In other words, only the commit builder will skip it by default.

Please note that
- K8s unit tests will be covered by the commit builder still.
- All PR builders are not consuming ASF resources and they provide lots of test coverage everyday also.

### Why are the changes needed?

To reduce GitHub Action usage to meet ASF INFRA policy.
- https://infra.apache.org/github-actions-policy.html

    > All workflows MUST have a job concurrency level less than or equal to 20. This means a workflow cannot have more than 20 jobs running at the same time across all matrices.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Manual review.

### Was this patch authored or co-authored using generative AI tooling?

No.

Closes #46388 from dongjoon-hyun/SPARK-48132.

Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
1 file changed
tree: 6fb000ad57003cb58ff9b9075109798f1bc2aa5a
  1. .github/
  2. assembly/
  3. bin/
  4. binder/
  5. build/
  6. common/
  7. conf/
  8. connector/
  9. core/
  10. data/
  11. dev/
  12. docs/
  13. examples/
  14. graphx/
  15. hadoop-cloud/
  16. launcher/
  17. licenses/
  18. licenses-binary/
  19. mllib/
  20. mllib-local/
  21. project/
  22. python/
  23. R/
  24. repl/
  25. resource-managers/
  26. sbin/
  27. sql/
  28. streaming/
  29. tools/
  30. ui-test/
  31. .asf.yaml
  32. .gitattributes
  33. .gitignore
  34. CONTRIBUTING.md
  35. LICENSE
  36. LICENSE-binary
  37. NOTICE
  38. NOTICE-binary
  39. pom.xml
  40. README.md
  41. scalastyle-config.xml
README.md

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, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

https://spark.apache.org/

GitHub Actions Build PySpark Coverage PyPI Downloads

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 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.