| commit | 7c728b2c2d6cbe2b8310cd8cf8aa4c2528b489c8 | [log] [tgz] |
|---|---|---|
| author | Dongjoon Hyun <dhyun@apple.com> | Sun May 05 23:01:01 2024 -0700 |
| committer | Dongjoon Hyun <dhyun@apple.com> | Sun May 05 23:01:01 2024 -0700 |
| tree | 8bcfc2102a3f343ea96a647cceb331b0d52b2f84 | |
| parent | 8294c5962febe53eebdff79f65f5f293d93a1997 [diff] |
[SPARK-48137][INFRA] Run `yarn` test only in PR builders and Daily CIs ### What changes were proposed in this pull request? We have been providing a dedicated test environment for `yarn` and `connect` module because they are flaky. - #45107 However, they are still flaky. So, this PR aims to run `yarn` test only in PR builders (if needed) and Daily CIs (always). - Reduce the irrelevant re-tries by triggering `YARN CI` only when we need to test `YARN` module. - Protect YARN CI from `connect` flakiness by providing an independent GitHub Action environment in PR Builders and Daily CIs. - Lastly, commit builder will offload YARN module tests to the daily CIs ### Why are the changes needed? - PR builders provide an extensive test coverage with YARN testing. - Daily CIs with YARN tests - NON-ANSI CI: https://github.com/apache/spark/actions/workflows/build_non_ansi.yml (1AM) - Java 21 SBT CI: https://github.com/apache/spark/actions/workflows/build_java21.yml (4AM) - RockDB UI CI: https://github.com/apache/spark/actions/workflows/build_rockdb_as_ui_backend.yml (6AM) - Maven Java 17 CI: https://github.com/apache/spark/actions/workflows/build_maven.yml (1PM) - Maven Java 21 CI: https://github.com/apache/spark/actions/workflows/build_maven_java21.yml (2PM) - Maven Java 21 on AppleSilicon CI: https://github.com/apache/spark/actions/workflows/build_maven_java21_macos14.yml (8PM every two days) - YARN CI has been flaky in GitHub Action environment and requires irrelevant re-tries very frequently. - https://github.com/apache/spark/actions/runs/8962451417/job/24611353908 (2024-05-05) - https://github.com/apache/spark/actions/runs/8962440192/job/24611326971 (2024-05-05) ``` [info] *** 6 TESTS FAILED *** [error] Failed tests: [error] org.apache.spark.deploy.yarn.YarnClusterSuite [error] (yarn / Test / test) sbt.TestsFailedException: Tests unsuccessful ``` <img width="544" alt="Screenshot 2024-05-05 at 20 12 28" src="https://github.com/apache/spark/assets/9700541/cbf9fb03-fc4c-4513-b5e5-158c3c9a085a"> ### 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 #46395 from dongjoon-hyun/SPARK-48137. Authored-by: Dongjoon Hyun <dhyun@apple.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
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.
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.)
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”.
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()
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()
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.
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
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.
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.
