| commit | 26c871f180306fbf86ce65f14f8e7a71f89885ed | [log] [tgz] |
|---|---|---|
| author | Dongjoon Hyun <dhyun@apple.com> | Wed May 01 10:42:26 2024 -0700 |
| committer | Dongjoon Hyun <dhyun@apple.com> | Wed May 01 10:42:26 2024 -0700 |
| tree | 0bb87c103992635fac30d84a8ccd0436b02fd765 | |
| parent | ff401dde50343c9bbc1c49a0294272f2da7d01e2 [diff] |
[SPARK-48068][PYTHON] `mypy` should have `--python-executable` parameter ### What changes were proposed in this pull request? This PR aims to fix `mypy` failure by propagating `lint-python`'s `PYTHON_EXECUTABLE` to `mypy`'s parameter correctly. ### Why are the changes needed? We assumed that `PYTHON_EXECUTABLE` is used for `dev/lint-python` like the following. That's not always guaranteed. We need to use `mypy`'s parameter to make it sure. https://github.com/apache/spark/blob/ff401dde50343c9bbc1c49a0294272f2da7d01e2/.github/workflows/build_and_test.yml#L705 This patch is useful whose `python3` chooses one of multiple Python installation like our CI environment. ``` $ docker run -it --rm ghcr.io/apache/apache-spark-ci-image:master-8905641334 bash WARNING: The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested root2ef6ce08d2c4:/# python3 --version Python 3.10.12 root2ef6ce08d2c4:/# python3.9 --version Python 3.9.19 ``` For example, the following shows that `PYTHON_EXECUTABLE` is not considered by `mypy`. ``` root18c8eae5791e:/spark# PYTHON_EXECUTABLE=python3.9 mypy --python-executable=python3.11 --namespace-packages --config-file python/mypy.ini python/pyspark | wc -l 3428 root18c8eae5791e:/spark# PYTHON_EXECUTABLE=python3.9 mypy --namespace-packages --config-file python/mypy.ini python/pyspark | wc -l 1 root18c8eae5791e:/spark# PYTHON_EXECUTABLE=python3.11 mypy --namespace-packages --config-file python/mypy.ini python/pyspark | wc -l 1 ``` ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Pass the CIs. ### Was this patch authored or co-authored using generative AI tooling? No. Closes #46314 from dongjoon-hyun/SPARK-48068. 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.
