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apache / spark / effd4d85a31284f3b6803d8ff293a452306014e2
commiteffd4d85a31284f3b6803d8ff293a452306014e2[log] [tgz]
authorRichard Chen <r.chen@databricks.com>Mon Jul 15 11:02:55 2024 +0900
committerHyukjin Kwon <gurwls223@apache.org>Mon Jul 15 11:02:55 2024 +0900
tree9885b95c122d8ae674e3e29fb5480bc0c01d3d93
parent206cc1a554ef130cfa0aceb1d4ee330e5c83de5f [diff]
[SPARK-48834][SQL] Disable variant input/output to python scalar UDFs, UDTFs, UDAFs during query compilation

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

Throws an exception if a variant is the input/output type to/from python UDF, UDAF, UDTF

### Why are the changes needed?

currently, variant input/output types to scalar UDFs will fail during execution or return a `net.razorvine.pickle.objects.ClassDictConstructor` to the user python code. For a better UX, we should fail during query compilation for failures, and block returning `ClassDictConstructor` to user code as we one day want to actually return `VariantVal`s to the user code.

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

yes - attempting to use variants in python UDFs will now throw an exception rather than returning a `ClassDictConstructor` as before. However, we want to make this change now as we one day want to be able to return `VariantVal`s to the user code and do not want users relying on this current behavior

### How was this patch tested?

added UTs

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

no

Closes #47253 from richardc-db/variant_scalar_udfs.

Authored-by: Richard Chen <r.chen@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
15 files changed
tree: 9885b95c122d8ae674e3e29fb5480bc0c01d3d93
  1. .github/
  2. assembly/
  3. bin/
  4. binder/
  5. build/
  6. common/
  7. conf/
  8. connect/
  9. connector/
  10. core/
  11. data/
  12. dev/
  13. docs/
  14. examples/
  15. graphx/
  16. hadoop-cloud/
  17. launcher/
  18. licenses/
  19. licenses-binary/
  20. mllib/
  21. mllib-local/
  22. project/
  23. python/
  24. R/
  25. repl/
  26. resource-managers/
  27. sbin/
  28. sql/
  29. streaming/
  30. tools/
  31. ui-test/
  32. .asf.yaml
  33. .gitattributes
  34. .gitignore
  35. CONTRIBUTING.md
  36. LICENSE
  37. LICENSE-binary
  38. NOTICE
  39. NOTICE-binary
  40. pom.xml
  41. README.md
  42. 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.