cj-zhukov opened a new pull request, #26864: URL: https://github.com/apache/datafusion/pull/26864
## Which issue does this PR close? <!-- We generally require a GitHub issue to be filed for all bug fixes and enhancements and this helps us generate change logs for our releases. You can link an issue to this PR using the GitHub syntax. For example `Closes #123` indicates that this PR will close issue #123. --> - Closes #https://github.com/apache/datafusion/issues/26863. ## Rationale for this change `DataFrame::from_columns` marks every column as nullable, even when the array contains no nulls. The schema does not reflect the data when written out or shared with other systems. <!-- Why are you proposing this change? If this is already explained clearly in the issue then this section is not needed. Explaining clearly why changes are proposed helps reviewers understand your changes and offer better suggestions for fixes. Please explain the problem you are trying to solve in terms of the user-visible behavior, rather than the implementation. For example, "The code in `foo.rs` doesn't handle nulls" is a symptom of the implementation. "COUNT(DISTINCT) returns wrong results when the column contains nulls" is the user-visible problem. --> ## What changes are included in this PR? - `DataFrame::from_columns` now builds each field with `array.is_nullable()` instead of `true`. A column is non-nullable when its array is known to contain no nulls; arrays that may contain logical nulls (e.g. a dictionary with nullable values) stay nullable, so the resulting `RecordBatch` is always valid. This matches arrow's `RecordBatch::try_from_iter`. - Doc comment for `from_columns` notes that nullability is derived from the array. <!-- There is no need to duplicate the description in the issue here, but it is sometimes worth providing a summary of the individual changes in this PR. --> ## What is the testing strategy for this PR? New tests in `datafusion/core/tests/dataframe/mod.rs`: - `test_dataframe_from_columns_nullability_follows_data` – nullable only when the array has nulls; plan schema matches the collected batches - `test_dataframe_from_columns_dictionary_logical_nulls` – dictionary with nullable values stays nullable even though `null_count()` is 0 - `test_dataframe_from_columns_empty_and_null_arrays` – empty arrays and `NullArray` - `test_dataframe_from_columns_non_nullable_enables_simplification` – `IS NULL` on a non-nullable column is optimized to `EmptyRelation` - `test_dataframe_macro_nullability` – `dataframe!` inherits the behavior <!-- We typically require tests for all PRs in order to: 1. Prevent the code from being accidentally broken by subsequent changes 2. Serve as another way to document the expected behavior of the code Briefly describe how this PR is tested, and point to the specific tests you added. For example: 'This new feature is covered by the `sqllogictest` cases added in `foo.slt`'. If this PR does not add tests, explain why. For example, if the change is already covered by existing tests, please mention it. You should also check the `codecov` bot reply on this PR to confirm the changed code is exercised. --> ## Are there any user-facing changes? Yes. Schemas produced by `DataFrame::from_columns` (and the `dataframe!` macro) now mark columns without nulls as non-nullable. The API signature is unchanged. Code that compares the result against a hand-built all-nullable schema may need updating. <!-- If there are user-facing changes then we may require documentation to be updated before approving the PR. If there are any breaking changes to public APIs, please add the `api change` label. --> -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
