bladedragon opened a new pull request, #29303:
URL: https://github.com/apache/flink/pull/29303
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## What is the purpose of the change
This change adds inline row- and range-based OVER window support to the
PyFlink `DataFrame` API while preserving the existing named Table API window
behavior.
The implementation reuses Flink's existing OVER window planner and runtime
operators. It only extends the expression construction and resolution path
required by the DataFrame API, without introducing a new operator or changing
the existing execution semantics.
## Brief change log
- Extend `Expression.over()` with inline `order_by`, `partition_by`, `rows`,
and `range` window specifications
- Add DataFrame frame-bound helpers such as `CURRENT_ROW`, `UNBOUNDED`,
`preceding()`, and `following()`
- Preserve the existing named Table API window syntax
- Resolve expanded OVER expressions and prevent their aggregates from being
handled as regular aggregations
- Add PyFlink and Java tests covering batch and streaming OVER behavior
- Document the OVER window API and its current execution restrictions in
`dataframe.rst`
## Verifying this change
- Added PyFlink unit tests for inline window construction, frame validation,
duration handling, and named-window compatibility
- Added batch integration tests for ROWS frames, time-based RANGE frames,
peer rows, and global aggregation
- Added streaming tests for successful OVER aggregation and existing planner
restrictions
- Added Java tests for expanded and named OVER expression resolution and
aggregation extraction
## Does this pull request potentially affect one of the following parts:
- Dependencies (does it add or upgrade a dependency): no
- The public API, i.e., is any changed class annotated with
`@Public(Evolving)`: yes
- The serializers: no
- The runtime per-record code paths (performance sensitive): no
- Anything that affects deployment or recovery: JobManager (and its
components), Checkpointing, Kubernetes/Yarn, ZooKeeper: no
- The S3 file system connector: no
## Documentation
- Does this pull request introduce a new feature? yes
- If yes, how is the feature documented? docs
`flink-python/docs/reference/pyflink.dataframe/dataframe.rst` + Python
docstrings
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