nikolauspschuetz opened a new pull request, #28819:
URL: https://github.com/apache/flink/pull/28819
## What is the purpose of the change
`_infer_type` in `flink-python/pyflink/table/types.py` inferred a list's
element type from `obj[0]` instead of the first non-`None` element it scans
for. When the first element is `None`, the array element type collapsed to
`NULL`, making schema inference order-dependent:
```
_infer_type([1, None]) -> ArrayType(BigIntType) # correct
_infer_type([None, 1]) -> ArrayType(NullType) # wrong; should be
BigIntType
```
The `dict` branch just above already handles this correctly (it infers from
the found non-`None` value). `from_elements([Row(c=[None, 1])])` without an
explicit schema infers `ARRAY<NULL>`, which then typically errors downstream.
## Brief change log
- Infer the array element type from the scanned non-`None` element `v`
rather than `obj[0]`.
## Verifying this change
This change added a test and can be verified as follows:
- Added `TypesTests.test_infer_array_type_with_leading_none` asserting
`_infer_type([None, 1])` yields `ArrayType(BigIntType)`.
- Verified against the released `apache-flink` wheel: the array element
type is `NullType` before the fix and `BigIntType` after (the all-`None` case
still yields `ARRAY<NULL>`).
## 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)`: no
- The serializers: no
- The runtime per-record code paths (performance sensitive): no
- Anything that affects deployment or recovery: no
- The S3 file system connector: no
## Documentation
- Does this pull request introduce a new feature? no
---
##### Was generative AI tooling used to co-author this PR?
- [X] Yes (Claude Code)
Generated-by: Claude Code (Opus 4.8)
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