somasays opened a new pull request, #2799:
URL: https://github.com/apache/iceberg-python/pull/2799
## Rationale
Fixes #2791
When writing PyArrow tables with smaller integer types (uint8, int8, int16,
uint16)
to Iceberg tables with IntegerType columns, PyIceberg preserves the original
Arrow
type in the Parquet file. This causes Spark to fail with:
```
java.lang.UnsupportedOperationException: Unsupported logical type: UINT_8
```
The fix casts smaller integer types to their canonical Iceberg representation
(int32 for IntegerType, int64 for LongType) during write, ensuring
cross-platform
compatibility.
## Changes
- Added integer type widening logic in
`ArrowProjectionVisitor._cast_if_needed()`
following the same pattern as existing timestamp handling
- Only widening conversions are allowed (e.g., uint8 → int32, int32 → int64)
- Narrowing conversions continue to be rejected via the existing `promote()`
function
## Testing
- Added parameterized tests for integer type casting
(`test__to_requested_schema_integer_promotion`)
- Verified existing `test_projection_filter_add_column_demote` still works
(narrowing rejection)
- All 3041 tests pass
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