JingsongLi commented on PR #8115:
URL: https://github.com/apache/paimon/pull/8115#issuecomment-4621217884
Thanks for adding multi-clause fall-through support. The overall evaluation
order looks aligned with `MERGE INTO` semantics: clauses are evaluated in
order, the first matching clause wins, and `NULL` conditions fall through.
One SQL-compatibility gap still needs to be handled before merging: a
non-last clause without a condition should be rejected. Spark SQL rejects
`MERGE INTO` statements where any non-last `WHEN MATCHED` / `WHEN NOT MATCHED`
clause omits its condition; only the last clause of each clause kind may be
unconditional.
This PR currently accepts the equivalent Python API shape, and the
implementation will silently make later clauses unreachable because the
unconditional clause consumes all remaining rows. For example:
```python
when_matched=[
WhenMatched(update="*"),
WhenMatched(update={"age": "s.age"}, condition="s.age > 10"),
]
```
The second clause can never run, while the corresponding SQL form would fail
during parsing/analysis. Could we add validation in `_prepare` for both
`when_matched` and `when_not_matched`, plus negative tests for unconditional
non-last clauses? That would make the Python API behavior match the supported
`MERGE INTO` syntax more closely.
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