fix(upsert): early rejection of unsupported join column types - #3384
fix(upsert): early rejection of unsupported join column types#3384abnobdoss wants to merge 7 commits into
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…pdate error expectations
…in key The error message renders the type from the Iceberg table's pyarrow schema, and schema_to_pyarrow converts pa.list_ into pa.large_list (see pyiceberg/io/pyarrow.py). The test regex must match the rendered large_list<element: int32>, not the source list<item: int32>.
A pa.null() source column was being rejected by _check_pyarrow_schema_compatible (format-version=2 forbids null) before the join-column validation could surface the intended "Null-type column ... cannot be used as a join key" error. Reordering the checks lets the upsert-specific rejection fire first, giving users the actionable message. Dataframe-level checks now skip columns that are absent from the source so the pre-existing _check_pyarrow_schema_compatible path still owns the "PyArrow table contains more columns" error in test_key_cols_misaligned.
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This pull request has been marked as stale due to 30 days of inactivity. It will be closed in 1 week if no further activity occurs. If you think that's incorrect or this pull request requires a review, please simply write any comment. If closed, you can revive the PR at any time and @mention a reviewer or discuss it on the dev@iceberg.apache.org list. Thank you for your contributions. |
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Still active - happy to address any feedback. |
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This pull request has been marked as stale due to 30 days of inactivity. It will be closed in 1 week if no further activity occurs. If you think that's incorrect or this pull request requires a review, please simply write any comment. If closed, you can revive the PR at any time and @mention a reviewer or discuss it on the dev@iceberg.apache.org list. Thank you for your contributions. |
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Still active - happy to address any feedback. |
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Rationale for this change
This is the first in a planned series of PRs to improve the stability and speed of the
Table.upsertoperation. This PR focuses on improving the correctness foundations by implementing "Fail Fast" validation for join key types.By rejecting unsupported types upfront, we prevent two major classes of issues:
This establishes a safe contract for the subsequent performance-focused PRs (Vectorization and Anti-Join de-duplication).
Are these changes tested?
Yes. I have added a comprehensive suite of parameterized tests in
tests/table/test_upsert.py.Are there any user-facing changes?
Yes. The
Table.upsertmethod now includes strict type validation for the join columns a user provides.For full disclosure - this PR was developed with the assistance of an AI coding assistant (Antigravity) to help refine the type-safety checks and edge-case validation.