Fix strict NotEqualTo/NotIn pruning with partial nulls or NaNs#3521
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kevinjqliu merged 3 commits intoJun 22, 2026
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…with partial nulls
_StrictMetricsEvaluator.visit_not_equal and visit_not_in short-circuited on
_can_contain_nulls / _can_contain_nans (null/NaN count > 0) and returned
ROWS_MUST_MATCH without checking the value bounds. A file holding any null or
NaN was therefore reported as fully matching the predicate, even when a
non-null value inside the bounds did not match.
This drives _DeleteFiles (table/update/snapshot.py): ROWS_MUST_MATCH drops the
whole data file without rewriting it. So delete(NotEqualTo("x", 5)) against a
file with stats [null, 5] and bounds lower=upper=5 would delete the entire
file, silently losing the row with value 5 that should have survived.
Every other strict ROWS_MUST_MATCH path already guards on the "only" variants
(_contains_nulls_only / _contains_nans_only), matching the reference
StrictMetricsEvaluator. Switch both methods to the same guard so that an
all-null/all-NaN column still short-circuits to ROWS_MUST_MATCH (those rows
satisfy not-equal/not-in), while a partially-null column falls through to the
bounds check.
Update the existing NotIn-on-some-nulls test that encoded the buggy result and
add a regression test covering the [null, value] / bounds-include-literal case
for both NotEqualTo and NotIn.
Fixes apache#3498 (partially)
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@kevinjqliu @Fokko Can you please take a look when you have a moment? |
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Summary
Related to #3498
Fix strict metrics evaluation for
NotEqualToandNotInso files are only proven to match when a column contains only nulls or only NaNs. Mixed null/NaN files now continue through the existing bounds checks instead of being treated asROWS_MUST_MATCH.Root Cause
The strict evaluator used
_can_contain_nulls/_can_contain_nansfor negative predicates. That is too broad: a file with values like[null, 5]and bounds5..5cannot be proven to matchx != 5orx not in {5}because the non-null row may still fail the predicate.Java Parity
This matches Java's
StrictMetricsEvaluator, which only short-circuits negative predicates when the column contains only nulls or only NaNs:notEqnotInValidation
UV_CACHE_DIR=.cache/uv PYTHON_GIL=1 PYTHONPATH=. uv run pytest tests/expressions/test_evaluator.py -k "mixed_nulls_and_matching_bounds or mixed_nans_and_matching_bounds or all_nulls or all_nans or strict_integer_not_in"UV_CACHE_DIR=.cache/uv PYTHON_GIL=1 PYTHONPATH=. uv run pytest tests/expressions/test_evaluator.pyUV_CACHE_DIR=.cache/uv PYTHON_GIL=1 PYTHONPATH=. uv run ruff check pyiceberg/expressions/visitors.py tests/expressions/test_evaluator.pygit diff --check