perf: optimize LEAD/LAG IGNORE NULLS evaluation#23711
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## main #23711 +/- ##
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- Coverage 80.69% 80.68% -0.01%
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Files 1088 1088
Lines 367741 367762 +21
Branches 367741 367762 +21
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xudong963
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July 20, 2026 09:46
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Which issue does this PR close?
Rationale for this change
The whole-partition evaluation path for
LEADandLAGwithIGNORE NULLScurrently collects every valid row index, performs a binary search for every output row, converts every selected value to aScalarValue, and finally rebuilds an Arrow array from those scalars.This makes index selection
O(n log m)fornrows andmnon-null rows, and the per-row scalar materialization is particularly expensive for strings and nested values.A local Criterion microbenchmark with 100,000 rows and 50% nulls measured the following speedups across the tested offsets:
Int64Utf8ViewListWhat changes are included in this PR?
VecDequestate instead of collecting all valid indices and binary-searching for every row.takekernel.zipkernel to fill non-null default values without constructing oneScalarValueper row.zipconcatenates dictionaries and can overflow bounded dictionary key types.The index generation and output materialization are now
O(n). There are no public API changes.Are these changes tested?
Yes.
Are there any user-facing changes?
No. This changes the implementation of whole-partition
LEAD/LAG ... IGNORE NULLSevaluation without changing the SQL behavior or public API.