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Summary

AND evaluation passes rows selected by earlier predicates through EvalContext.validity. The
custom vectorized string LIKE implementation ignored this bitmap and still ran the matcher for
every row in the input column. This is especially expensive when FUSE Parquet Prewhere combines a
selective predicate with one or more LIKE predicates.

For example:

SELECT event_time, payload
FROM benchmark_events
WHERE worker = 'worker-7'
  AND payload NOT LIKE '%absent_token%'
  AND category NOT LIKE '%controller.module%'
ORDER BY event_time DESC
LIMIT 50;

If worker = 'worker-7' selects 1/64 of the rows, the two LIKE predicates previously still
processed the complete columns. They now process only rows whose validity bit is true.

The change covers column LIKE scalar, scalar LIKE column, and column LIKE column. It keeps the
existing full-column path when validity is absent or all true, and retains the specialized
%literal% substring search path.

On an S3-backed synthetic FUSE table with 10 million rows and approximately 2 KiB strings, the
query above improved from a 13.50 s median to 1.60-1.74 s with default settings. Both plans read the
same 10 million rows; LIKE evaluations dropped from 10 million to 156,250 per predicate. Aggregate
results and the Top-50 result fingerprint were identical before and after the change.

Tests

• Unit Test
• Logic Test
• Benchmark Test
• No Test - Explain why

Added tests for:

• sparse validity across all three vector argument shapes;
• matcher call counts for inactive rows;
• all-true validity and the specialized substring path;
• 128 property-test cases comparing sparse and full evaluation for active rows.

Type of change

• Bug Fix (non-breaking change which fixes an issue)
• New Feature (non-breaking change which adds functionality)
• Breaking Change (fix or feature that could cause existing functionality not to work as expected)
• Documentation Update
• Refactoring
• Performance Improvement
• Other (please describe):

This change is