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The idiomatic per-interval overlap count -- SELECT a.cols, COUNT(b.col) FROM a LEFT JOIN b ON a.interval INTERSECTS b.interval GROUP BY a.cols -- declined to the naive plan like every outer join, becoming a HASH_JOIN on the low-cardinality chromosome key with the position inequalities as a residual filter (quadratic; ~180x slower than achievable at scale). Detect this shape and reuse the INNER IEJoin path (which already plans COUNT + GROUP BY through IE_JOIN), then wrap its counts in a zero-fill LEFT join back onto the distinct left keys so left intervals with no overlap report 0. Supports a single COUNT(<right column>) / COUNT(DISTINCT <right column>) with a GROUP BY on the projected left columns; COUNT(*), non-COUNT aggregates, ORDER BY / LIMIT / HAVING, and other outer-join shapes still take the naive-predicate plan. Claude-Session: https://claude.ai/code/session_01TERWBHov76DQM3nQeT8yyy
Add shape, decline (COUNT(*), non-COUNT aggregate, no GROUP BY, ORDER BY, self-join), a Hypothesis property test against a Python count_overlaps reference (zero-overlap rows, one-sided chromosomes, empty right table, duplicate left rows), and an EXPLAIN plan guard asserting the generated inner SQL is not planned as a BLOCKWISE_NL_JOIN. Claude-Session: https://claude.ai/code/session_01TERWBHov76DQM3nQeT8yyy
Describe the LEFT-join COUNT over INTERSECTS routing through the INNER IE_JOIN count plus a zero-fill LEFT join, and the shapes that remain on the naive-predicate plan. Claude-Session: https://claude.ai/code/session_01TERWBHov76DQM3nQeT8yyy
Tighten the count_overlaps detection gate for two shapes a review found producing wrong results on matched queries: a GROUP BY carrying keys beyond the projected columns (the extra key inflates the count cardinality and drops zero-count rows the zero-fill base cannot reproduce), and a projected key whose output name collides with the COUNT alias (the zero-fill USING / COALESCE binds to the wrong duplicately-named column). Require the group keys to equal the projected keys exactly and all output names to be distinct, declining to the naive plan otherwise. Claude-Session: https://claude.ai/code/session_01TERWBHov76DQM3nQeT8yyy
Add decline tests for a GROUP BY exceeding the projection, an output-name collision between a key and the COUNT alias, and a top-level WHERE, plus a COUNT(DISTINCT b.col) execution test asserting equivalence with the naive plan. Claude-Session: https://claude.ai/code/session_01TERWBHov76DQM3nQeT8yyy
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