Donkie ecd43bd51a backend: make color similarity search scale with matches, not catalog size ...

find_by_color loaded every filament as a full ORM object (with its vendor
join) into memory and computed delta-E in Python, then the endpoint re-queried
the matched ids anyway. The full-catalog ORM load dominated the cost: a color
search ran at a flat ~100ms regardless of how few results it returned, ~5-20x
slower than an equivalent regular filtered search, and each concurrent request
cloned the whole catalog into memory.

Scan only id/color_hex/multi_color_hexes and return the matched ids; callers
load full rows for just the matches. The matching algorithm is unchanged.
Color search now scales with the match count like any other filter: ~5-16x
faster here and the concurrency memory spike (+21MB at 8 concurrent) is gone.
Drops the "Slow operation!" note from the color_hex query param since it no
longer applies.

Verified across SQLite/Postgres/MariaDB/CockroachDB (372 integration tests
pass on each).

Claude-Session: https://claude.ai/code/session_014Cwk5Yyze9yPvkYkUtMAP4

2026-07-21 11:53:08 +02:00

..

v1

backend: make color similarity search scale with matches, not catalog size

2026-07-21 11:53:08 +02:00

__init__.py

Renamed to Spoolman

2023-04-02 19:19:15 +02:00