Backfill listings.building_cadastral_number (was 0%) from the nearest
cadastral building. gendesign_cad_buildings is a postgres_fdw foreign table
with no geom — a per-listing FDW nearest query is ~1.16s/row (~13h for 43k
listings). Instead materialize the FDW once into a LOCAL cad_buildings_local
table (Point geom + GIST), then run a fast local KNN nearest-neighbour join.
Perf: the distance gate uses geometry-space ST_DWithin(geom, point, deg) (GIST
index, no geography cast) + geom <-> KNN order + ST_DistanceSphere metric
recheck on the single nearest row. A geography-cast ST_DWithin in the WHERE
defeated the index (58s+, full update never finished); the geometry-gate design
does the whole ~41.9k-listing UPDATE in ~5.4s (refresh+match ~7s end-to-end),
matching 16027 listings at 50m across 2229 distinct buildings.
The match is GEO-NEAREST (approximate): a street-level-geocoded listing matches
the nearest building within threshold_m (default 50m), not necessarily its exact
cadastral building. Exact cadastral + parcel-containment deferred (cad_parcels
FDW not exposed). Threshold is logged.
- 124: cad_buildings_local table (empty) + GIST. Migration does not read the FDW
(deploy-independent); populated by the refresh job.
- 125: scrape_schedules seed source=cadastral_geo_match, enabled, next_run_at
tomorrow 09:00 UTC (after geocode_missing).
- tasks/cadastral_geo_match.py: refresh_cad_buildings_local (bulk FDW scan),
match_listings_to_buildings (chunked LATERAL KNN UPDATE), run_cadastral_geo_match
run-lifecycle wrapper.
- scheduler: trigger_cadastral_geo_match_run + dispatch (sync DB-only, executor).
bot-backend
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