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The `GET /api/v1/dive-sites/{id}/nearby` endpoint previously calculated
distances using the Haversine formula on standard float columns, forcing
a full table scan and dynamic calculation for every query.
This commit resolves the performance bottleneck by:
- Adding a `location` column of type `POINT SRID 4326` to `dive_sites`
- Applying a `SPATIAL INDEX` to the `location` column
- Adding SQLAlchemy event listeners to sync `latitude` and `longitude`
into the new `location` POINT on insert/update
- Refactoring the `/nearby` endpoint to execute a highly optimized
raw MySQL `ST_Distance_Sphere` query
- Generating Alembic migration 0073 to backfill existing data
- Adding `@pytest.mark.spatial` to test coverage
The frontend API contract and UI remain completely unaffected,
but map and detail views rendering nearby sites will now
scale efficiently regardless of database size.
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The
GET /api/v1/dive-sites/{id}/nearbyendpoint previously calculated distances using the Haversine formula on standard float columns, forcing a full table scan and dynamic calculation for every query.This commit resolves the performance bottleneck by:
locationcolumn of typePOINT SRID 4326todive_sitesSPATIAL INDEXto thelocationcolumnlatitudeandlongitudeinto the newlocationPOINT on insert/update/nearbyendpoint to execute a highly optimized raw MySQLST_Distance_Spherequery@pytest.mark.spatialto test coverageThe frontend API contract and UI remain completely unaffected, but map and detail views rendering nearby sites will now scale efficiently regardless of database size.