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zakkiyyat
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Mar 7, 2026
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Reviewed the changes and they look good. Once the conflicts are addressed, this can be merged.
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| const querySchema = z.object({ | ||
| longitude: z.coerce.number(), | ||
| latitude: z.coerce.number(), | ||
| cursor: z.string().optional(), | ||
| }) |
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add strict longitude/latitude range checks ([-180,180], [-90,90]) from query validation, focus on:
- coordinate validation boundaries and malformed query handling
- $geoNear pipeline correctness and distance sorting
- cursor pagination behavior (stability and safety)
- response contract consistency (distanceMeters, cursor nullability)
# Conflicts: # backend/README.md # backend/src/models/food-item.model.ts # backend/src/models/index.ts # backend/src/routes/index.ts
zakkiyyat
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Thanks for addressing the comments. The changes look good. Approving.
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This introduces a location-based discovery feature that allows users to view food items from restaurants physically near them. The goal is to improve relevance in the food discovery experience by prioritizing restaurants within the user’s geographic vicinity. To support this functionality, the Restaurant MongoDB schema has been updated to include a GeoJSON
Pointfield for location data along with a2dsphereindex to enable efficient geospatial queries.A new endpoint,
GET /api/foods/discover, has been implemented to retrieve food items from nearby restaurants based on the user's coordinates. The endpoint acceptslongitude,latitude, andcursoras query parameters. Internally, a MongoDB$geoNearaggregation pipeline is used to fetchFoodItemsbelonging to restaurants located within a defined radius (10km) of the provided coordinates. The results are sorted by proximity so that the closest restaurants appear first.To support scalable browsing, the endpoint implements cursor-based pagination, returning results in batches of 10 items per request. Each response includes the calculated distance between the user and the restaurant, allowing the client application to display proximity information. Query performance is optimized through the use of the
2dsphereindex on the restaurant location field, ensuring efficient geospatial lookups even as the dataset grows.closes #150