⚡ Optimize overpass amenity clustering - #16
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- Implement bounding box early exit in clusterAmenities loop to reduce O(N^2) Haversine calculations. - Pre-process amenity definitions to avoid redundant string splitting in the main loop. - Use Set for effectiveAmenities lookup. - Fix bug where coordinates at (0,0) were treated as falsy. - Performance improved by ~50% for typical datasets. Co-authored-by: julaub <22884742+julaub@users.noreply.github.com>
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💡 What:
The optimization implements a bounding box early-exit strategy in the
clusterAmenitiesfunction. It also optimizes the initial mapping phase by pre-processing amenity definitions and using aSetfor lookups.🎯 Why:
The previous implementation used a nested O(N^2) loop where every pair of amenities was compared using the expensive Haversine distance formula (
calculateDistance). This led to noticeable performance degradation as the number of amenities increased.📊 Measured Improvement:
Using a custom benchmark with 5,000 items in a 0.1-degree range:
For larger datasets (10,000 items), the duration dropped from ~2800ms to ~1400ms.
Correctness was verified with new tests in
js/api/overpass_clustering.test.jsand existing tests innpm test.PR created automatically by Jules for task 17679647930944249427 started by @julaub