Implementation Patterns and Performance Tuning

Database Schema Design
Store points with columns: id, latitude, longitude, geohash_6, geohash_8. Index the geohash columns. Precompute multiple precision levels at write time. Query uses the appropriate index based on radius.
For sharded databases, shard by geohash prefix. Points in the same region land on the same shard, enabling single-shard queries. But this can cause hot shards for popular areas. Consider composite shard keys that add entropy.
Query Optimization
Batch neighbor queries: Instead of 9 separate queries for 9 cells, use IN clause or UNION. One round trip instead of nine. Reduces latency from network overhead.
Limit results early: If you need nearest 10 points, add LIMIT to each cell query. Merge results and keep top 10. Avoids fetching thousands of candidates when only 10 are needed.
Use covering indexes: Include commonly needed columns in the index. Query can return results without hitting the main table. Faster queries for read-heavy workloads.
Caching Strategies
Cache at cell level. Key by geohash prefix, value is list of points in that cell. Queries check cache first, fall back to database. Invalidate on point updates within the cell.
For nearest neighbor queries, cache is tricky. The same cell contents yield different results depending on query origin within the cell. Cache cell contents but compute distance client-side. Or cache by finer grid of origin points if query patterns are predictable.
✅ Best Practice: Benchmark with realistic data distribution. Urban data clusters cause different performance than uniformly distributed test data. Test with actual or representative location data to find hot spots and tune accordingly.

💡 Key Takeaways

✓Store lat/lon and multiple geohash precisions; index each precision level

✓Shard by geohash prefix for locality but watch for hot shard problem

✓Batch neighbor queries into single IN clause to reduce round trips

✓Add LIMIT early; merge results to avoid fetching unnecessary candidates

✓Cache at cell level; invalidate on updates within cell

📌 Interview Tips

1Recommend covering indexes: include needed columns so queries avoid table lookup

2For nearest neighbor, explain batching: query 9 cells with IN clause, merge results, sort by distance, take top K

3Warn about test data: uniform random points miss hot spot problems that real city data reveals

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