Which technique speeds up distance-based searches?

Which technique speeds up distance-based searches?

• A. Bounding-box pre-filter before precise Haversine calculations
• B. Always compute distance for all records
• C. Ignore lat/lon data
• D. Use random subset of addresses

Answer: (A)

Reducing the workload before doing the heavy distance calculations is the fastest path. By applying a lightweight bounding box around the query location, you quickly filter out records that cannot be within the desired radius. Any address whose latitude or longitude falls outside this box is guaranteed not to be a match, so you skip the expensive distance computation for them. Only the remaining candidates inside the box get the precise distance calculation (such as the Haversine formula), which dramatically speeds up the search on large datasets. This beats computing distances for every record, which wastes resources on many distant points. Ignoring latitude/longitude data makes distance searches impossible, and using a random subset risks missing nearby matches.

Reducing the workload before doing the heavy distance calculations is the fastest path. By applying a lightweight bounding box around the query location, you quickly filter out records that cannot be within the desired radius. Any address whose latitude or longitude falls outside this box is guaranteed not to be a match, so you skip the expensive distance computation for them. Only the remaining candidates inside the box get the precise distance calculation (such as the Haversine formula), which dramatically speeds up the search on large datasets.

This beats computing distances for every record, which wastes resources on many distant points. Ignoring latitude/longitude data makes distance searches impossible, and using a random subset risks missing nearby matches.