Design a location-based radius top-K search

Q: Design a location-based radius top-K search

This question evaluates system-design and backend engineering skills, focusing on geospatial data modeling, indexing and query strategies, sharding and partitioning, handling static versus dynamic location updates, and high-level capacity and latency estimation for throughput-sensitive services.

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Question

Design a location-based search service.

Input:

latitude , longitude
radius (meters)
K

Output:

The top K locations within the radius.

The term “location” is generic and can represent:

Static locations (e.g., businesses/POIs like a Yelp listing)
Dynamic locations (e.g., nearby drivers in a ride-hailing app) that update frequently

Discuss:

Data model and APIs
How to index/query efficiently (e.g., geohash, quadtree, or alternatives)
Trade-offs for static vs dynamic data
Sharding strategy (including hybrid approaches)
How you would estimate memory/throughput at a high level

Assume global scale and low-latency queries.

Design a location-based radius top-K search

Quick Overview

Solution

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