I spent too long on the data model upfront and barely had time to talk through the geospatial indexing side, which is obviously the core of the problem.
Start by clarifying functional and non-functional requirements, then design a scalable architecture that uses a geospatial index (e.g., geohash or quadtree) for efficient radius queries. Discuss data modeling, storage choices, and trade-offs between consistency and latency, and explain how to handle high read throughput with caching and read replicas.
Pro tip: Emphasize the importance of choosing the right geospatial indexing strategy based on query patterns and data distribution, and discuss how to handle edge cases like dense urban areas and sparse rural regions.
Ask questions to understand scale, read/write ratio, latency requirements, consistency needs, and filtering/sorting criteria. Define the scope and constraints.
Design the schema for places with attributes, and choose a database that supports geospatial indexing (e.g., PostgreSQL with PostGIS, or a NoSQL solution like Elasticsearch). Consider partitioning and replication.
Select an indexing technique (geohash, quadtree, R-tree) to efficiently find places within a radius. Explain how to combine spatial and attribute filters, and how to sort results (e.g., by distance or rating).
Address high read throughput with caching (e.g., Redis), read replicas, and CDN for static data. Discuss write path, data freshness, and trade-offs between consistency and availability.
Summarize key trade-offs (e.g., index update cost vs. query speed, consistency vs. latency) and mention potential extensions like personalization, real-time updates, or global distribution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.