Start by clarifying functional and non-functional requirements, then walk through the high-level architecture covering data model, API design, indexing, ranking, and scalability. Emphasize trade-offs and justify your choices based on the platform's scale and user needs.
Pro tip: Focus on the ranking algorithm and how it balances relevance, availability, price, and personalization—this is often the most challenging part and demonstrates deep product thinking. Also, mention how you would handle updates to listings and availability in near real-time to keep search results fresh.
Ask questions to understand the scope: expected scale (listings, queries per second), key features (filters, sorting, personalization), and non-functional requirements (latency, consistency, availability).
Define entities like Listing, User, Booking, Review, and Location. Consider denormalization for search performance and how to model availability and pricing.
Outline RESTful endpoints for search (e.g., GET /search with query params for location, dates, guests, filters) and for fetching listing details. Discuss pagination, sorting, and error handling.
Choose an indexing solution (e.g., Elasticsearch) and design the index mapping. Explain the ranking function that combines relevance, availability, price, quality, and personalization signals.
Discuss scaling the search cluster, caching strategies, handling hot spots, and trade-offs between consistency and latency. Mention monitoring and iteration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.