I jumped straight into the database schema and the interviewer had to nudge me back toward clarifying requirements first.
Start by clarifying functional and non-functional requirements, then propose a high-level architecture that separates query understanding, retrieval, and ranking. Dive into key components like indexing, ranking signals, and scalability, making explicit trade-offs and justifying choices based on Airbnb's unique inventory and user expectations.
Pro tip: Emphasize that Airbnb search is not just about matching text but about ranking listings by relevance and quality, so discuss how you'd incorporate signals like host quality, price competitiveness, and personalization. Also, mention how you'd handle the cold-start problem for new listings and users.
Ask questions to understand scope: Is this for guests searching listings? What are the key features (filters, dates, location, amenities)? What are the scale and latency requirements? What are the consistency and availability trade-offs?
Outline the main components: query parser, search index (e.g., Elasticsearch), ranking service, and data stores. Explain how a search request flows through these components and how results are returned.
Pick 1-2 areas to detail, such as how to build and update the index (e.g., inverted index, geospatial indexing), or how to design the ranking algorithm (e.g., learning-to-rank with features like price, reviews, location).
Discuss how to scale the system (sharding, replication, caching), and trade-offs between consistency and availability, latency and freshness, and relevance and diversity.
Summarize the design, mention potential bottlenecks, and suggest enhancements like personalization, A/B testing, or handling seasonal demand spikes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.