Start by clarifying functional and non-functional requirements, then outline the high-level architecture covering data ingestion, indexing, query flow, and ranking. Dive into key components like search APIs, real-time updates, and scaling strategies, and discuss trade-offs and observability. Structure your answer to show breadth first, then depth on critical areas.
Pro tip: Emphasize how you would handle the trade-off between freshness and relevance in search results, and propose a hybrid approach using both batch and stream processing. Also, mention specific technologies like Elasticsearch or Kafka to demonstrate practical knowledge.
Ask questions to understand the scope: what entities are searchable (restaurants, dishes), what filters (location, cuisine, price), expected scale (QPS, data volume), latency requirements, and consistency needs.
Sketch the main components: data sources (restaurant menus, user reviews), ingestion pipeline, indexing system, search API, ranking service, and real-time update mechanism. Explain how they interact.
Detail the indexing strategy (inverted index, geo-indexing), query flow (parsing, retrieval, ranking), and ranking factors (relevance, popularity, delivery time). Discuss how to handle real-time updates (e.g., menu changes, availability).
Explain how to scale each component (sharding, replication, caching) and discuss trade-offs like consistency vs. availability, latency vs. freshness, and cost vs. performance.
Describe metrics (latency, QPS, error rates), logging, and tracing to ensure system health. Mention A/B testing for ranking changes and alerting for anomalies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.