I started with the obvious stuff, trie-based prefix matching, caching hot queries, but the LinkedIn angle tripped me up a bit.
Start by clarifying requirements and scale, then outline a high-level architecture covering data collection, indexing, query serving, and ranking. Dive into key components like the trie or inverted index for prefix matching, discuss trade-offs between latency and freshness, and address scalability and personalization.
Pro tip: Emphasize the importance of ranking by relevance and personalization, as LinkedIn's typeahead must surface the most relevant connections and entities, not just prefix matches. Also, discuss how to handle updates and deletions in real-time to keep the index fresh.
Ask about expected query volume, latency requirements, data sources (e.g., connections, companies, skills), and whether personalization is needed. Define functional and non-functional requirements.
Outline the main components: data ingestion pipeline, indexing service, query service, and ranking layer. Discuss how data flows from sources to the index and how queries are served.
Choose appropriate data structures like tries or inverted indexes for prefix matching. Explain how to build and maintain the index, including sharding and replication for scalability.
Describe how to retrieve top-k suggestions efficiently, possibly using caching. Discuss ranking factors such as connection degree, profile completeness, and personalization.
Address how to handle high throughput and low latency with techniques like caching, CDNs, and approximate algorithms. Discuss trade-offs between consistency, availability, and freshness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.