Spent too long on the data model upfront and had to rush through the feed ranking and delivery parts.
Start by clarifying the unique aspects of Nextdoor's newsfeed, such as hyperlocal content and neighbor relationships, then outline a high-level design covering data model, feed generation, and ranking. Dive into trade-offs between fan-out on write vs. read, and discuss how to handle scale and real-time updates.
Pro tip: Emphasize the importance of location-based partitioning and caching strategies to ensure low-latency feed retrieval for millions of neighborhoods. Also, discuss how to balance chronological and algorithmic ranking to prioritize relevant local content.
Ask questions to understand functional and non-functional requirements, such as feed types (e.g., main feed, neighborhood feed), user interactions (post, like, comment), scale (DAU, QPS), and latency goals.
Sketch the main components: clients, API gateway, feed service, post service, user service, graph service, and storage layers. Explain how they interact to generate and deliver the feed.
Design schemas for posts, users, relationships, and feeds. Choose appropriate databases (e.g., SQL for user data, NoSQL for posts, graph DB for social graph) and discuss partitioning and indexing strategies.
Compare fan-out on write vs. fan-out on read, and propose a hybrid approach. Explain how to incorporate ranking signals (recency, proximity, engagement) and handle real-time updates.
Discuss scaling strategies (sharding, caching, CDN), fault tolerance, and trade-offs (consistency vs. availability, latency vs. freshness). Mention monitoring and metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.