The part that tripped me up wasn't the storage or the read path, it was justifying the fan-out strategy without just reciting the hybrid answer.
Start by clarifying functional and non-functional requirements, then design a high-level architecture that separates write and read paths. Focus on the feed generation and ranking pipeline, discussing trade-offs between fan-out on write vs. read, and how to scale to a billion users with low latency.
Pro tip: Emphasize the importance of precomputing and caching ranked feeds for active users while using a pull-based approach for inactive users to balance cost and freshness. Also, discuss how to handle celebrity users (hotkeys) with a hybrid approach.
Ask about functional requirements (post types, interactions, feed personalization) and non-functional requirements (latency, availability, consistency). Estimate scale: 1B DAU, ~2B posts/day, read-heavy workload.
Outline core components: clients, API gateway, post service, graph service (follows), feed service, ranking service, and storage (SQL/NoSQL, cache, blob storage). Separate write path (post creation) from read path (feed retrieval).
Discuss feed generation strategies: fan-out on write (push) vs. fan-out on read (pull) vs. hybrid. Explain how to rank posts using signals (affinity, recency, engagement) and ML models, and how to precompute feeds for active users.
Address scaling challenges: sharding, replication, caching, CDN for media, and handling hotkeys (celebrities). Discuss trade-offs: latency vs. freshness, cost vs. personalization, consistency vs. availability.
Summarize the design, mention monitoring (latency, error rates, feed freshness), and potential bottlenecks. Suggest future improvements like real-time updates or more advanced ranking.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said shard the cache and immediately got a follow-up asking what happens when the shard itself gets overwhelmed.
Start by clarifying the scenario and requirements, then propose a multi-layered caching strategy that includes request coalescing, local caching, and cache replication. Emphasize trade-offs between consistency, latency, and cost, and explain how you would monitor and adapt the solution.
Pro tip: Mention that you would simulate the hot key scenario with load tests and use real-time metrics to validate the solution, showing a proactive and data-driven approach.
Ask questions to understand the scale, read/write ratio, latency requirements, and existing infrastructure. This ensures your solution is tailored to the specific context.
Explain that a single key overwhelming the cache can cause increased latency, cache misses, and potential outages. Highlight the need to distribute load and reduce redundant requests.
Discuss techniques like request coalescing (single flight), local in-memory caching, key replication with random suffixes, and using a CDN or edge cache. Explain how each helps.
Compare consistency, latency, cost, and complexity of each approach. For example, key replication may cause stale data, while request coalescing adds slight latency but reduces load.
Describe how you would monitor cache hit rates, latency, and load, and set up alerts. Mention the importance of load testing and gradually rolling out changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Described a two-stage ranker: lightweight pass using recency and basic affinity signals, then a heavier model for the top candidates.
Start by clarifying the product context and requirements, then walk through the multi-stage pipeline: candidate generation, filtering, scoring, and ranking. Emphasize the trade-offs between relevance, diversity, and freshness, and how you would measure and iterate on the system.
Pro tip: Show that you understand the end-to-end system, not just the ranking algorithm. Discuss how you would handle cold-start users, feedback loops, and real-time signals, and how you'd A/B test changes to avoid regressions.
Ask about the product (e.g., Facebook Feed), scale, latency requirements, and success metrics. Understand what 'personalized' means and what signals are available.
Describe how to retrieve a set of potentially relevant items from a large corpus. Mention techniques like inverted indices, embeddings, and graph-based retrieval, and how to balance recall and efficiency.
Explain how to apply hard filters (e.g., blocked content, already seen) and compute cheap scores to prune the candidate set before expensive ranking.
Detail the ranking model (e.g., gradient boosted trees, deep neural networks) that predicts engagement probabilities. Discuss feature engineering, training data, and online serving.
Cover post-processing steps like diversity, freshness, and business rules. Explain how to blend multiple objectives and produce the final ordered list.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly a drill-down I wasn't expecting mid-conversation.
Start by clarifying the scale and consistency requirements, then discuss a multi-layered caching strategy (client, CDN, application, database) and how invalidation propagates through each layer. Focus on trade-offs between consistency, latency, and complexity, and propose a concrete solution like write-through with versioning or event-driven invalidation.
Pro tip: Emphasize that perfect consistency is often unnecessary; instead, discuss how to bound staleness (e.g., TTLs) and use idempotent operations to handle race conditions gracefully. Mention monitoring and fallback mechanisms to detect and recover from cache inconsistencies.
Ask about scale (QPS, data size), consistency needs (strong vs eventual), and latency SLAs. This shows you tailor solutions to real-world constraints.
Identify all caches involved: client-side, CDN, application-level (e.g., Redis), and database query caches. Explain how each layer might store follow relationships or post data.
Propose a strategy such as write-through (update cache on write), write-behind (async invalidation), or versioned keys. For unfollow/delete, consider event-driven invalidation via a message queue to propagate changes.
Discuss how to handle concurrent updates, cache stampedes, and partial failures. Use techniques like idempotent operations, retries with exponential backoff, and circuit breakers.
Compare consistency vs latency vs complexity. Suggest monitoring cache hit rates and staleness, and define fallback mechanisms (e.g., serving stale data with a warning).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.