This is a big one and I felt the scope creep pretty fast.
Start by clarifying functional and non-functional requirements, then estimate scale (hundreds of millions of users, millions of followers). Propose a hybrid feed generation strategy (push for normal users, pull for celebrities) with a ranking layer, and discuss trade-offs in data storage, caching, and consistency.
Pro tip: Explicitly call out the celebrity problem and propose a hybrid push-pull model; this shows you understand real-world trade-offs at scale. Also, mention that ranking should be pluggable and initially simple (e.g., reverse chronological with basic signals) to allow iteration.
Ask about feed content (text, media), ranking factors (recency, engagement), latency requirements, and consistency needs. Estimate QPS, storage, and fan-out rates based on user and follower counts.
Define schemas for users, posts, follows, and feeds. Choose appropriate databases: e.g., graph DB for social graph, wide-column store for posts and feeds, and cache for hot data.
Specify endpoints for post creation, follow/unfollow, and feed fetching. Outline service responsibilities: post service, graph service, feed service, and ranking service.
Decide between push (fan-out on write), pull (fan-out on read), or hybrid. For celebrities, use pull to avoid write amplification; for normal users, push to enable fast reads.
Describe a simple ranking algorithm (e.g., reverse chronological with basic weights) and how to integrate it. Discuss trade-offs: latency vs. freshness, storage vs. compute, and consistency vs. availability.
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Pretty straightforward to sketch out but the follow-up about async fanout queues tripped me up a little.
Start by clarifying requirements and scale (e.g., number of users, read/write ratio, latency expectations). Then walk through the write path step-by-step: client request, API gateway, post service, storage, and feed distribution. Finally, discuss trade-offs between fan-out on write vs. read, and how you'd handle hot users and consistency.
Pro tip: Proactively mention the hybrid approach: fan-out on write for most users, but fan-out on read for celebrities to avoid write amplification. This shows you understand real-world trade-offs and scalability.
Ask about expected scale (DAU, posts per second), read/write ratio, latency requirements, and consistency needs. This sets the stage for design decisions.
Describe the flow: client sends POST request to API gateway, which routes to post service. Post service validates, stores post in database (e.g., posts table), and returns success to user.
Explain how the post reaches followers: either fan-out on write (push to followers' feed caches) or fan-out on read (pull from followed users at read time). Discuss trade-offs.
Address hot users (celebrities) with hybrid approach, use message queues for asynchronous fan-out, and ensure idempotency and retries for reliability.
Conclude with key trade-offs: latency vs. consistency, write amplification vs. read latency, and how your design meets the requirements.
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I talked about reading from a precomputed Redis sorted set keyed by user ID, fetching the top N post IDs, then doing a batch lookup against a post metadata cache.
Start by clarifying the scope and assumptions (e.g., feed size, user scale, read/write ratio, consistency requirements) to show you think before designing. Then walk through the read path step-by-step, from client request to response, highlighting key components and optimizations. Finally, explain how you achieve ~200ms latency by combining caching, precomputation, parallelization, and efficient data access patterns.
Pro tip: Quantify the latency budget: break down the 200ms into network, compute, and storage components, and show how each optimization fits within that budget. This demonstrates a performance-driven mindset and helps you prioritize trade-offs.
Ask about feed size, user scale, read/write ratio, consistency needs, and latency target. State assumptions to frame the design.
Describe the flow: client request → API gateway → feed service → data stores (cache, DB) → response. Mention key components like load balancers, CDN, and service discovery.
Explain each step: authentication, fetching user graph, retrieving feed items (precomputed or on-the-fly), ranking, filtering, and assembling the response. Highlight data stores and their roles.
Detail how you achieve ~200ms: caching (Redis, CDN), precomputation (fan-out on write), parallel fetching, pagination, compression, and efficient serialization.
Discuss trade-offs (e.g., consistency vs. latency, precompute vs. on-demand) and how you handle failures (cache misses, timeouts, fallbacks).
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Start by clarifying the scale and requirements (e.g., expected media volume, latency, cost constraints). Then propose a high-level architecture using object storage (e.g., S3) for media, a CDN for delivery, and a database for metadata. Discuss trade-offs and optimizations like image resizing, video transcoding, and caching.
Pro tip: Mention the importance of decoupling media processing from the main application flow using asynchronous workers and queues to ensure scalability and resilience. Also, highlight cost optimization strategies like lifecycle policies and tiered storage.
Ask about scale (number of users, media size), latency expectations, budget, and any compliance needs. This shows you consider context before designing.
Propose using object storage (e.g., AWS S3) for storing media, a CDN for global delivery, and a relational or NoSQL database for metadata. Explain how uploads and downloads flow.
Describe asynchronous processing for resizing images, transcoding videos, and generating thumbnails. Use queues (e.g., SQS) and workers to handle tasks without blocking the main application.
Explain how CDN caches media at edge locations, reducing latency and origin load. Discuss cache invalidation strategies and signed URLs for secure access.
Mention auto-scaling for processing workers, lifecycle policies to move old media to cheaper storage, and using compression to reduce bandwidth costs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I blanked for a second here because I'd been thinking purely about the plumbing and ranking felt like a different domain.
Start by clarifying the product goals and user needs for the feed, then propose a ranking system that balances relevance, engagement, and freshness. Outline a concrete architecture with offline training, online serving, and feedback loops, and discuss trade-offs like latency, complexity, and potential biases.
Pro tip: Emphasize the importance of a fallback to reverse chronological order for new users or when signals are sparse, and highlight how you'd measure success through A/B testing and guardrail metrics.
Ask about the feed's purpose, target users, and business objectives (e.g., engagement, retention). Identify constraints like latency, infrastructure, and data availability.
List potential signals such as recency, user interactions (clicks, likes), content quality, author affinity, and diversity. Explain how each signal could be computed and weighted.
Propose a two-stage system: candidate generation (e.g., from follow graph, trending) and ranking (e.g., machine learning model). Describe offline training and online serving with low latency.
Discuss trade-offs: relevance vs. freshness, personalization vs. diversity, and complexity vs. maintainability. Mention cold-start, feedback loops, and bias mitigation.
Define metrics (e.g., CTR, time spent, retention) and propose A/B testing. Include guardrail metrics to prevent negative effects and plan for continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.