← Anthropic Interview Insights
Start by clarifying requirements and scale (e.g., daily active users, upload volume, feed latency). Then design the core components—photo upload pipeline, feed generation, and follow graph—focusing on data models, storage choices, and trade-offs. Finally, discuss scaling strategies like caching, sharding, and asynchronous processing.
Pro tip: Emphasize the trade-off between fan-out on write vs. read for feed generation, and propose a hybrid approach for celebrities. This shows you understand real-world complexities beyond basic designs.
Ask about expected scale (users, photos, follows), latency requirements, and features like feed ranking. Establish assumptions to guide design decisions.
Outline the upload flow: client requests pre-signed URL, uploads directly to object storage (e.g., S3), then metadata is saved in a database. Include image processing (resizing, thumbnails) via asynchronous workers.
Model the follow relationship in a graph database or a relational table with indexes. Discuss sharding strategies (e.g., by user ID) and caching for fast follower/following lookups.
Compare fan-out on write (push) vs. fan-out on read (pull) for feed generation. Propose a hybrid approach: push for normal users, pull for celebrities. Include caching and ranking.
Discuss scaling components: CDN for media, database sharding, caching layers (Redis), and message queues for async tasks. Highlight trade-offs like consistency vs. latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the interview got interesting.
Start by defining the home feed generation problem and the two main strategies: fan-out on write (push) and fan-out on read (pull). Compare their trade-offs in terms of latency, storage, and complexity, then justify your choice for a read-heavy system, likely favoring fan-out on write with optimizations like hybrid approaches for high-profile users.
Pro tip: Mention that real-world systems often use a hybrid approach, such as fan-out on write for most users but fan-out on read for celebrities with millions of followers, to balance write and read costs. This shows you understand practical scalability trade-offs.
Ask about scale (users, followers, read/write ratio), latency requirements, and consistency needs. State assumptions if not provided.
Describe how new posts are immediately pushed to followers' precomputed feeds. Highlight pros (fast reads) and cons (expensive writes, storage duplication).
Describe how feeds are generated on demand by pulling posts from followed users. Highlight pros (cheap writes, no duplication) and cons (slow reads, complex queries).
Contrast write amplification, read latency, storage cost, and complexity. Use metrics like fan-out factor and read/write ratio.
Select fan-out on write (or hybrid) because it optimizes for read-heavy workloads by precomputing feeds, ensuring low-latency reads. Mention caching and denormalization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and requirements, then explain the fan-out on write model and its bottleneck with 50M followers. Propose a hybrid approach that combines fan-out on write for most users with fan-out on read for celebrities, and discuss trade-offs like latency, consistency, and cost.
Pro tip: Mention that you would monitor and dynamically adjust the threshold for when to switch between fan-out strategies based on real-time load, showing awareness of operational complexity.
Ask about expected read/write patterns, latency requirements, and consistency needs to tailor the solution.
Describe how fan-out on write works and why it causes a write storm with 50M followers (e.g., 50M writes per post).
Suggest using fan-out on write for normal users and fan-out on read for celebrities, merging results at read time.
Discuss trade-offs: increased read latency for celebrity posts, complexity of merging, and potential inconsistency.
Mention optimizations like caching, async processing, and dynamic threshold adjustment based on load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Consistent hashing came up and I was glad I'd reviewed it.
Start by clarifying the scale and access patterns of the feed store and user data, then propose a partitioning strategy that balances load and minimizes reshuffling. Explain consistent hashing as a solution to the remapping problem of modulo hashing, and discuss trade-offs like virtual nodes and replication.
Pro tip: Mention that consistent hashing is used in real systems like Dynamo and Cassandra, and that virtual nodes help with load balancing—this shows practical awareness beyond textbook theory.
Ask about data size, read/write ratio, latency requirements, and whether the feed is user-specific or global. This determines partitioning key choice (e.g., user ID for user data, feed ID for feeds).
Describe how simple modulo (hash(key) % N) distributes data but causes massive reshuffling when N changes (e.g., adding/removing a node), leading to cache misses and downtime.
Explain the hash ring, where nodes and keys are mapped to a circle; keys are assigned to the next node clockwise. Adding/removing a node only affects a fraction of keys.
Discuss virtual nodes (each physical node mapped to multiple points on the ring) to avoid hotspots, and replication (e.g., N replicas) for fault tolerance and availability.
Acknowledge that consistent hashing adds complexity but is essential for dynamic scaling; cite systems like Dynamo, Cassandra, or Redis Cluster that use it.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Saved this for the end and honestly ran a bit low on time.
Start by clarifying the product context and requirements, then outline a high-level system design covering data sources, candidate generation, ranking, and serving. Emphasize scalability, evaluation metrics, and iteration, while discussing trade-offs and potential challenges.
Pro tip: Demonstrate product sense by linking technical choices to user value and business goals, and proactively discuss how you'd measure success and iterate based on feedback.
Ask questions to understand the product goals, scale, and constraints. Define what 'follow' means and the success metrics.
Identify relevant data sources such as user profiles, interactions, and social graph. Discuss how to handle sparse data and cold start.
Describe methods to generate a pool of potential followees, e.g., collaborative filtering, graph-based approaches, or content similarity.
Explain how to rank candidates using a model that predicts likelihood of follow or engagement, incorporating features and business rules.
Outline the serving architecture (batch/real-time), A/B testing, and offline/online evaluation metrics to iterate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and requirements, then compare fan-out on write vs. read for a user following 5,000 accounts. Propose a hybrid approach that precomputes feeds for active users and falls back to on-read merging for inactive or high-fan-out cases, while discussing caching and ranking to keep loads fast.
Pro tip: Emphasize that the real bottleneck is often the tail latency of merging thousands of sources, so use tiered caching and precomputation for active users, and consider a pull-based fallback for the long tail to avoid write amplification.
Ask about user activity patterns, feed freshness requirements, and read/write ratios to determine the appropriate trade-off.
Explain that on write, each post is pushed to all followers' feeds, which for 5,000 followees means a user's feed is precomputed but writes are expensive and can cause high latency for celebrities.
Explain that on read, the feed is assembled by pulling from all 5,000 followees at request time, which shifts cost to reads and can cause slow feed loads due to merging and ranking thousands of items.
Suggest precomputing feeds for active users (fan-out on write) and using fan-out on read for inactive users or those following many high-volume accounts, with caching layers to speed up reads.
Mention techniques like ranking at write time, using a fast in-memory store (e.g., Redis) for precomputed feeds, and limiting the number of followees considered for real-time merging.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Tricky edge case I hadn't fully thought through.
Start by clarifying the system's feed architecture (push vs. pull) and the consistency requirements. Then discuss the trade-offs between immediate removal, lazy filtering, and eventual consistency, and propose a solution that balances user experience with system performance.
Pro tip: Mention that the right approach depends on product requirements and scale; for example, at Anthropic's scale, a hybrid approach with lazy filtering and asynchronous cleanup is often optimal. Also, consider the user's perception: they expect the unfollowed user's posts to disappear promptly, but a slight delay is acceptable if performance is maintained.
Ask whether the feed is precomputed (push model) or generated on read (pull model), and whether it's a social graph like Twitter. This determines the scope of the problem.
Explain that precomputed feeds contain posts from followed users, so unfollowing creates stale entries. The challenge is to remove or hide those posts without disrupting the user experience or overloading the system.
Compare immediate removal (costly, may cause latency spikes), lazy filtering (cheap but adds read-time overhead), and asynchronous cleanup (eventual consistency). Discuss pros and cons of each.
Suggest a combination: mark the unfollow event, filter out posts from unfollowed users at read time, and asynchronously remove them from the precomputed feed in the background. This balances performance and consistency.
Discuss handling of re-follows, large fan-out, and ensuring the solution scales with millions of users. Mention monitoring and metrics to validate the approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current pipeline and constraints, then propose an incremental, hybrid approach that layers ranking on top of the existing chronological feed. Focus on decoupling ranking from ingestion, using feature flags and A/B testing to validate changes without disrupting the pipeline.
Pro tip: Emphasize the importance of maintaining a fallback to the chronological feed and using shadow mode to compare ranked vs. chronological results before full rollout. This shows you prioritize reliability and data-driven decisions.
Ask about the current pipeline architecture, latency requirements, scale, and what 'ranked' means (e.g., engagement, relevance). Understand why a full rebuild is undesirable.
Propose keeping the existing ingestion and storage layers, but adding a ranking service that consumes the chronological feed and reorders items. Use a feature store for ranking signals.
Roll out ranking behind a feature flag, starting with a small percentage of traffic. Use A/B testing to measure impact and allow instant rollback to chronological.
Set up metrics for engagement, latency, and system health. Use shadow mode to compare ranked vs. chronological without affecting users, then gradually increase traffic.
Discuss trade-offs like increased latency, complexity, and cost. Propose caching, precomputation, or approximate ranking to meet performance goals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.