HubSpot·Software Engineer·Onsite - System Design / Architecture·Intermediate

Intermediate

Apr 2026

Summary

Did a system design round for a software engineer role at HubSpot. The focus was on designing Netflix, but the interviewer kept drilling into schema choices and cache behavior rather than the high-level architecture, which I wasn't fully prepared for.

Questions Asked (1)

Design the Netflix system. Walk through your schema design and how you'd handle caching, including what happens when cache miss rates spike.

System DesignData ModelingTechnical Trade-offs

Author's notes

I spent too long on the broad strokes (services, load balancing, CDN) and the interviewer kept pulling me back to the data model and cache behavior.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., 200M subscribers, 100M daily active users, global distribution). Then design a schema that supports efficient reads for browsing and streaming metadata, and a multi-tier caching strategy (client, CDN, application, database) with cache invalidation and fallback mechanisms. Finally, discuss how to detect and mitigate cache miss spikes using monitoring, auto-scaling, and techniques like request coalescing and negative caching.

Pro tip: Emphasize that cache miss spikes are often caused by hot keys or cache stampedes; propose solutions like consistent hashing, key sharding, and probabilistic early expiration to show depth beyond basic caching.

1. Clarify Requirements and Scale

Ask about expected user base, traffic patterns, content size, and read/write ratio to set the stage for design decisions.

2. Design Core Schema

Outline entities like Users, Titles, Genres, ViewingHistory, and Ratings, and how they relate. Discuss partitioning and indexing for scalability.

3. Propose Caching Strategy

Describe multi-level caching: CDN for video segments, application-level cache (Redis/Memcached) for metadata, and client-side caching. Explain TTL, eviction policies, and invalidation.

4. Handle Cache Miss Spikes

Explain monitoring for miss rates, auto-scaling cache clusters, request coalescing, negative caching, and fallback to database with circuit breakers.

5. Discuss Trade-offs and Improvements

Mention consistency vs. availability, cost of caching, and potential optimizations like pre-warming caches or using a write-through cache.

Key Points to Mention

Database choice: NoSQL (e.g., Cassandra) for scalability and high write throughput, or SQL with sharding for transactional integrity.
Cache eviction policies: LRU, LFU, or TTL-based, and how they affect hit rates.
Cache stampede mitigation: mutex locks, probabilistic early expiration, or background refresh.
Monitoring and alerting: track cache hit ratio, latency, and error rates; use tools like Prometheus and Grafana.
Fallback strategies: circuit breakers, rate limiting, and graceful degradation to maintain availability.
Content delivery: use CDN for video streaming to reduce origin load and improve user experience.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.