Architect a low-latency Caching Engine
Last updated: October 3, 2025
Quick Overview
Design a low-latency caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
HubSpot
October 3, 2025121
10
3,920 solved
Design a low-latency caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
HubSpot asks this during the Onsite to assess your architectural thinking. They want to see how you decompose a complex problem, choose appropriate technologies, and reason about failure modes. Strong candidates proactively discuss monitoring, alerting, and operational concerns.
What the Interviewer Expects
- Systematically gather requirements and estimate capacity (QPS, storage, bandwidth)
- Design a scalable architecture with clear component responsibilities
- Make well-reasoned database and caching decisions with trade-off analysis
- Address consistency vs availability trade-offs specific to the use case
- Discuss partitioning strategy, replication, and data modeling
- Cover failure handling, monitoring, and alerting strategies
Key Topics to Cover
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- How would you optimize costs as the system scales?
- How would you implement rate limiting to protect the system?
- What happens if one of your database nodes goes down?
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Requirements
Functional Requirements
- Data Caching: Store frequently accessed data to reduce latency for read operations.
- Write-through and Write-back Caching: Support both strategies to ensure da...
Capacity Estimation
Assuming HubSpot serves around 1 million active users daily, and each user generates about 100 requests per day:
- Total Requests per Day: 1,000,000 users * 100 requests = 100,000,000 requests/da...
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