Design a scalable Caching System
Last updated: February 13, 2026
Quick Overview
Design a scalable caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Square/Block
February 13, 20262
3
476 solved
Design a scalable caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
System design interviews at Square/Block typically last 45-60 minutes. You are expected to drive the conversation, starting from requirements gathering through to a detailed architecture. The interviewer will evaluate your ability to handle ambiguity and make practical engineering decisions.
What the Interviewer Expects
- Drive the design discussion proactively with minimal interviewer guidance
- Perform detailed capacity estimation and use it to inform design decisions
- Design for global scale with multi-region deployment and data consistency
- Deep dive into 2-3 critical components with implementation-level detail
- Address complex trade-offs: CAP theorem, eventual consistency, conflict resolution
- Discuss operational excellence: deployment strategy, chaos engineering, SLOs/SLIs
- Propose a phased rollout plan from MVP to full-scale system
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 handle a region-wide outage?
- How would you handle a 10x increase in traffic overnight?
- How would you optimize costs as the system scales?
- What happens if one of your database nodes goes down?
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Requirements
Functional Requirements:
- Request Handling: The caching system must handle millions of read requests per second with low latency.
- Data Freshness: Must provide mechanisms to ensure dat...
Capacity Estimation
Assuming Square/Block handles approximately 100 million transactions per day, we can estimate:
- Total Requests per Second (RPS): 100 million / 86400 seconds = ~1157 RPS.
- Cache Hit Rate: Tar...
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