Design Caching Infrastructure for real-time analytics
Last updated: May 27, 2026
Quick Overview
Design a high-throughput caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Coinbase
May 27, 202699
11
4,278 solved
Design a high-throughput caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This is a common system design question asked during Technical Screen at Coinbase. The interviewer expects you to demonstrate your ability to design large-scale distributed systems, make well-reasoned trade-offs, and communicate your thought process clearly. Coinbase values engineers who can think about scalability from day one.
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
- What monitoring and alerting would you set up on day one?
- What would the deployment pipeline look like for this system?
- How would you handle schema migrations with zero downtime?
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Requirements
Functional Requirements
- High-Throughput Caching: The system must handle millions of read requests per second with low latency.
- Real-Time Analytics: Cache must support real-time updat...
Capacity Estimation
Back-of-Envelope Calculation
- User Base: Assume 10 million active users.
- Request Rate: Each user generates approximately 5 requests per minute, leading to:
- Total requests = 10M u...
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