Design a large-scale Caching Platform
Last updated: December 24, 2025
Quick Overview
Design a event-driven caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
SentinelOne
December 24, 202548
14
4,877 solved
Design a event-driven caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This is a common system design question asked during System Design Round at SentinelOne. 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. SentinelOne values engineers who can think about scalability from day one.
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 handle a region-wide outage?
- 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:
- Event-Driven Cache: The system should cache responses to events (e.g., API requests) and support event-based updates to the cache.
- High Throughput: The cachi...
Capacity Estimation
Back-of-Envelope Calculations:
- Expected QPS: Assume 10 million requests per second at peak.
- Cache Size: If each cached item is approximately 1 KB and we want to cache 100 million uniqu...
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