Architect a distributed Caching Engine
Last updated: November 17, 2025
Quick Overview
Design a distributed caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ServiceNow
November 17, 20255
13
2,329 solved
Design a distributed 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 ServiceNow. 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. ServiceNow values engineers who can think about scalability from day one.
What the Interviewer Expects
- Clearly define functional and non-functional requirements
- Propose a reasonable high-level architecture with core components
- Choose appropriate data storage solutions with basic justification
- Discuss basic scaling strategies (horizontal scaling, caching)
- Identify potential bottlenecks and suggest simple solutions
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 implement rate limiting to protect the system?
- What monitoring and alerting would you set up on day one?
- How would you migrate from a monolithic to a microservices architecture?
- How would you handle a 10x increase in traffic overnight?
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Requirements
Functional Requirements:
- Cache Storage: The system must support storing key-value pairs with expiration policies.
- Data Retrieval: Provide API endpoints for retrieving cached data wit...
Capacity Estimation
To estimate capacity:
- Daily Requests: 10 million requests.
- Average Request Size: Assume each request involves 1 KB of data.
- Storage Needs: If we cache 50% of the requests, that would...
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