Architect a scalable Caching Engine
Last updated: October 3, 2025
Quick Overview
Design a scalable caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ServiceNow
October 3, 2025120
13
365 solved
Design a scalable caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This is a common system design question asked during Onsite 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
- 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 schema migrations with zero downtime?
- How would you handle a 10x increase in traffic overnight?
- How would you handle a region-wide outage?
- How would you migrate from a monolithic to a microservices architecture?
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Requirements
- Functional Requirements:
- Support caching for API responses to reduce load on the primary database.
- Provide a cache invalidation mechanism for consistency across distributed services. -...
Capacity Estimation
To estimate the capacity required for the caching engine:
- Assume we expect 10 million users, each making an average of 10 requests per second (QPS).
- Total QPS = 10 million users * 10 requests/user...
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