Architect a low-latency Caching Engine
Last updated: December 21, 2025
Quick Overview
Design a low-latency caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Instacart
December 21, 20258
9
1,505 solved
Design a low-latency 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 Instacart. 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. Instacart 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
- How would you migrate from a monolithic to a microservices architecture?
- How would you handle a 10x increase in traffic overnight?
- How would you handle a region-wide outage?
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Requirements
Functional Requirements:
- Read Caching: The system must cache frequently accessed data such as product listings, user carts, and order histories to reduce latency and improve response times....
Capacity Estimation
To estimate the capacity for the caching engine:
- User Base: Assume Instacart has 20 million active users.
- Requests per User: Each user makes approximately 10 requests per day, leading to...
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