Architect a multi-tenant Caching Engine
Last updated: October 8, 2025
Quick Overview
Design a multi-tenant caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Rippling
October 8, 202534
4
4,477 solved
Design a multi-tenant caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ML system design at Rippling goes beyond model selection. This Onsite question evaluates your ability to design end-to-end ML pipelines, from data collection to model serving, while considering production constraints like latency and reliability.
What the Interviewer Expects
- Design the full ML lifecycle from data collection to model monitoring
- Address cold start, exploration/exploitation, and model freshness
- Discuss multi-objective optimization and ranking systems
- Plan for model debugging, fairness, and bias mitigation
- Design the feature store and training pipeline for scale
- Address model versioning, canary deployments, and rollback strategies
- Discuss the data flywheel and long-term system evolution
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 run A/B tests on different model versions?
- How would you ensure fairness and reduce bias in the model?
- What is your model retraining strategy?
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Requirements
Functional Requirements
- Multi-Tenant Support: The caching engine must support multiple tenants with isolated data access and quota management.
- Request Handling: Should handle million...
Capacity Estimation
Assuming Rippling serves around 1 million users, each making about 100 requests per day on average:
- Total Requests: 1,000,000 users * 100 requests/user = 100,000,000 requests/day
- Peak Load...
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