Design a Caching Service
Last updated: March 8, 2026
Quick Overview
Design a distributed caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Tesla
March 8, 202625
6
686 solved
Design a distributed caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ML system design at Tesla goes beyond model selection. This System Design Round 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
- Define clear ML objectives with appropriate loss functions and metrics
- Design a comprehensive feature engineering pipeline
- Discuss model selection with trade-offs (complexity vs interpretability vs latency)
- Plan online and offline evaluation strategies including A/B testing
- Address serving infrastructure: batch vs real-time, latency requirements
- Consider data quality, labeling strategy, and feedback loops
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
- What is your model retraining strategy?
- How would you handle a 10x increase in prediction requests?
- How would you run A/B tests on different model versions?
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Requirements
Functional Requirements
- Cache Data Storage: The system must store and retrieve model inference results and feature sets to reduce latency in requests.
- Cache Invalidation: Mechanism t...
Capacity Estimation
Assuming Tesla's application handles around 1 million requests per minute for model inference:
- Peak Requests: 1,000,000 requests/minute = 16,667 requests/second.
- Data Size: Average respons...
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