Design a Caching for Slack
Last updated: April 15, 2026
Quick Overview
Design a scalable caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Slack
System Design
Software Engineer
Slack
April 15, 2026Software Engineer
System Design Round
System Design
Medium
27
7
3,136 solved
Design a scalable caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Slack asks this during the System Design Round to assess your understanding of the full ML lifecycle. They want to see how you translate a business problem into an ML objective, design the feature pipeline, and plan for model monitoring and retraining.
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
Feature engineering and feature stores
Data collection and labeling strategy
Feedback loops and model retraining
ML objective formulation and metric selection
Model serving and latency optimization
A/B testing and experimentation
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 the cold start problem?
- How would you run A/B tests on different model versions?
- How would you handle a 10x increase in prediction requests?
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Requirements
Functional Requirements
- Cache Types: Implement a multi-tier caching system including user-level caches for frequently accessed messages, channel metadata, and user preferences.
- **Invalidat...
Capacity Estimation
To estimate capacity, consider the following:
- User Base: Slack has approximately 18 million daily active users.
- Average Requests: If each user generates 100 requests per day, we anticipate...
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