Design a scalable Recommendation System
Last updated: April 20, 2026
Quick Overview
Design a scalable recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Twilio
April 20, 20268
6
1,458 solved
Design a scalable recommendation 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 Twilio. 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. Twilio 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 a 10x increase in traffic overnight?
- How would you implement rate limiting to protect the system?
- How would you handle a region-wide outage?
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Requirements
Functional Requirements:
- User Profile Management: Store and manage user profiles, including preferences and interaction history.
- Content Item Storage: Store various content items (e....
Capacity Estimation
Back-of-Envelope Calculations:
- User Base: Assume 100 million active users, each generating an average of 10 requests per day for recommendations.
- Total Requests: 100 million users * ...
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