Build a low-latency Ad Serving Pipeline
Last updated: January 8, 2026
Quick Overview
Design a low-latency ad serving system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Booking.com
January 8, 202611
4
1,026 solved
Design a low-latency ad serving system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Booking.com asks this during the Onsite to assess your architectural thinking. They want to see how you decompose a complex problem, choose appropriate technologies, and reason about failure modes. Strong candidates proactively discuss monitoring, alerting, and operational concerns.
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 do you ensure data consistency across multiple services?
- How would you optimize costs as the system scales?
- How would you handle schema migrations with zero downtime?
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Solve on CodemiaSample Answer
1. Requirements and Scope
### Functional requirements For Booking.com, the ad serving pipeline should serve sponsored accommodation and travel-related ads in low-latency surfa...
2. Capacity Estimation
Assume Booking.com has very high global traffic across web and mobile. ### Traffic assumptions - Peak search/page requests that may request ads: `2 ...
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