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
System Design
Software Engineer
Booking.com
January 8, 2026
Software Engineer
Onsite
System Design
Medium

11

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
Consistency models and replication
Requirements gathering and capacity estimation
High-level architecture and component design
Partitioning and sharding strategies
Failure handling and fault tolerance
Database selection and data modeling
How to Approach This
  1. Start by clarifying functional and non-functional requirements with the interviewer.
  2. Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
  3. Draw a high-level architecture first, then deep dive into 1-2 critical components.
  4. Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
  5. 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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Sample Answer
Requirements

Functional Requirements

  1. Ad Serving: The system must serve relevant ads to users in real-time based on their queries and behavior.
  2. Targeting: Ability to target ads based on user demog...
Capacity Estimation

Back-of-envelope Calculations

  1. User Base: Assuming 100 million users, with 10% active at peak times = 10 million active users.
  2. Requests: Each user makes an average of 1 ad request per...

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