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Design ad frequency capping

Last updated: Apr 23, 2026

Quick Overview

This question evaluates a candidate's competence in large-scale system design, including distributed systems, real-time analytics, data modeling, and consistency and scalability trade-offs for ad frequency capping.

  • medium
  • Netflix
  • System Design
  • Software Engineer

Design ad frequency capping

Company: Netflix

Role: Software Engineer

Category: System Design

Difficulty: medium

Interview Round: Onsite

Design a frequency capping system for an advertising platform. The system must ensure that a user does not see the same advertisement more than a configured number of times within a given time window, such as: - at most 3 impressions per user per ad per day - at most 10 impressions per user per campaign per week Your design should address: - how ad-serving systems check caps in real time before showing an ad - how impressions are counted and stored - how to support multiple cap scopes, such as user-ad, user-campaign, or household-level limits - how to handle high QPS, low latency, and eventual consistency across regions - what happens when counting data is delayed, duplicated, or arrives out of order - data retention, expiration of old counts, and operational trade-offs between accuracy and latency

Quick Answer: This question evaluates a candidate's competence in large-scale system design, including distributed systems, real-time analytics, data modeling, and consistency and scalability trade-offs for ad frequency capping.

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Netflix logo
Netflix
Apr 15, 2026, 12:00 AM
Software Engineer
Onsite
System Design
433
0

Design a frequency capping system for an advertising platform.

The system must ensure that a user does not see the same advertisement more than a configured number of times within a given time window, such as:

  • at most 3 impressions per user per ad per day
  • at most 10 impressions per user per campaign per week

Your design should address:

  • how ad-serving systems check caps in real time before showing an ad
  • how impressions are counted and stored
  • how to support multiple cap scopes, such as user-ad, user-campaign, or household-level limits
  • how to handle high QPS, low latency, and eventual consistency across regions
  • what happens when counting data is delayed, duplicated, or arrives out of order
  • data retention, expiration of old counts, and operational trade-offs between accuracy and latency

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