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Design Rolling-Window Ad Frequency Capping

Last updated: May 14, 2026

Quick Overview

This question evaluates system-design skills for building scalable, low-latency services that enforce time-based rolling-window state, including data modeling for impression history, read/write paths, concurrency control, and fault tolerance; category: System Design.

  • medium
  • Netflix
  • System Design
  • Software Engineer

Design Rolling-Window Ad Frequency Capping

Company: Netflix

Role: Software Engineer

Category: System Design

Difficulty: medium

Interview Round: Onsite

Design a frequency-capping service for an advertising platform. The ad platform receives ad requests for users and must decide whether a candidate ad, ad group, or campaign can be shown. Each cap has the form: a user may receive at most `N` impressions for a given ad scope during the last `W` units of time. The window is rolling, not calendar-based. Your design should cover: - the read path used when deciding whether to show an ad, - the write path used after an ad impression is served, - data models for cap definitions and impression history, - how to enforce caps correctly under high request volume, - how to handle distributed-system issues such as race conditions, duplicate requests, failures, and data retention.

Quick Answer: This question evaluates system-design skills for building scalable, low-latency services that enforce time-based rolling-window state, including data modeling for impression history, read/write paths, concurrency control, and fault tolerance; category: System Design.

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Netflix logo
Netflix
Apr 17, 2026, 12:00 AM
Software Engineer
Onsite
System Design
0
0

Design a frequency-capping service for an advertising platform.

The ad platform receives ad requests for users and must decide whether a candidate ad, ad group, or campaign can be shown. Each cap has the form: a user may receive at most N impressions for a given ad scope during the last W units of time. The window is rolling, not calendar-based.

Your design should cover:

  • the read path used when deciding whether to show an ad,
  • the write path used after an ad impression is served,
  • data models for cap definitions and impression history,
  • how to enforce caps correctly under high request volume,
  • how to handle distributed-system issues such as race conditions, duplicate requests, failures, and data retention.

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