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System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

What difficulty level is this interview question?

This is a hard difficulty System Design question, commonly asked during Technical Screen rounds at OpenAI.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at OpenAI during technical interviews.

Design an online ads serving system | OpenAI Interview Question

Quick Overview

This question evaluates a candidate's competency in large-scale, low-latency distributed system design, covering real-time ad selection and auction mechanics, API and data-model design, scaling, fault tolerance, near-real-time reporting, and privacy/compliance considerations.

System Design: Low-Latency Targeted Ad Serving (≤100 ms E2E)

You are designing an online advertising serving system for web/app inventory with a strict end-to-end latency SLA of 100 ms (client request to ad response). Assume multi-region deployment and peak global load.

Functional Requirements

Real-time ad selection and auction within 100 ms end-to-end.
Targeting: keywords/context, user segments, and geography.
Campaign management: budgets (daily/total), pacing, schedules, frequency capping, creatives.
Ranking: combine advertiser bid with predicted CTR/CVR and quality signals.
Real-time auctions and pricing.
Near-real-time reporting for impressions, clicks, spend, basic conversions.

What to Deliver

APIs
- Define an ad request API (input fields) and ad response API (output fields).
Core Data Models
- Specify entities for campaign/line item, creative, targeting, budget/pacing, user segments, and event/log schemas.
Architecture
- Outline components and data flow: edge gateways, ad selector, feature store, model service, auctioneer, throttling/pacing, caching layers, logging/streaming pipeline, and offline analytics/warehouse.
Auction Mechanics and Strategy
- Choose an auction (e.g., first-price, second-price/GSP, VCG), explain incentives and trade-offs.
Additional Topics
- Handling cold start for new ads and new users.
- Deduplication (impressions/clicks/events; creative dedupe within response).
- A/B testing strategy and experiment bucketing.
- Privacy and compliance (GDPR/CCPA, consent, data retention, DSAR).
Non-Functional Requirements
- Scaling approach, storage and indexing choices, consistency guarantees.
- Fault tolerance, backpressure strategies.
- Capacity planning with back-of-the-envelope estimates (traffic, CPU for scoring/auction, storage for logs/features, network bandwidth).

Constraints and Assumptions (you may refine)

100 ms total E2E budget (assume ~50–70 ms server-side budget to account for network/client).
Peak traffic: choose and justify a realistic peak QPS (e.g., 20k–100k QPS globally); design should scale.
Near-real-time reporting: ≤5 minutes latency.
Availability target: ≥99.9% per region with graceful degradation.

Quick Overview

Functional Requirements

Real-time ad selection and auction within 100 ms end-to-end.

Targeting: keywords/context, user segments, and geography.

Campaign management: budgets (daily/total), pacing, schedules, frequency capping, creatives.

Ranking: combine advertiser bid with predicted CTR/CVR and quality signals.

Real-time auctions and pricing.

Near-real-time reporting for impressions, clicks, spend, basic conversions.

What to Deliver

APIs

Define an ad request API (input fields) and ad response API (output fields).

Core Data Models

Specify entities for campaign/line item, creative, targeting, budget/pacing, user segments, and event/log schemas.

Architecture

Outline components and data flow: edge gateways, ad selector, feature store, model service, auctioneer, throttling/pacing, caching layers, logging/streaming pipeline, and offline analytics/warehouse.

Auction Mechanics and Strategy

Choose an auction (e.g., first-price, second-price/GSP, VCG), explain incentives and trade-offs.

Additional Topics

Handling cold start for new ads and new users.
Deduplication (impressions/clicks/events; creative dedupe within response).
A/B testing strategy and experiment bucketing.
Privacy and compliance (GDPR/CCPA, consent, data retention, DSAR).

Non-Functional Requirements

Scaling approach, storage and indexing choices, consistency guarantees.
Fault tolerance, backpressure strategies.
Capacity planning with back-of-the-envelope estimates (traffic, CPU for scoring/auction, storage for logs/features, network bandwidth).

Constraints and Assumptions (you may refine)

100 ms total E2E budget (assume ~50–70 ms server-side budget to account for network/client).

Peak traffic: choose and justify a realistic peak QPS (e.g., 20k–100k QPS globally); design should scale.

Near-real-time reporting: ≤5 minutes latency.

Availability target: ≥99.9% per region with graceful degradation.

Design an online ads serving system

Quick Overview

System Design: Low-Latency Targeted Ad Serving (≤100 ms E2E)

Functional Requirements

What to Deliver

Constraints and Assumptions (you may refine)

Solution

Submit Your Answer

Design an online ads serving system

Quick Overview

System Design: Low-Latency Targeted Ad Serving (≤100 ms E2E)

Functional Requirements

What to Deliver

Constraints and Assumptions (you may refine)

Solution

Submit Your Answer