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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.

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This is a hard difficulty System Design question, commonly asked during Onsite rounds at Snapchat.

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

Design a streaming ads system | Snapchat Interview Question

Quick Overview

This question evaluates a candidate's competency in designing low-latency, scalable, and privacy-compliant ad-insertion platforms, encompassing distributed systems, real-time bidding integration, ad format and measurement standards, content and identity signal processing, pacing/frequency control, and multi-region fault tolerance.

System Design: Ad-Insertion Platform for Live and On-Demand Video

Context

You are designing an end-to-end ad-insertion platform for a consumer video streaming app that serves both live and on-demand (VOD) content on mobile and TV clients. The platform must support client-side and server-side ad insertion, integrate with external ad exchanges, and operate globally with strict latency and reliability goals.

Requirements

Functional
1. Targeting: by user segments, device, geo, time, content metadata, context.
2. Pacing and Budgeting: ensure line items spend smoothly over time and do not overspend.
3. Frequency Capping: per-user and cross-device caps at creative/line-item/advertiser levels.
4. Brand Safety: content classification, blocklists/allowlists, competitive separation, IVT/fraud detection.
5. Ad Formats and Standards: VAST, VPAID, OMID; video/mid-roll/pre-roll/post-roll; skippable or not.
6. Ad Podding: construct multi-ad breaks with duration constraints and category separation.
7. RTB Integration: OpenRTB to demand-side platforms (DSPs) with strict timeouts.
8. Delivery: SSAI for seamless playback and CSAI for interactive units; HLS/DASH manifests through CDNs.
9. Measurement: impressions, quartiles (25/50/75/100), clicks, viewability (OMID), IVT filtering.
10. Deduplication: identity across devices for frequency and measurement.
11. Experimentation: A/B testing of ad policies (e.g., frequency caps, auction rules, pod policies).
Non-Functional
1. Low-latency ad selection: p99 < 100 ms at the edge.
2. High availability: fault tolerance, multi-region active-active, graceful degradation.
3. Privacy and Compliance: GDPR/CCPA/COPPA; consent-aware targeting; data minimization, regional data residency.
4. Scale: tens of millions of DAU, high QPS, live-event spikes.
Assumptions
- Clients support HLS/DASH; mobile/TV SDKs can send OMID signals.
- For SSAI, we control a stitcher/manifest manipulator; for CSAI, clients can render VAST/VPAID.

Deliverables

Design components including the ad decision engine, metadata store, user/segment service, content signals pipeline, and delivery through CDNs/players with VAST/VPAID/OMID support. Explain:

Low-latency selection at the edge (<100 ms), fault tolerance, and multi-region failover.
Privacy compliance and measurement (impressions, quartiles, clicks).
RTB exchange integration, ad podding, cross-device deduplication, and A/B experimentation on ad policies.

Quick Overview

Context

Requirements

Functional

Targeting: by user segments, device, geo, time, content metadata, context.
Pacing and Budgeting: ensure line items spend smoothly over time and do not overspend.
Frequency Capping: per-user and cross-device caps at creative/line-item/advertiser levels.
Brand Safety: content classification, blocklists/allowlists, competitive separation, IVT/fraud detection.
Ad Formats and Standards: VAST, VPAID, OMID; video/mid-roll/pre-roll/post-roll; skippable or not.
Ad Podding: construct multi-ad breaks with duration constraints and category separation.
RTB Integration: OpenRTB to demand-side platforms (DSPs) with strict timeouts.
Delivery: SSAI for seamless playback and CSAI for interactive units; HLS/DASH manifests through CDNs.
Measurement: impressions, quartiles (25/50/75/100), clicks, viewability (OMID), IVT filtering.
Deduplication: identity across devices for frequency and measurement.
Experimentation: A/B testing of ad policies (e.g., frequency caps, auction rules, pod policies).

Non-Functional

Low-latency ad selection: p99 < 100 ms at the edge.
High availability: fault tolerance, multi-region active-active, graceful degradation.
Privacy and Compliance: GDPR/CCPA/COPPA; consent-aware targeting; data minimization, regional data residency.
Scale: tens of millions of DAU, high QPS, live-event spikes.

Assumptions

Clients support HLS/DASH; mobile/TV SDKs can send OMID signals.
For SSAI, we control a stitcher/manifest manipulator; for CSAI, clients can render VAST/VPAID.

Deliverables

Design components including the ad decision engine, metadata store, user/segment service, content signals pipeline, and delivery through CDNs/players with VAST/VPAID/OMID support. Explain:

Low-latency selection at the edge (<100 ms), fault tolerance, and multi-region failover.

Privacy compliance and measurement (impressions, quartiles, clicks).

RTB exchange integration, ad podding, cross-device deduplication, and A/B experimentation on ad policies.

Design a streaming ads system

Quick Overview

System Design: Ad-Insertion Platform for Live and On-Demand Video

Context

Requirements

Deliverables

Solution

Comments (0)

Design a streaming ads system

Quick Overview

System Design: Ad-Insertion Platform for Live and On-Demand Video

Context

Requirements

Deliverables

Solution

Comments (0)