I started at the impression level and worked upward, which in hindsight was the wrong call.
Start by clarifying the scope and key requirements of the ads demand-intake system, such as the types of ads, targeting capabilities, and reporting needs. Then, design a hierarchical data model that captures the relationships from advertiser accounts down to individual impression events, ensuring scalability and performance. Finally, discuss how the model supports core use cases like campaign management, pacing, and analytics.
Pro tip: Emphasize the importance of denormalization and pre-aggregation for performance at Netflix scale, and discuss how you would handle late-arriving or duplicate events in the impression stream.
Ask questions to understand the system's purpose, scale, and key features. Identify the main entities and the granularity of data needed.
List the main entities (e.g., Account, Advertiser, Campaign, Ad Group, Creative, Targeting, Impression) and their key attributes, considering both business and technical needs.
Define the relationships between entities, such as one-to-many from Account to Advertiser, and from Ad Group to Creative. Explain the hierarchy and how it supports data access patterns.
Discuss how to handle high-volume impression events, including partitioning, indexing, and potential use of columnar stores or streaming pipelines.
Walk through how the model supports key use cases like campaign setup, ad serving, pacing, and reporting, ensuring all requirements are met.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a separate targeting table keyed to the ad group, with a type-value structure for flexibility.
Start by clarifying the scope: are you designing a data model for a targeting service that evaluates rules at request time, or for analytics? Then walk through how you'd represent each targeting dimension (geo, device, audience, frequency) in a flexible, scalable schema, and discuss trade-offs like normalization vs. denormalization, indexing, and caching. Finally, explain how the model supports efficient rule evaluation and updates.
Pro tip: Emphasize that frequency caps require a stateful, time-windowed counter, which is often the hardest part—discuss how you'd store and update these counters at scale (e.g., using Redis or a distributed counter service) while ensuring low latency and consistency.
Ask about scale (number of users, rules, updates per second), latency requirements, and whether targeting is evaluated in real-time or batch. This shows you think before designing.
Identify entities like User, Device, Geo, AudienceSegment, Campaign, and TargetingRule. Describe how they relate (e.g., many-to-many between users and segments) and how you'd model these relationships.
Explain how you'd store geo (hierarchical or flat), device type (enum or reference table), audience segments (bitmaps, sets, or join tables), and frequency caps (counters with TTL). Discuss indexing strategies for fast lookups.
Detail how you'd track and enforce frequency caps, including time windows (e.g., per day, per campaign), storage (e.g., Redis sorted sets), and how to handle distributed updates and consistency.
Compare approaches: normalized vs. denormalized, SQL vs. NoSQL, precomputed vs. on-the-fly evaluation. Explain how your choices affect performance, flexibility, and maintainability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the interview got genuinely hard.
Start by framing the problem: explain that your model treats bidding as a constrained optimization problem where advertisers submit bids and the system allocates impressions while respecting budget and pacing constraints. Then walk through the key components: bid types, pacing algorithms, budget caps, and auction mechanics, highlighting trade-offs and how they interact. Finally, connect these to Netflix's ad-supported tier, emphasizing scalability, fairness, and user experience.
Pro tip: Emphasize that pacing and budget caps are not just constraints but also levers to improve long-term advertiser ROI and system stability—showing you think beyond immediate revenue. Also, mention that second-price auctions incentivize truthful bidding, but in practice, platforms often use modified versions to balance revenue and advertiser trust.
Explain the bid types your model supports (e.g., CPM, CPC, CPA) and how they map to advertiser goals. Clarify that bids represent the maximum willingness to pay for an outcome.
Detail how pacing smooths spend over time (e.g., probabilistic throttling, token buckets) and how budget caps prevent overspend. Mention daily vs. lifetime budgets and how pacing interacts with them.
Contrast first-price and second-price auctions: in first-price, the winner pays their bid; in second-price, they pay the second-highest bid plus a small increment. Discuss implications for bidding strategy and revenue.
Highlight trade-offs: second-price auctions encourage truthful bidding but may reduce revenue; pacing can delay delivery but improves ROI. Explain how your model balances these via simulations or real-time feedback.
Relate the model to Netflix's ad tier: need for scalability, low latency, and user experience. Mention how you'd handle high-volume auctions and ensure fairness among advertisers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.