Start by clarifying requirements and scale, then sketch a high-level entity-relationship diagram covering the core entities and their relationships. Walk through the write path (campaign creation) and read path (ad serving and tracking), and finally discuss indexing and partitioning strategies for scale.
Pro tip: Emphasize how the model supports Netflix's unique advertising needs, such as frequency capping and targeting based on viewing behavior, and discuss trade-offs between consistency and latency in a high-volume environment.
Ask about expected QPS, data volume, and specific features like real-time bidding or frequency capping. This ensures the design meets actual needs and shows you think before coding.
Define entities: Advertiser, Campaign, Creative, TargetingRule, UserIdentifier, DeliveryLog. Describe relationships: Advertiser has many Campaigns, Campaign has many Creatives and TargetingRules, DeliveryLog links UserIdentifier to Creative.
For write path (campaign creation), use normalized tables for consistency. For read path (ad serving), consider denormalized views or caching for low-latency lookups. Explain how tracking events are written to DeliveryLog.
Propose indexes on foreign keys and frequently filtered columns (e.g., campaign status, targeting attributes). Partition DeliveryLog by time (e.g., daily) and consider sharding by user_id or campaign_id for horizontal scaling.
Mention trade-offs like normalization vs. denormalization, consistency vs. latency, and how to handle high-cardinality targeting attributes. Suggest using a columnar store for analytics and a KV store for real-time serving.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.