Start by clarifying requirements and scale, then design a three-part system: an offline pipeline for bulk uploads and attribute-based audience creation, a serving layer for low-latency membership checks, and a real-time decision engine that combines audience membership with ad request context. Emphasize trade-offs between latency, accuracy, and cost, and discuss how to handle updates and consistency.
Pro tip: Netflix operates at massive scale with a global user base, so highlight how your design handles millions of QPS with sub-10ms latency, and discuss using approximate membership structures like Bloom filters or cuckoo filters to reduce memory footprint while accepting a small false positive rate.
Ask questions to understand the expected number of users, audiences, ad requests per second, latency requirements, and consistency needs. Establish functional and non-functional requirements.
Define how audiences are represented (e.g., attribute-based rules, explicit user ID lists) and design a scalable ingestion pipeline for bulk uploads, including validation, deduplication, and storage.
Architect a low-latency serving layer that evaluates audience membership in real-time, using in-memory stores, caching, and efficient data structures. Integrate with the ad server to make targeting decisions.
Explain how audience updates propagate (e.g., via pub/sub, versioning), how to handle eventual consistency, and how to scale horizontally. Discuss partitioning and replication strategies.
Compare exact vs. approximate membership, synchronous vs. asynchronous updates, and cost vs. latency. Mention monitoring, failure handling, and potential optimizations like precomputation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.