I'd done this kind of question before so the skeleton came out fine.
Start by clarifying requirements and scale, then walk through the high-level architecture from ad request to billing. Dive into each component—candidate generation, ranking, auction, pacing, attribution, and billing—explaining trade-offs and how they integrate. Emphasize Netflix's unique context (e.g., ads on a subscription platform, focus on user experience).
Pro tip: Netflix is new to ads, so highlight how you'd balance ad revenue with subscriber experience—e.g., limiting ad load, ensuring relevance, and avoiding disruption. Also, discuss how you'd leverage Netflix's existing data and infrastructure for targeting and measurement.
Ask about expected QPS, ad types (video, display), targeting capabilities, latency requirements, and business goals. Establish assumptions for the design.
Outline the end-to-end flow: ad request from client, ad server, candidate generation, ranking, auction, ad selection, rendering, and tracking. Mention key components like ad server, targeting service, pacing service, attribution service, and billing service.
Explain candidate generation (e.g., targeting, retrieval), ranking (e.g., ML models, features), auction mechanics (e.g., second-price, reserve), and pacing (e.g., budget smoothing). Discuss trade-offs and scalability.
Describe how to track ad events (impressions, clicks, conversions) and attribute them to the correct ad campaign. Explain billing models (CPM, CPC, CPA) and how to ensure accurate and timely billing.
Summarize key design decisions, discuss potential bottlenecks, and tailor to Netflix's needs (e.g., integration with existing microservices, personalization, ad load management).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went straight to Redis sliding window counters and talked through the latency tradeoff.
Start by clarifying requirements: define N, the time window, and whether capping is per user, per ad, or per campaign. Then propose a scalable, low-latency solution using a distributed store like Redis with TTL-based counters, and discuss trade-offs between accuracy, memory, and performance.
Pro tip: Mention that frequency capping should be enforced at ad-serving time with a fast read, and that you'd use a sliding window or token bucket to avoid bursty behavior at window boundaries. Also highlight the need for graceful degradation if the store is unavailable.
Ask about N, window duration, whether capping is per user, per ad, or per campaign, and the expected scale (QPS, number of users). Confirm latency and accuracy requirements.
Propose a key structure like user_id:ad_id with a counter and TTL. Use a distributed in-memory store (e.g., Redis) for low-latency reads/writes, and consider sharding by user_id for scalability.
Use atomic increments (e.g., INCR) with expiration to track views. For sliding windows, use a sorted set of timestamps or a token bucket. Ensure atomicity to avoid race conditions.
Before serving an ad, check the counter; if it exceeds N, skip the ad. This check must be fast and part of the critical path, so optimize for read performance.
Discuss trade-offs: fixed vs. sliding window, memory vs. accuracy, and handling of store failures (e.g., fail-open or fail-closed). Mention monitoring and configurable parameters.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Frame the problem as a real-time budget pacing system that controls spend rate using feedback loops and throttling mechanisms. Discuss how you would monitor spend, compare against a target pace, and adjust ad delivery dynamically to avoid early exhaustion while maximizing value.
Pro tip: Emphasize the trade-off between smooth pacing and opportunity cost—sometimes it's better to spend faster if high-value impressions are available, so incorporate a value-based pacing strategy rather than purely time-based.
Clarify that the goal is to spend the daily budget evenly over the day, but also consider business objectives like maximizing conversions or ROI. Define key metrics: spend rate, budget utilization, and pacing error.
Implement a controller that continuously compares actual spend against a target spend curve (e.g., linear or front-loaded). Use the difference to adjust the bid or throttle ad delivery in real-time.
Decide between probabilistic throttling (randomly dropping requests), bid modification (lowering bids to reduce win rate), or budget caps per time window. Each has trade-offs in terms of latency, accuracy, and system complexity.
Ensure the solution works at Netflix scale: low-latency decisions, distributed counters, and fault tolerance. Consider using a distributed rate limiter or a centralized pacing service with local caching.
Continuously monitor pacing performance and adjust parameters. Use A/B testing to compare pacing strategies and incorporate machine learning for predictive pacing based on historical data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scope and scale of the ads system, then walk through the end-to-end pipeline from event ingestion to billing, emphasizing exactly-once processing and idempotency. Highlight how you ensure accuracy through reconciliation, auditing, and attribution modeling, and discuss trade-offs between latency and correctness.
Pro tip: Emphasize the importance of idempotent writes and deterministic aggregation to avoid double-counting, and mention how you'd handle late-arriving events with watermarks and reprocessing. Also, discuss how you'd design for auditability and explainability to build trust with advertisers.
Ask about expected event volume, latency requirements, and accuracy guarantees. Understand the types of ads (e.g., display, video) and attribution models (e.g., last-click, multi-touch).
Describe how events are collected (e.g., client-side beacons, server-side logs), ingested into a durable queue (e.g., Kafka), and processed with stream processing (e.g., Flink) for real-time aggregation. Ensure exactly-once semantics via idempotent producers and transactional sinks.
Explain how you join ad impressions/clicks with conversion events, apply attribution rules, and compute spend per advertiser. Use windowing and watermarks to handle late data, and store results in a scalable database (e.g., Cassandra, BigQuery).
Describe reconciliation jobs that compare real-time aggregates with batch reprocessing, and auditing mechanisms to detect discrepancies. Implement idempotent billing writes and maintain an immutable ledger for financial integrity.
Discuss how the system handles failures (e.g., retries, dead-letter queues), scales horizontally, and supports backfills. Mention monitoring, alerting, and SLAs for data freshness and accuracy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.