Start by clarifying requirements: define CTR as unique clicks divided by impressions per campaign, with a rolling 24-hour window and handling of late events. Then outline a streaming architecture using event-time processing, watermarks, and stateful operators to maintain per-campaign counts and deduplicate clicks. Finally, discuss trade-offs between accuracy and latency, and propose a scalable implementation using a framework like Flink or Kafka Streams.
Pro tip: Emphasize the importance of defining a watermark strategy to balance completeness and latency, and mention that click deduplication should be based on a unique click ID, not user ID, to avoid overcounting.
Ask about the definition of CTR, the expected event rate, the acceptable latency, and the handling of late events. Confirm that clicks should be deduplicated by a unique click ID and that the window is event-time based.
Propose using a stream processing framework (e.g., Apache Flink) with event-time processing, watermarks, and keyed state per campaign. Maintain a rolling window of 24 hours using a sliding window or a custom state with timers.
For clicks, use a deduplication mechanism such as a Bloom filter or a state store with click IDs and TTL. For impressions, simply count events. Compute CTR as unique clicks / impressions per campaign.
Define a watermark strategy that allows late events up to a certain threshold (e.g., 1 hour). Use allowed lateness to update results, and consider side outputs for very late events.
Address partitioning by campaign ID, state size management, and trade-offs between accuracy (longer watermark delay) and latency (shorter delay). Mention possible optimizations like approximate deduplication.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.