I started with the ingestion layer since that felt most concrete.
Start by clarifying requirements (scale, latency, accuracy) and then design a pipeline: ingestion, processing/aggregation, storage, and real-time serving. Focus on how to compute top-K frequent exceptions efficiently using approximate algorithms and how to update results in real-time.
Pro tip: Mention that exception signatures should be normalized (e.g., stripping variable data like IDs, timestamps) to group similar exceptions, and discuss the trade-off between exact and approximate counting for scalability.
Ask about scale (events per second, number of services), latency requirements (real-time vs near-real-time), and what defines an exception signature. Also clarify if top-K is global or per-service.
Propose a pipeline: services send exceptions to a message queue (e.g., Kafka), a stream processor (e.g., Flink, Spark Streaming) aggregates by signature, and results are stored in a fast database (e.g., Redis) for serving to a dashboard.
Design a normalization function to create a stable signature (e.g., exception type + normalized stack trace). Use a streaming aggregation to count occurrences per signature over a sliding window.
For scalability, use approximate algorithms like Count-Min Sketch with a heap to maintain top-K, or use a distributed approach where each node computes local top-K and merges. Discuss trade-offs between exact and approximate.
Store the top-K results in a low-latency store (e.g., Redis sorted sets) and push updates to on-call engineers via WebSockets or a dashboard that polls periodically. Ensure the system can handle high write throughput.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.