Spent the first few minutes fumbling through clarifications I should've had a mental checklist for.
Start by clarifying requirements (scale, latency, consistency, time window definition) and then propose a high-level architecture with a write path for ingesting queries and a read path for serving Top-K. For the all-time variant, use a distributed counting system with sharded counters and a heap-based aggregation; for the recent window, use a time-bucketed sliding window approach with incremental updates. Discuss trade-offs between accuracy, latency, and resource usage, and consider optimizations like caching and approximate algorithms.
Pro tip: Demonstrate awareness of real-world constraints by mentioning that exact Top-K over a sliding window is expensive, so you might use a combination of exact counts for recent buckets and approximate algorithms (e.g., Count-Min Sketch) for older data, with periodic batch reconciliation.
Ask about scale (QPS, number of unique queries), latency requirements, consistency needs, and how 'recent' is defined (e.g., last hour, last day). Also clarify if the Top-K should be exact or approximate, and whether the results need to be real-time or can be slightly stale.
Propose a distributed system with an ingestion layer (e.g., Kafka) to handle query logs, a processing layer (e.g., stream processing or batch jobs) to count frequencies, and a serving layer (e.g., cache or database) to return Top-K. Separate the all-time and recent window pipelines if needed.
For all-time counts, use sharded counters (e.g., Redis) with a min-heap or sorted set for Top-K. For recent window, use time-bucketed counters (e.g., per-minute buckets) and a sliding window aggregation, or use a Count-Min Sketch with a decaying window. Discuss how to merge results from shards.
Explain how to scale horizontally (sharding by query hash), handle hot keys, and manage memory. Discuss trade-offs: exact vs approximate (e.g., using Space-Saving algorithm), latency vs accuracy, and cost. Mention caching strategies for frequent Top-K requests.
Cover how to handle node failures (replication, checkpointing), data consistency (eventual vs strong), and monitoring (tracking ingestion lag, Top-K staleness). Also mention how to handle sudden spikes in traffic.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.