I went straight to the heap-based solution because that's what I'd drilled, but then the read vs write framing made me realize I'd only answered half the question.
Start by outlining the algorithmic solution for a single query: count frequencies with a hash map, then use a min-heap or bucket sort to find the top K. Then discuss how the design changes for read-heavy vs. write-heavy systems: for read-heavy, precompute and cache results; for write-heavy, optimize for fast updates and possibly use approximate algorithms.
Pro tip: Mention that in real systems, exact top-K may not be necessary; approximate algorithms like Count-Min Sketch with a heap can handle high throughput with bounded error, which is often acceptable and more scalable.
Ask about data size, update frequency, query frequency, latency requirements, and whether exact results are needed. This determines the appropriate trade-offs.
Explain the standard approach: count frequencies using a hash map, then use a min-heap of size K or bucket sort to extract top K. Discuss time and space complexity.
Precompute and cache the top K results, possibly using a background process to update the cache periodically. Use a read-optimized data store and consider caching layers.
Optimize for fast updates: use a streaming approach with a Count-Min Sketch for approximate counts and a heap for top K, or use a distributed stream processor like Flink. Avoid recomputing from scratch on each write.
Talk about partitioning, distributed processing, consistency vs. availability, and memory vs. accuracy trade-offs. Mention monitoring and adjusting parameters like K and error bounds.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.