Priority queue was the obvious move here and I went with it.
Start by clarifying the problem constraints (size of dataset, memory limits, whether K is small or large) and then propose a heap-based solution for streaming data or quickselect for in-memory data. Compare trade-offs between time complexity, space complexity, and practical considerations like distributed processing if the dataset is too large for a single machine.
Pro tip: Mention that for very large datasets, a distributed approach like MapReduce with local top-K per partition followed by a global merge is often used in production systems like Uber's. This shows you think beyond textbook algorithms and consider real-world scalability.
Ask about the dataset size, memory availability, whether the data is static or streaming, and the expected value of K relative to N. This determines the appropriate algorithm and data structures.
Suggest sorting the entire dataset and taking the first K elements, which is O(N log N) time. Acknowledge its simplicity but note inefficiency for large N.
For better performance, use a min-heap of size K for O(N log K) time, ideal when K is small. Alternatively, use quickselect for average O(N) time when the data fits in memory and K is not too small.
Compare time and space complexity, stability, and suitability for streaming data. Mention edge cases like duplicate elements, K > N, or memory constraints.
If the dataset is too large for one machine, describe a distributed approach: partition data, compute local top-K per partition, then merge and compute global top-K.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.