My first instinct was just sort and slice, which works but they pushed back on performance immediately.
Clarify requirements (e.g., tie-breaking, rating scale, memory constraints) and propose a two-phase approach: filter movies by preferred genres, then use a min-heap of size K to find top K by rating in O(N log K) time. Discuss trade-offs between heap, quickselect, and full sort, and handle large datasets with streaming or external sorting if needed.
Pro tip: Mention that for K much smaller than N, a min-heap is optimal, but if K is large, quickselect (average O(N)) may be better; also discuss tie-breaking and whether ratings are unique.
Ask about tie-breaking rules, rating scale, memory limits, and whether the dataset fits in memory. Confirm if K is small relative to N and if genres are exact matches.
Propose filtering by genre (e.g., using a set for O(1) lookups) and then using a min-heap of size K to track top K ratings. Discuss alternatives like quickselect or sorting based on K and N.
Explain that filtering is O(N) and heap operations are O(N log K), total O(N log K) time and O(K) space. Compare with O(N log N) sorting and O(N) average quickselect.
Address empty results, fewer than K movies, duplicate ratings, and streaming data. For 10^6 entries, discuss external sorting or distributed processing if memory is limited.
Reiterate the chosen approach, its trade-offs, and why it's optimal for the given constraints. Mention potential optimizations like parallel filtering.
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