I knew the sliding window approach cold from the classic version, so I felt fine until they said bitmask.
First, clarify the problem constraints and the bitmask requirement, then propose a sliding window approach where the bitmask tracks seen names via a hash function. Explain how to handle collisions and justify why the bitmask is sufficient for uniqueness within the window, and finally analyze time and space complexity.
Pro tip: Acknowledge that bitmask uniqueness is probabilistic due to hash collisions, and discuss how to mitigate (e.g., double hashing or fallback to exact check) to show depth. Also, relate the tradeoff to Netflix's scale: memory savings vs. potential false positives.
Ask if names are from a known set or arbitrary; confirm that bitmask size is fixed (e.g., 64-bit) and discuss implications. Clarify whether exact uniqueness is required or if probabilistic is acceptable.
Use two pointers to maintain a window of unique names. For each name, compute a hash to set a bit; if the bit is already set, shrink the window from the left until the bit is cleared.
Explain that collisions can cause false positives (treating distinct names as duplicates). Propose solutions: use multiple hash functions, or maintain a secondary exact structure for verification, and discuss the tradeoff.
Time: O(n) average with sliding window, but worst-case O(n^2) if many collisions cause frequent shrinking. Space: O(1) for bitmask vs. O(k) for hash set. Discuss when bitmask is beneficial (memory-constrained) vs. when hash set is better (exactness).
Walk through examples: all unique, all duplicates, collisions, empty array. Verify correctness and discuss how to detect and handle collisions in practice.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.