Sliding window with a hashmap, pretty standard once you see it.
Use a sliding window with two pointers to maintain a window of unique show names, expanding the right pointer and shrinking the left when a duplicate is found. Track the longest window's start and end indices, normalizing case by converting show names to lowercase for comparison.
Pro tip: Clarify edge cases upfront, such as empty input or all unique shows, and mention that the space complexity is O(min(n, m)) because the hash map only stores distinct shows within the current window.
Confirm that show names are case-insensitive, indices are 0-based, and handle empty or single-element arrays. Ask if the input can be modified or if additional space is allowed.
Use two pointers (left and right) to represent the current window. Maintain a hash map (or dictionary) to store the last seen index of each show name (lowercased).
Iterate right from 0 to n-1. If the current show is in the map and its last index >= left, update left to last index + 1. Update the map with the current index. Track the maximum window length and its start/end indices.
After the loop, return the start and end indices of the longest window. Explain that time is O(n) because each element is visited at most twice, and space is O(min(n, m)) due to the hash map storing at most m distinct shows.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a sliding window with a hash map to track the last seen index of each show name, maintaining the longest unique-name window seen so far. For each incoming show, update the window start if the show was seen within the current window, then update the longest window if needed. Analyze the time and space complexity, emphasizing O(1) amortized update time and O(min(n, m)) memory where n is window size and m is distinct show names.
Pro tip: Explicitly discuss the trade-off between memory and latency: storing last-seen indices for all distinct names ensures O(1) updates but uses memory proportional to distinct names, which is acceptable for streaming but may need bounding in extreme cases. Also, mention that the window can be reported in O(1) by maintaining the start and end indices.
Confirm that the stream yields one show name at a time, and we need to report the current longest window of unique names after each element. Discuss assumptions about memory limits and whether the window must be contiguous in the stream.
Maintain a hash map mapping show name to its most recent index in the stream, and variables for window start, current window length, and best window (start, end, length). For each new show at index i, if it exists in the map and its last index >= window start, move window start to last index + 1. Update the map with i, and update best window if current length exceeds best.
Each element requires O(1) average time for hash map lookup and update, and O(1) for window adjustments. Thus, per-element update is O(1) amortized. Reporting the current longest window is O(1) by maintaining the best window boundaries.
Memory usage is O(min(W, D)) where W is the maximum window length and D is the number of distinct show names seen. In the worst case, if all names are unique, memory is O(D). Discuss potential optimizations like capping the map size if memory is constrained, but note that it may affect correctness.
Address trade-offs: using a hash map gives fast updates but uses memory; alternative data structures like balanced BST would increase update time to O(log D). Mention edge cases: empty stream, all duplicates, very long unique sequences, and handling of window reporting when no unique window exists.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.