Use a sliding window with a hash set to track the current window's unique strings, expanding the right pointer and shrinking the left when a duplicate is found. Keep track of the maximum window length and its start index to return the actual subarray. This yields O(n) time and O(k) space, where k is the number of distinct strings.
Pro tip: Clarify whether the subarray must be contiguous (yes) and whether returning the subarray itself or just its length is required. Also, mention edge cases like empty input or all duplicates, and discuss how the solution scales for streaming data.
Confirm that the subarray must be contiguous, and ask whether to return the subarray or its length. Discuss edge cases: empty array, single element, all duplicates, and very large input.
Explain that a brute-force check of all subarrays is O(n^2) or worse, so a sliding window with a hash set gives O(n) time. The window represents a contiguous subarray with no duplicates.
Initialize left=0, a hash set, and variables for max length and start index. Iterate right from 0 to n-1: while the current string is in the set, remove the string at left and increment left; then add the current string, and update max length and start if the window is larger.
State that each element is added and removed at most once, so time is O(n) and space is O(k) where k is the number of distinct strings. Mention that using a hash map to store last seen indices can avoid the while loop, but the set approach is simpler and still O(n).
Walk through a small example like ['a','b','a','c'] to verify the algorithm. Discuss how to adapt for streaming data or if the array is extremely large and cannot fit in memory.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.