I went with a fixed-size sliding window and a frequency array to track character counts.
Use a sliding window of length equal to the target word, maintaining a frequency count of characters in the window and comparing it to the target's frequency count. For each new character, update the window and check if the counts match, recording the end position if they do. Optimize the comparison by tracking the number of matching characters or using a hash of the frequency array.
Pro tip: Discuss trade-offs between different approaches, such as using a fixed-size array vs. hash map for character counts, and mention how to handle Unicode or large character sets. Also, clarify assumptions about the stream (e.g., infinite, real-time constraints) and propose a solution that processes each character in O(1) time.
Ask about the character set (e.g., ASCII, Unicode), target word length, and whether the stream is infinite. Confirm that we need to output positions where an anagram ends, i.e., the end index of the window.
Decide on a frequency map for the target and the sliding window. For small character sets, use a fixed-size array; for larger sets, use a hash map. Consider maintaining a count of matches to avoid full comparison each time.
Initialize the window with the first L characters (L = target length). For each subsequent character, add it to the window and remove the oldest character. Update the frequency counts and the match count accordingly.
After each update, if the match count equals the number of distinct characters in the target, the current window is an anagram. Record the end index (current position) as a valid position.
State that each character is processed in O(1) time (assuming constant alphabet size), leading to O(n) total time and O(1) extra space. Discuss edge cases: target longer than stream, empty target, repeated characters, and stream ending.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.