Use a sliding window of length equal to p and compare character frequency counts between the window and p. Optimize by maintaining a running count and updating it as the window slides, achieving O(n) time.
Pro tip: Mention that you can use an array of size 26 for lowercase letters to achieve constant space and faster operations, and discuss how this approach can be extended to Unicode by using a hash map.
Ask about string lengths, character set (e.g., lowercase English letters), and whether p can be longer than s. Confirm that anagrams are case-sensitive and consider empty strings.
Decide to use a fixed-size array (e.g., 26 for lowercase) or hash map to count character frequencies. Explain why this is efficient for anagram comparison.
Compute frequency of p and the first window of s. Then slide the window one character at a time, updating counts by removing the left character and adding the right character.
After each slide, compare the window's frequency array with p's. If they match, record the starting index. Continue until the window reaches the end of s.
State time complexity O(n) and space O(1) (or O(k) for hash map). Discuss potential optimizations like using a difference counter to avoid full array comparison each time.
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