Started with the brute force and they let me finish before asking what the complexity was.
Start by clarifying constraints (e.g., alphabet size, expected input sizes) and then propose an efficient algorithm like KMP or Rabin-Karp. Explain the algorithm's logic, complexity, and why it's better than naive. If time allows, discuss trade-offs and potential optimizations.
Pro tip: Mention that for ML engineering at Google, string matching often appears in tokenization or log processing, so emphasizing scalability and practical trade-offs (e.g., memory vs. speed) can set you apart.
Ask about input constraints: haystack/needle length, alphabet size, expected frequency of calls, and whether preprocessing is allowed.
Select an efficient algorithm such as KMP (O(n+m) time, O(m) space) or Rabin-Karp (average O(n+m), worst O(nm)). Justify based on constraints.
Walk through the chosen algorithm's steps, highlighting how it avoids redundant comparisons (e.g., KMP's failure function).
State time and space complexity, and compare with naive approach. Discuss trade-offs (e.g., KMP's preprocessing vs. Rabin-Karp's simplicity).
Cover empty needle, needle longer than haystack, no match, and multiple matches. Mention potential optimizations like early termination.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.