Started with the naive approach, checking every position against every fragment, and they pushed back pretty fast on the complexity.
Start by clarifying requirements: are fragments matched case-sensitively? Should matches be non-overlapping? Then propose an efficient algorithm using a trie or Aho-Corasick for multi-pattern matching, and discuss how to handle overlaps with a longest-match-wins rule. Finally, analyze time and space complexity and consider edge cases.
Pro tip: Mention that you would first discuss the trade-offs between simplicity and performance: a naive approach might be acceptable for small inputs, but for large-scale systems like Uber's, an optimized solution using Aho-Corasick is preferable. Also, explicitly state your assumptions about overlapping matches and confirm with the interviewer.
Ask about case sensitivity, overlapping matches, and whether to prefer longest match. Confirm the expected output format and any constraints on input size.
For multiple fragments, build a trie or Aho-Corasick automaton to scan the sentence in O(n + m + k) time, where n is sentence length, m is total fragment length, and k is number of matches.
If overlaps are allowed, collect all matches with their start and end indices. For longest-match-wins, sort matches by start index and then by length descending, and greedily select non-overlapping matches.
Iterate through the sentence, inserting brackets around selected matches. Use a StringBuilder for efficiency, and skip characters that are part of a match.
State time and space complexity, and walk through examples including edge cases like empty fragments, no matches, and multiple overlapping matches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.