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I spent the first few minutes trying to think of a regex approach and the interviewer kind of just waited.
Start by clarifying the problem constraints and edge cases, then outline a single-pass state machine that tracks parenthesis depth and quote state to correctly identify record boundaries. After splitting, extract the email token by locating the '@' symbol and trimming surrounding characters, using a set for deduplication. Finally, discuss trade-offs and potential optimizations.
Pro tip: Demonstrate maturity by proactively discussing how to handle malformed input (e.g., unbalanced parentheses or quotes) and mentioning that a single-pass approach minimizes memory usage and is suitable for large logs. Also, consider mentioning that email extraction could be more robust with a regex, but the state machine is more efficient for this specific parsing task.
Ask about the definition of a valid email, handling of nested parentheses, escaped quotes, and whether display names can contain semicolons. Confirm that deduplication should be case-insensitive or exact.
Define states: normal, inside parentheses (track depth), inside quotes (single or double). Only treat semicolon as a delimiter when in normal state. Iterate through the string character by character.
For each record, find the '@' symbol and extract the contiguous token around it, trimming whitespace and punctuation. Validate that it looks like an email (e.g., contains a dot after '@').
Use a hash set to store unique emails, preserving order if needed. Return the list of unique emails.
Discuss time complexity O(n) and space O(n) for the set. Compare with regex-based approaches, noting that regex may be simpler but less efficient for very large logs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.