Felt fine at first since I'd seen a version of this before.
Clarify the exact normalization rules and edge cases (e.g., multiple plus signs, dots in domain) before coding. Then implement a clean solution by splitting the email at '@', processing the local part (remove dots, truncate at first '+'), and reassembling. Discuss trade-offs like in-place vs. new string, and test with examples.
Pro tip: Mention that Gmail's canonicalization is specific to Gmail and not a general email standard; for a company like Stripe, you'd likely need to handle multiple providers or store the original email for delivery. This shows awareness of real-world constraints.
Ask about the definition of 'Gmail-style': does it apply only to gmail.com addresses? How to handle multiple plus signs, dots in domain, or invalid emails? Confirm expected output format.
Explain the steps: split at '@', process local part by removing all '.' and truncating at the first '+', then concatenate with the domain. Mention that domain is left unchanged.
Write clean code (e.g., in Python) using string methods. Walk through examples like 'first.last+tag@gmail.com' -> 'firstlast@gmail.com' and edge cases like 'a+b+c@d.com' -> 'a@d.com'.
Talk about time/space complexity (O(n) time, O(n) space). Consider if normalization should be provider-specific, and how to handle non-Gmail addresses. Mention potential need to store original email for sending.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the normalization rules (e.g., lowercase, remove dots in local part, strip plus aliases) and confirm whether grouping is by normalized email or by domain. Then use a hash set to track unique normalized emails, or a hash map to group original emails by normalized form, and count the unique keys.
Pro tip: Mention that normalization rules can be provider-specific (e.g., Gmail ignores dots and plus aliases, but other providers may not), so it's crucial to ask the interviewer for the exact rules to avoid over- or under-normalizing.
Ask about the normalization rules: case sensitivity, dot removal, plus alias handling, and whether subaddressing is universal. Confirm if grouping should be by normalized email or by domain.
Decide between a hash set (for counting unique) or a hash map (for grouping). Consider time and space complexity; both are O(n) time and O(n) space.
Write a function to normalize an email according to the clarified rules. Handle edge cases like invalid emails or missing parts.
Iterate through the list, normalize each email, and either add to the set or append to the map's list for that normalized key.
Return the count of unique normalized emails or the grouped map. Discuss potential optimizations, such as early termination or parallel processing for large lists.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where things got interesting and a bit stressful.
Start by clarifying requirements: what types of rules (filtering, blocking, routing), who manages them, and how they interact with existing email flow. Then propose a rule engine that evaluates incoming emails against user-defined rules, with actions like deliver, block, or route. Discuss trade-offs in storage, evaluation order, and scalability, and consider how to handle edge cases like rule conflicts and performance.
Pro tip: Emphasize idempotency and auditability: rules should be applied exactly once per email, and every action should be logged for debugging and compliance. This shows you think about production reliability, not just functionality.
Ask about rule types (e.g., exact match, regex, domain), actions (deliver, block, forward, label), and who can create rules (users, admins). Also clarify scale: number of users, emails per second, and rule complexity.
Propose a schema for rules: each rule has conditions (field, operator, value) and actions (type, parameters), plus priority and enabled status. Consider storing rules in a database with indexing for fast lookup.
Design an engine that evaluates rules in priority order, short-circuits on first match, and applies actions. Discuss caching compiled rules for performance and handling conflicts (e.g., first-match vs. all-match).
Explain where the rule engine fits: after receiving email but before delivery. Ensure it doesn't block the main email flow; consider async processing or a separate service.
Discuss trade-offs: regex performance vs. exact match, rule evaluation latency, storage costs, and consistency. Propose sharding by user or using a distributed cache for rules.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.