Seemed straightforward at first but I second-guessed myself on what counts as a duplicate.
Start by clarifying what constitutes a duplicate (e.g., exact match vs. fuzzy match) and the scale of the data. Then propose a solution using a hash-based approach for O(n) time complexity, and discuss trade-offs like memory usage and whether to preserve order or keep the first occurrence.
Pro tip: Mention that for large datasets, a distributed approach like MapReduce or using a Bloom filter for approximate deduplication can be more efficient, showing awareness of scalability beyond a single machine.
Ask questions to understand what defines a duplicate (e.g., exact email address, case-insensitive, or based on multiple fields) and the expected data size and memory constraints.
Select an appropriate data structure such as a hash set or hash map to track seen emails, enabling O(1) average-time lookups.
Iterate through the records, checking each against the set; if not seen, add to the set and keep the record; otherwise, skip it. This preserves order and keeps the first occurrence.
Discuss time and space complexity (O(n) time, O(n) space) and alternatives like sorting (O(n log n) time, O(1) extra space) or using a database with unique constraints.
For very large datasets, propose distributed solutions like MapReduce or external sorting, and mention approximate methods like Bloom filters if exactness isn't critical.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.