My first instinct was to group by user and event then sort by timestamp, which is fine, but I initially forgot to handle the sliding 24-hour window correctly.
Clarify the problem constraints (e.g., whether the 24-hour window is sliding or fixed, and if timestamps are sorted). Then propose an efficient solution: group logs by user and event type, sort each group by timestamp, and use a sliding window (two-pointer) to check if any window contains more than k events. Analyze time and space complexity, and discuss trade-offs with alternative approaches like bucketing.
Pro tip: Demonstrate Amazon leadership principles by proactively discussing edge cases (e.g., duplicate timestamps, empty logs) and scalability (e.g., handling millions of logs with distributed processing). This shows customer obsession and ownership.
Ask questions to understand the input format, size, whether timestamps are sorted, and the definition of a 24-hour window (sliding vs. fixed). Confirm the output format (list of user IDs).
Propose grouping logs by user and event type, then for each group, sort by timestamp and use a sliding window to count events within any 24-hour period. Check if count exceeds k.
Discuss time complexity (O(N log N) due to sorting) and space complexity (O(N)). Mention potential optimizations like bucketing if timestamps are bounded or using a hash map for counts.
Address edge cases: empty input, k=0, multiple events at same timestamp, and users with no qualifying events. For large-scale data, discuss distributed processing (e.g., MapReduce) or streaming algorithms.
Walk through a small example to verify correctness, and suggest test cases including boundary conditions (exactly k events, events spanning window boundaries).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.