Started with sorting by timestamp and building a map from event ID to the latest event seen.
Start by clarifying the problem constraints (e.g., input size, memory limits, whether timestamps are unique) and then propose a hash map-based solution that keeps the latest timestamp per ID. Walk through the algorithm, discuss edge cases, and then extend to a parallelized version using partitioning and local deduplication followed by a global merge.
Pro tip: Emphasize the trade-offs between time and space complexity, and proactively mention how you would handle ties or out-of-order events. For the parallel version, highlight the importance of partitioning by ID to avoid cross-worker conflicts and ensure correctness.
Ask about input size, memory limits, timestamp uniqueness, and whether the list is sorted. This shows you think before coding and helps tailor the solution.
Use a hash map to store the latest event per ID, iterating through the list and updating if the timestamp is newer. Discuss time O(n) and space O(k) where k is unique IDs.
Cover cases like duplicate timestamps, empty input, large data that doesn't fit in memory, and alternative approaches (e.g., sorting). Mention trade-offs between time, space, and simplicity.
Partition events by ID across workers, each worker deduplicates locally, then merge results by comparing timestamps. Discuss load balancing, fault tolerance, and communication overhead.
Recap the approach, complexity, and parallel design. Suggest testing with small and large datasets, and mention potential optimizations like using a combiner.
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