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Microsoft·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Microsoft SWE interview with a system design/coding question on a time-based key-value store. Pretty focused session, one meaty problem with a clear optimal path if you know your binary search.

Questions Asked (1)

Q1

Design a time-based key-value store that supports storing multiple values per key at different timestamps, and retrieving the value associated with the largest timestamp that does not exceed a given query timestamp.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

The set operation was trivial since timestamps are guaranteed to be strictly increasing per key, so you just append.

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AI HintsAI Generated

Suggested Approach

Clarify the requirements and constraints (e.g., number of operations, timestamp range, concurrency) before designing. Propose a data structure where each key maps to a list of (timestamp, value) pairs sorted by timestamp, and use binary search to find the largest timestamp ≤ query timestamp. Discuss trade-offs between different implementations (e.g., hash map + sorted list vs. balanced BST) and analyze time/space complexity.

Pro tip: Mention that timestamps are monotonically increasing per key, so appending to the list maintains sorted order, and binary search gives O(log n) retrieval. Also, consider edge cases like querying before the first timestamp and handling duplicate timestamps.

1. Clarify Requirements and Constraints

Ask about expected number of operations, timestamp range, concurrency needs, and whether timestamps are unique per key. This shows you think about real-world usage and helps tailor the solution.

2. Choose Data Structures

Propose a hash map from key to a list of (timestamp, value) pairs, where the list is kept sorted by timestamp. Alternatively, consider a balanced BST or skip list for more dynamic scenarios.

3. Design Set Operation

For set(key, value, timestamp), append the new (timestamp, value) to the list for that key. Since timestamps are monotonically increasing, the list remains sorted. If timestamps can be out of order, insert in sorted position.

4. Design Get Operation

For get(key, timestamp), retrieve the list for the key and use binary search to find the largest timestamp ≤ the query timestamp. Return the corresponding value, or empty string if none exists.

5. Analyze Complexity and Trade-offs

Discuss time complexity: O(1) average for set (append), O(log n) for get (binary search). Space: O(n) total entries. Compare with alternatives like using a tree map per key (O(log n) for both) and mention concurrency considerations.

Key Points to Mention

  • Use a hash map to store key -> list of (timestamp, value) pairs.
  • Maintain the list sorted by timestamp; since timestamps are increasing, appending keeps it sorted.
  • Use binary search to find the largest timestamp ≤ query timestamp in O(log n) time.
  • Handle edge cases: query timestamp before first entry, duplicate timestamps, and non-existent keys.
  • Discuss trade-offs: hash map + sorted list vs. balanced BST (e.g., TreeMap) for different operation frequencies.
  • Consider concurrency and scalability if the store is distributed or accessed by multiple threads.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.