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Microsoft·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Microsoft SWE interview with a system design flavored coding problem. The core challenge was building a time-based key-value store, and the whole session basically lived or died on whether you knew binary search cold.

Questions Asked (1)

Q1

Design and implement a time-based key-value store that can store multiple values for the same key at different timestamps, and retrieve the most recent value at or before a given timestamp.

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

The set operation is straightforward, just append to a list per key.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a design using a hash map from keys to sorted lists of (timestamp, value) pairs, with binary search for efficient retrieval. Discuss trade-offs between different data structures and consider concurrency and scalability aspects.

Pro tip: Mention that timestamps are monotonically increasing per key, which allows appending to the list and binary search for retrieval. Also, discuss how to handle edge cases like duplicate timestamps and out-of-order inserts.

1. Clarify Requirements

Ask about expected operations (set, get), timestamp uniqueness, ordering guarantees, and performance requirements. Confirm whether timestamps are strictly increasing per key and if multiple values per timestamp are allowed.

2. Choose Data Structures

Propose a hash map for O(1) key lookup, with each key mapping to a list of (timestamp, value) pairs sorted by timestamp. Discuss alternatives like balanced BSTs or skip lists and their trade-offs.

3. Implement Operations

For set: append to the list if timestamp is increasing, else insert in sorted order. For get: binary search for the largest timestamp <= given timestamp and return the corresponding value.

4. Analyze Complexity

State time and space complexity: set O(1) amortized if appending, O(log n) if inserting; get O(log n) due to binary search. Space O(n) for n total entries.

5. Discuss Extensions

Address concurrency (e.g., locking per key), persistence, and scalability (sharding). Mention how to handle out-of-order timestamps and duplicate timestamps.

Key Points to Mention

  • Use of hash map for key lookup and sorted list for timestamps.
  • Binary search for efficient retrieval of the most recent value at or before a timestamp.
  • Time complexity: O(log n) for get, O(1) amortized for set if timestamps are increasing.
  • Handling of out-of-order timestamps and duplicate timestamps.
  • Concurrency considerations: per-key locking or lock-free data structures.
  • Scalability: sharding by key, persistence options, and memory management.

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