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Microsoft·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Microsoft SWE interview with a token management design problem. Pretty classic LeetCode-adjacent stuff but the TTL edge cases tripped me up more than I expected.

Questions Asked (1)

Q1

Design an authentication token manager that supports three operations given a fixed TTL: generating a new token with an expiry at currentTime + ttl, renewing a token only if it hasn't expired yet, and counting all currently unexpired tokens at a given time.

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

I got the basic structure down fast, just a hashmap from tokenId to expiry time.

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AI HintsAI Generated

Suggested Approach

Clarify the requirements and constraints, then propose a data structure that supports efficient token generation, renewal, and counting of unexpired tokens. Discuss trade-offs between different approaches (e.g., hash map with lazy deletion vs. priority queue) and consider concurrency and scalability aspects.

Pro tip: Mention that you would use a min-heap or balanced BST to efficiently remove expired tokens, but also consider lazy deletion to avoid frequent cleanups. Emphasize that the choice depends on the read/write ratio and whether the system is distributed.

1. Clarify requirements and constraints

Ask about expected token volume, read/write ratio, concurrency needs, and whether tokens are stored in memory or persisted. Confirm that TTL is fixed and that time is monotonic.

2. Design data structures for core operations

Propose a hash map to store token metadata (expiry time) for O(1) generation and renewal. For counting unexpired tokens, consider a min-heap or balanced BST keyed by expiry time to efficiently remove expired tokens.

3. Handle expiration and counting

Explain how to maintain a count of unexpired tokens: either eagerly remove expired tokens on each operation or lazily clean up during counting. Discuss the impact on time complexity.

4. Address concurrency and scalability

Discuss thread-safety using locks or concurrent data structures. For distributed systems, consider sharding by token or using a centralized store with TTL support (e.g., Redis).

5. Analyze trade-offs and edge cases

Compare approaches: hash map + min-heap gives O(log n) for cleanup but O(1) for generation/renewal; lazy deletion may cause memory bloat. Handle edge cases like renewing an expired token, clock skew, and token collisions.

Key Points to Mention

  • Use a hash map for O(1) token lookup and renewal, storing expiry timestamps.
  • Maintain a min-heap or balanced BST of expiry times to efficiently remove expired tokens and keep an accurate count.
  • Consider lazy deletion vs. eager cleanup: lazy deletion avoids overhead on every operation but may require periodic cleanup.
  • For concurrency, use read-write locks or concurrent data structures; for distributed systems, consider Redis with TTL or sharding.
  • Discuss time complexity: generation O(1), renewal O(1), counting O(k) where k is number of expired tokens cleaned, or O(1) if maintaining a counter with lazy cleanup.
  • Mention edge cases: renewing an expired token should fail, handling clock skew, and ensuring token uniqueness.

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