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Microsoft·Software Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediatePrefer not to say
Jun 2026

Summary

Microsoft SWE coding round, one question the whole time. They gave me a custom set data structure to implement and the snapshot iterator behavior is what made it interesting. Felt like a fair problem but I definitely underestimated how much the iterator semantics would trip me up.

Questions Asked (1)

Q1

Design and implement a SnapshotSet data structure that supports add, remove, contains, and an iterator that captures a snapshot of the set at the moment iterator() is called, so subsequent mutations don't affect ongoing iteration.

Algorithms & Data StructuresTechnical Trade-offsSystem Design
Author's notes

The add/remove/contains part is basically just a HashSet wrapper, so I got through that fast and felt good.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints (e.g., thread safety, memory vs. time trade-offs). Then present a versioned approach: maintain a global version counter and store each element with the version it was added/removed, so an iterator captures the current version and filters elements accordingly. Discuss alternative designs like copy-on-write or persistent data structures, and analyze their trade-offs.

Pro tip: Mention that you would use a concurrent hash map with versioned entries to avoid locking the entire set during iteration, and that you'd consider lazy deletion to keep memory overhead low. This shows you think about real-world performance and concurrency, which is crucial at Microsoft.

1. Clarify requirements and constraints

Ask about thread safety, expected size, frequency of mutations vs. iterations, and memory constraints. This ensures your design aligns with the actual use case.

2. Propose a versioned approach

Explain that each element will have a version number indicating when it was added or removed. The iterator captures the current global version and only returns elements that were present at that version.

3. Detail the data structures

Use a concurrent hash map mapping element to a list of version intervals (or a single version if only add/remove once). Maintain a global atomic version counter. For iteration, traverse the map and filter based on the snapshot version.

4. Discuss trade-offs and alternatives

Compare with copy-on-write (simple but memory-heavy) and persistent data structures (efficient but complex). Highlight that the versioned approach balances memory and performance, especially for read-heavy workloads.

5. Address concurrency and edge cases

Explain how to handle concurrent mutations during iteration (e.g., using atomic operations or fine-grained locks). Mention edge cases like removing an element that was added after the snapshot, and how versioning handles it.

Key Points to Mention

  • Versioning: each element stores the version at which it was added and/or removed.
  • Global version counter: incremented on each mutation to provide a consistent snapshot point.
  • Iterator captures the current version and filters elements based on their version intervals.
  • Thread safety: use concurrent data structures or locks to ensure atomicity of version updates and reads.
  • Memory management: lazy deletion or periodic cleanup of old versions to avoid unbounded growth.
  • Trade-offs: compare with copy-on-write and persistent data structures in terms of time, space, and complexity.

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