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Anthropic·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Apr 2026Remote

Summary

Anthropic coding round focused on extending a key-value store with backup and restore functionality, including some tricky TTL pausing logic that took me a while to think through clearly.

Questions Asked (1)

Q1

You have an in-memory key-value store. Extend it to support backup(timestamp) and restore(at_timestamp), where backup pauses TTL countdowns and restore resumes remaining TTL for each key from the most recent snapshot at or before the given timestamp.

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

The core data structure part was fine but the TTL semantics tripped me up.

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AI HintsAI Generated

Suggested Approach

Clarify the semantics of backup and restore, especially how TTLs are paused and resumed, and whether multiple snapshots can exist. Then design a data structure that stores snapshots with timestamps and supports efficient retrieval of the most recent snapshot at or before a given time, while handling TTL adjustments correctly.

Pro tip: Discuss the trade-off between memory usage and restore speed: storing full snapshots is simple but memory-heavy, while storing deltas or using copy-on-write saves memory but complicates restore. Also, consider concurrency and atomicity of backup/restore operations.

1. Clarify requirements and semantics

Ask questions to pin down: Can backup be called multiple times? Does restore delete later snapshots? How are TTLs represented (absolute expiry vs remaining)? What happens to keys modified after backup? This ensures you solve the right problem.

2. Design snapshot storage

Choose a data structure to store snapshots keyed by timestamp, e.g., a sorted list or balanced BST. Each snapshot should capture the state of all keys and their remaining TTLs at backup time, with TTLs paused.

3. Implement backup(timestamp)

On backup, pause all TTL countdowns (e.g., by converting absolute expiry to remaining TTL) and store a deep copy or persistent snapshot of the store at the given timestamp. Ensure the operation is atomic.

4. Implement restore(at_timestamp)

Find the most recent snapshot with timestamp <= at_timestamp. Replace the current store state with that snapshot, resuming TTLs by converting remaining TTL back to absolute expiry based on the current time.

5. Analyze trade-offs and edge cases

Discuss time/space complexity, memory overhead of snapshots, handling of keys added/deleted after backup, and concurrency. Mention possible optimizations like copy-on-write or delta encoding.

Key Points to Mention

  • TTL representation: store remaining TTL in snapshots to pause countdown, and convert to absolute expiry on restore.
  • Snapshot storage: use a sorted structure (e.g., TreeMap) to efficiently find the latest snapshot <= given timestamp.
  • Atomicity and concurrency: ensure backup and restore are atomic, possibly using locks or versioning.
  • Memory vs. performance trade-off: full snapshots are simple but memory-heavy; deltas or copy-on-write reduce memory but add complexity.
  • Edge cases: restoring to a timestamp before any snapshot, multiple restores, and keys modified after backup.
  • Complexity analysis: backup O(n) for full snapshot, restore O(n) to replace state; with deltas, could be O(k) where k is changes.

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