← Perplexity Interview Insights

Perplexity·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
May 2026Remote

Summary

Perplexity SWE interview that went deep on a timestamped key-value store design, specifically around adding a restore operation and hashing out the semantics and tradeoffs live with the interviewer. More open-ended than I expected, less about coding and more about thinking through data structure choices under pressure.

Questions Asked (1)

Q1

You have a timestamped key-value store with set, delete, and get operations. Design a restore(timestamp) operation that rolls the entire store back to its state at the given time. Discuss the semantics, data structure changes, and time/space complexity tradeoffs.

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

This one took me a minute to even figure out what they were actually asking.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the semantics of restore: whether it's a destructive rollback or a non-destructive time-travel query, and how it interacts with subsequent writes. Then propose a versioned data structure (e.g., persistent balanced BST or versioned hash map) that stores all historical states, and analyze the time/space tradeoffs of different approaches.

Pro tip: Mention that restore can be implemented as a non-destructive operation by keeping a version chain and returning a view at the timestamp, which avoids expensive copying and supports concurrent reads. Also discuss the tradeoff between eager (copy-on-write) and lazy (log-based) versioning.

1. Clarify semantics and requirements

Ask whether restore is destructive (modifies current state) or non-destructive (returns a snapshot), and whether timestamps are unique and monotonic. Also consider concurrency and persistence requirements.

2. Choose a versioning strategy

Decide between maintaining full snapshots at each timestamp (simple but space-heavy) or using a versioned data structure (e.g., persistent tree, versioned hash map) that shares structure across versions.

3. Design the data structure

For a persistent balanced BST (e.g., treap, red-black tree), each set/delete creates a new version with O(log n) new nodes. For a versioned hash map, use a global version counter and store (key, value, version) entries, with per-key version lists.

4. Implement restore(timestamp)

For persistent trees, store a mapping from timestamp to root node; restore is O(1) lookup. For versioned hash maps, restore requires iterating over all keys to find the latest version ≤ timestamp, which is O(n) or O(k log v) with per-key binary search.

5. Analyze complexity and tradeoffs

Compare time/space: persistent trees give O(log n) set/delete/get and O(1) restore, but higher constant factors; versioned hash maps give O(1) set/get, O(n) restore, and O(total versions) space. Discuss garbage collection of old versions if needed.

Key Points to Mention

  • Semantics: destructive vs. non-destructive restore, and how it affects subsequent operations.
  • Persistent data structures (e.g., persistent balanced BST, copy-on-write) for efficient versioning.
  • Versioned hash map with per-key version lists and binary search for point-in-time queries.
  • Time complexity: set/delete/get O(log n) or O(1), restore O(1) or O(n) depending on approach.
  • Space complexity: O(n log n) for persistent trees, O(total versions) for versioned maps.
  • Tradeoffs: simplicity vs. performance, memory overhead, and concurrency considerations.

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