Character.AI·Software Engineer·Technical Phone Screen·Intermediate

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Apr 2026Remote

Summary

Character.AI system design round, one question but it had a follow-up that caught me flat-footed. The core problem wasn't too bad but the out-of-order variant exposed some gaps in how I think about sorted data structures under mutation.

Questions Asked (1)

Design a class with insert(key, value, timestamp) and get(key, timestamp) operations, where get returns the value with the smallest timestamp greater than or equal to the queried timestamp. Inserts are guaranteed to arrive in increasing timestamp order. Follow-up: how does your design change if inserts can arrive out of order?

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The base case I got through fine.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: get returns the value with the smallest timestamp >= queried timestamp, and inserts are in increasing order. For the in-order case, propose a simple list or array with binary search for get, and O(1) append for insert. For the out-of-order follow-up, discuss balanced BST or skip list to maintain sorted order, enabling O(log n) insert and get.

Pro tip: Mention that in the in-order case, you can use a dynamic array and binary search for get, but if timestamps are not strictly increasing, you need to handle duplicates by keeping the latest value for the same timestamp. Also, consider memory constraints and potential need for persistence.

1. Clarify requirements and assumptions

Confirm that get returns the value with the smallest timestamp >= queried timestamp, and that inserts are in increasing order. Ask about duplicate timestamps, null values, and expected operation frequencies.

2. Design for in-order inserts

Use a dynamic array to store (timestamp, value) pairs. Since inserts are in increasing order, append in O(1). For get, binary search for the first timestamp >= query and return its value, or null if none.

3. Analyze complexity and trade-offs

Insert: O(1) amortized; Get: O(log n). Space: O(n). Discuss alternatives like hash map + sorted list, but note that binary search on array is simplest and efficient.

4. Handle out-of-order inserts

If inserts can be out of order, maintain a balanced binary search tree (e.g., TreeMap in Java, sortedcontainers in Python) keyed by timestamp. Insert: O(log n); Get: O(log n) using ceilingEntry. Alternatively, use a skip list or a segment tree if range queries are needed.

5. Discuss optimizations and edge cases

Consider duplicate timestamps: overwrite value or keep latest. For high read/write ratio, consider caching or indexing. Mention that if timestamps are bounded, a hash map with sorted keys or a time-based index could be used.

Key Points to Mention

Binary search for get in the in-order case, leveraging sorted timestamps.
Dynamic array for O(1) append and O(log n) get.
Balanced BST (e.g., TreeMap) for out-of-order inserts, providing O(log n) insert and get.
Handling duplicate timestamps: decide whether to overwrite or keep multiple values.
Time and space complexity analysis for both scenarios.
Potential need for thread safety or concurrency if used in a multi-threaded environment.

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