Snapchat·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Snapchat SWE interview with a data structures design question that had more depth to it than I expected. The trade-off discussion is where they really wanted to see your thinking.

Questions Asked (1)

Design a data structure that supports two operations: one that records a strictly increasing integer timestamp, and one that returns how many recorded timestamps are strictly greater than a given value. Then discuss the trade-offs between different designs that optimize for different access patterns.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

I went straight for the obvious approach, store timestamps in a list, binary search on query, O(1) write and O(log n) read.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: timestamps are strictly increasing, so they are naturally sorted. Then propose a simple array or list with binary search for O(log n) queries, and discuss alternatives like balanced BSTs or Fenwick trees for different trade-offs. Finally, analyze time and space complexity for each design and recommend based on expected access patterns.

Pro tip: Mention that since timestamps are strictly increasing, the data is inherently sorted, so binary search is optimal for queries without extra structure. Also, consider that in a real system like Snapchat, timestamps might be generated at high volume, so insertion efficiency matters.

1. Clarify requirements and constraints

Ask about the expected number of operations, memory constraints, and whether timestamps are unique and strictly increasing (given). Confirm that queries are for strictly greater values.

2. Propose a baseline solution

Suggest storing timestamps in a dynamic array (list) and using binary search to find the first timestamp greater than the query value. Insertion is O(1) amortized (append), query is O(log n).

3. Discuss alternative data structures

Consider balanced BSTs (e.g., Red-Black tree) for O(log n) insertion and query, or Fenwick tree (BIT) with coordinate compression for O(log n) both, but note overhead. Also mention skip lists or B-trees for disk-based scenarios.

4. Analyze trade-offs

Compare time and space complexity: array is simple, cache-friendly, but insertion at end is O(1) amortized; BSTs offer O(log n) insertion but higher constant factors; Fenwick tree requires coordinate compression and is less intuitive.

5. Recommend based on access patterns

If queries dominate, array with binary search is best. If insertions and queries are mixed, balanced BST or Fenwick tree may be better. Emphasize that strictly increasing timestamps simplify the problem.

Key Points to Mention

Timestamps are strictly increasing, so they are already sorted; binary search is applicable.
Array with binary search: O(1) amortized insertion, O(log n) query, O(n) space.
Balanced BST: O(log n) insertion and query, but higher memory overhead and constant factors.
Fenwick tree (BIT) with coordinate compression: O(log n) for both, but requires pre-processing and is less flexible for dynamic insertions.
Trade-offs: simplicity vs. performance, memory vs. speed, and suitability for different access patterns (read-heavy vs. write-heavy).
Consider real-world constraints: high insertion rate might favor array append; frequent queries might favor binary search.

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