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Snapchat·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jul 2026

Summary

Snapchat SWE interview with a data structures and algorithms question that looked deceptively simple on the surface but had a lot of layers once you got into the query optimization part.

Questions Asked (1)

Q1

Given an unsorted list of timestamp events (with possible duplicates), implement a query(start, end) function that returns the count of events falling within the inclusive time range [start, end].

Algorithms & Data Structures
Author's notes

My first instinct was to just sort the list and do two binary searches, which gets you most of the way there.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: are events static or dynamic? If static, sort the timestamps and use binary search (bisect) to find the count in O(log n) per query. If dynamic, consider a balanced BST or segment tree to support insertions and range queries efficiently.

Pro tip: Mention that sorting once and using binary search is optimal for static data, but if updates are frequent, a Fenwick tree or segment tree with coordinate compression is better. Also, handle duplicates by counting them in the range.

1. Clarify requirements

Ask whether the list is static or dynamic, and whether queries are frequent. This determines the data structure choice.

2. Choose data structure

For static data, sort the timestamps and use binary search. For dynamic data, use a balanced BST or Fenwick tree with coordinate compression.

3. Handle duplicates

Ensure the counting method includes all duplicates within the range, e.g., using bisect_left and bisect_right.

4. Implement query

For binary search: find the first index >= start and the first index > end, then return the difference. For tree-based: traverse and count nodes in range.

5. Analyze complexity

State time and space complexity: O(n log n) preprocessing, O(log n) per query for static; O(log n) per update/query for dynamic.

Key Points to Mention

  • Binary search on sorted array for static data
  • Use of bisect_left and bisect_right to handle inclusive range and duplicates
  • Time complexity: O(n log n) preprocessing, O(log n) per query
  • Alternative: Fenwick tree or segment tree for dynamic updates
  • Coordinate compression if timestamps are large or sparse
  • Edge cases: empty list, start > end, no events in range

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