Snapchat·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Snapchat SWE interview with a grid-based coding problem that went deeper than I expected. They weren't satisfied with just a working solution, they pushed hard on complexity, recursion limits, and alternative approaches.

Questions Asked (1)

Given an m×n grid of '1's (land) and '0's (water), count the number of islands where cells are connected in all 8 directions. Implement a solution using BFS or DFS, analyze the time and space complexity, and explain how you'd handle very large grids that might hit recursion limits.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went with DFS first because it felt more natural to write quickly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then present a BFS or DFS solution that treats the grid as a graph and explores all 8 directions from each unvisited land cell. After coding, analyze time and space complexity, and discuss how to avoid recursion limits by using an iterative BFS or an explicit stack for DFS.

Pro tip: Mention that you can mutate the input grid to mark visited cells (e.g., set '1' to '0') to save space, but note the trade-off of altering input data. Also, for very large grids, an iterative BFS with a queue is often safer and more memory-efficient than recursive DFS.

1. Clarify and Define

Confirm the problem details: 8-directional connectivity, grid dimensions, and whether the input can be modified. Discuss edge cases like empty grid, all water, or all land.

2. Choose Traversal Method

Decide between BFS and DFS. Explain that BFS uses a queue and avoids recursion limits, while DFS is simpler but may cause stack overflow on large grids.

3. Implement the Algorithm

Iterate through each cell; when a '1' is found, increment island count and traverse all connected '1's using BFS/DFS, marking visited cells (e.g., set to '0').

4. Analyze Complexity

State time complexity O(m*n) since each cell is visited once, and space complexity O(min(m,n)) for BFS queue or O(m*n) for DFS recursion stack in worst case.

5. Address Large Grids

For very large grids, use iterative BFS with a queue to avoid recursion limits. Alternatively, use an explicit stack for DFS. Discuss memory optimizations like in-place marking.

Key Points to Mention

8-directional connectivity: check all 8 neighbors (horizontal, vertical, diagonal).
Time complexity: O(m*n) because each cell is processed once.
Space complexity: O(min(m,n)) for BFS queue in worst case (e.g., grid of all land), or O(m*n) for DFS recursion stack.
Recursion limit: recursive DFS may cause stack overflow on large grids; iterative BFS or explicit stack DFS avoids this.
In-place marking: modify grid to '0' to avoid extra visited matrix, but note it mutates input.
Edge cases: empty grid, single row/column, all water, all land.

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