Depth-First Search Questions at Snapchat: What to Expect

Depth-First Search (DFS) is a fundamental algorithm for exploring graphs and trees, and its prevalence at Snapchat is no accident. The company's core products—like Snap Map, Stories, and Lenses—rely heavily on graph-like data structures. Whether it's traversing a social graph to recommend friends, exploring a spatial map for location-based features, or processing nested visual effect trees, DFS provides the recursive, exploratory logic needed to navigate these interconnected systems efficiently. Mastering DFS is not just about passing an interview; it's about demonstrating you can think through the non-linear data traversal problems central to Snapchat's engineering challenges.

What to Expect — Types of Problems

Snapchat's DFS questions typically fall into three categories, moving from straightforward to complex.

1. Classic Tree/Graph Traversal: These are foundational. You might be asked to perform a pre-order, in-order, or post-order traversal on a tree, or to simply traverse all nodes in a graph. The goal is to confirm you understand the recursion/stack mechanics and can avoid cycles in graphs.
2. Pathfinding & Connectivity: This is where most Snapchat questions land. Problems involve finding if a path exists between two points (e.g., "can user A reach user B's story?"), counting connected components in a grid (analogous to image segmentation for Lenses), or finding the longest path in a DAG. These test your ability to augment basic DFS with state tracking.
3. Complex State & Backtracking: The most challenging tier. Here, DFS is used to explore a state space, such as generating all possible combinations of filters or solving a puzzle. You'll need to manage visited states, prune invalid paths (backtracking), and potentially combine DFS with other techniques like memoization.

How to Prepare — Study Tips with One Code Example

Focus on understanding the pattern, not memorizing problems. The core pattern is: 1) Handle the base case, 2) Mark the current node as visited, 3) Recurse on all adjacent, unvisited nodes. Practice implementing it both recursively and iteratively using a stack.

A quintessential problem is Number of Islands (grid connectivity), which directly mirrors image region analysis. Given a 2D grid of '1's (land) and '0's (water), count the number of islands.

def numIslands(grid):
    if not grid:
        return 0

    def dfs(r, c):
        if r < 0 or c < 0 or r >= rows or c >= cols or grid[r][c] != '1':
            return
        grid[r][c] = '#'  # Mark as visited
        # Explore all 4 directions
        dfs(r+1, c)
        dfs(r-1, c)
        dfs(r, c+1)
        dfs(r, c-1)

    rows, cols = len(grid), len(grid[0])
    count = 0
    for r in range(rows):
        for c in range(cols):
            if grid[r][c] == '1':
                dfs(r, c)
                count += 1
    return count

Recommended Practice Order

Build competence progressively. Start with basic tree traversals (Binary Tree Inorder Traversal). Move to standard graph traversal (Clone Graph). Then, practice the core grid/connectivity pattern (Number of Islands, Max Area of Island). Advance to pathfinding problems (All Paths From Source to Target). Finally, tackle backtracking (Subsets, Word Search). Always analyze time/space complexity.

Practice Depth-First Search at Snapchat