Breadth-First Search Questions at Snapchat: What to Expect

Breadth-First Search (BFS) is a critical algorithm for Snapchat interviews because the company's core products—Snap Map, Stories discovery, friend networks, and content distribution—are built on graph-like structures. Efficiently traversing social graphs, calculating degrees of separation, finding shortest paths in geospatial data, or discovering connected content are daily engineering challenges. Mastering BFS demonstrates you can reason about scalable, level-order traversal, which is essential for features that must process relationships and proximity in real-time for millions of users.

What to Expect — Types of Problems

Snapchat's BFS problems typically fall into three categories:

1. Shortest Path in Unweighted Graphs: Finding the minimum steps between users, the shortest transformation sequence, or the nearest location. These are pure BFS applications.
2. Level-Order Traversal in Trees/Matrices: Processing data layer by layer, such as calculating time to propagate a message across a network or rotting adjacent oranges in a grid.
3. Connected Components & Graph Search: Identifying isolated user groups, checking if a friendship network is connected, or exploring all reachable states in a puzzle.

Expect the problems to be framed around Snapchat's domain: think "minimum snaps to send a story to a friend," "degrees of separation in the Snap Map," or "finding all mutual friends within two hops."

How to Prepare — Study Tips with One Code Example

Focus on the core BFS template using a queue. Practice writing it from memory. Key variations include tracking visited nodes, counting levels, and handling multi-source BFS (starting from multiple points). Always clarify if the graph is implicit (like a grid) or explicit (adjacency list).

A fundamental pattern is finding the shortest path in an unweighted graph. Here’s the template in three languages:

from collections import deque

def bfs_shortest_path(graph, start, target):
    if start == target:
        return 0
    queue = deque([start])
    visited = set([start])
    distance = {start: 0}

    while queue:
        node = queue.popleft()
        for neighbor in graph[node]:
            if neighbor not in visited:
                visited.add(neighbor)
                distance[neighbor] = distance[node] + 1
                if neighbor == target:
                    return distance[neighbor]
                queue.append(neighbor)
    return -1  # No path found

Recommended Practice Order

1. Start with classic BFS on trees (level order traversal) and simple grids (number of islands, rotting oranges).
2. Move to shortest path problems in explicit graphs (word ladder, find if path exists).
3. Tackle multi-source BFS problems (walls and gates, time to infect all nodes).
4. Finally, solve Snapchat’s tagged problems, which often combine BFS with slight twists like tracking paths or conditional moves.

Practice Breadth-First Search at Snapchat