Breadth-First Search Questions at TikTok: What to Expect

Breadth-First Search (BFS) is a core algorithm for navigating graphs and grids layer by layer, making it essential for problems involving shortest paths, level-order traversal, or exploring states in discrete steps. At TikTok, with 43 BFS questions out of 383 total, it's a heavily tested concept. This frequency reflects the nature of problems at scale: designing features that involve shortest connection paths between users, analyzing network structures, processing hierarchical data (like comments or organizational charts), or solving grid-based puzzles in their engineering systems. Mastering BFS is non-negotiable for roles dealing with distributed systems, infrastructure, or backend services where efficient traversal is key.

What to Expect — Types of Problems

TikTok's BFS problems typically fall into three categories:

1. Shortest Path in Unweighted Graphs/Grids: The most direct application. You'll be given a grid (like a maze or map) or an adjacency list representing a network, and you must find the minimum steps or distance between two points. Obstacles, weighted cells, or multiple start/end points are common twists.
2. Level-Order Traversal & Hierarchical Processing: This involves traversing trees or graphs level by level. Problems may include zigzag traversal, connecting level-order siblings, or finding the largest value on each level—useful for UI rendering or data aggregation pipelines.
3. State-Space Search (BFS on Implicit Graphs): Here, the "graph" isn't given explicitly. Each state (e.g., a configuration of a puzzle, a combination of locks, or a transformed string) is a node, and edges are valid transitions. BFS finds the minimum moves to reach a target state, common in feature logic or optimization challenges.

How to Prepare — Study Tips with One Code Example

Focus on the standard BFS template using a queue. Memorize the pattern for grids (directions array) and graphs (adjacency list). Always track visited states to avoid cycles. Practice converting problem descriptions into a graph model—this is the hardest part.

A key pattern is BFS for shortest path in a binary matrix. Here’s the template in three languages:

from collections import deque

def shortestPathBinaryMatrix(grid):
    if not grid or grid[0][0] == 1:
        return -1

    n = len(grid)
    directions = [(-1,-1),(-1,0),(-1,1),(0,-1),(0,1),(1,-1),(1,0),(1,1)]
    queue = deque([(0, 0, 1)])  # (row, col, distance)
    grid[0][0] = 1  # mark visited

    while queue:
        r, c, dist = queue.popleft()
        if r == n-1 and c == n-1:
            return dist
        for dr, dc in directions:
            nr, nc = r + dr, c + dc
            if 0 <= nr < n and 0 <= nc < n and grid[nr][nc] == 0:
                queue.append((nr, nc, dist + 1))
                grid[nr][nc] = 1  # mark visited
    return -1

Recommended Practice Order

Start with fundamental grid and tree traversal to internalize the queue mechanics. Then progress to more complex, TikTok-relevant scenarios.

1. Basics: Binary Tree Level Order Traversal, Number of Islands.
2. Core Shortest Path: Shortest Path in Binary Matrix, Rotting Oranges.
3. State Search: Word Ladder, Open the Lock.
4. Advanced Variations: Shortest Path with Obstacles, Multi-source BFS (like time to infect all nodes), BFS with Bitmasking for visited states.

Practice Breadth-First Search at TikTok