Depth-First Search Questions at TikTok: What to Expect

Depth-First Search (DFS) is a fundamental algorithm for exploring graphs and trees, and its prevalence at TikTok is a direct reflection of the company's technical challenges. TikTok's platform is built on interconnected data: social graphs (followers, friends), recommendation systems traversing content networks, and UI view hierarchies. DFS provides the systematic, recursive approach needed to navigate these structures, whether it's finding paths, detecting cycles, or exhaustively searching state spaces. With 44 specific DFS questions in their known problem set, mastering this algorithm is non-negotiable for passing their technical interviews.

What to Expect — Types of Problems

TikTok's DFS questions typically fall into three categories. First, classic tree/graph traversal. You'll be asked to search or modify binary trees, N-ary trees, or adjacency lists. Expect variations like finding a node, calculating depth, or performing a specific order traversal.

Second, pathfinding and state search. These problems involve finding a specific path between nodes, checking for valid sequences, or exploring all possible states in a game-like scenario (e.g., generating parentheses). They often test your ability to apply DFS to implicit graphs.

Third, connected components and area calculation. Common in matrix-based problems, you'll use DFS to explore contiguous regions—think "number of islands," "flood fill," or "max area of an island." These questions assess how you handle grid boundaries and visited state management.

How to Prepare — Study Tips with One Code Example

Start by internalizing the core recursive pattern. DFS is either implemented recursively (using the call stack) or iteratively (using an explicit stack). For interviews, the recursive approach is often clearer for tree problems, while the iterative approach can be safer for deep graphs to avoid stack overflow. Always clarify the graph's nature (cyclic/acyclic) to decide on visited tracking.

Practice writing the skeleton quickly. The key is to cleanly handle the base case (often a null node or an invalid cell), mark the current node as visited, perform the necessary operation (like adding to a path list), and then recursively explore all neighbors or children. Backtracking is a critical related concept; you must undo changes to shared state after a recursive call returns when exploring multiple paths.

Here is the essential pattern for a recursive DFS on a binary tree node, performing a pre-order traversal:

def dfs(node):
    if not node:
        return
    # Process current node (e.g., print value, add to list)
    print(node.val)
    # Recursively explore children
    dfs(node.left)
    dfs(node.right)

For grid (matrix) DFS, the pattern expands to four directions and includes a visited set or modifies the grid in-place to mark cells.

Recommended Practice Order

Build competence progressively. First, master basic tree traversals (pre-order, in-order, post-order) and simple recursive calculations (max depth, path sum). Second, tackle classic LeetCode Easy DFS problems like "Maximum Depth of Binary Tree" and "Flood Fill" to build confidence with the pattern.

Third, move to medium-difficulty path and state problems, such as "Path Sum II" (backtracking) and "Number of Islands." Finally, attempt TikTok's most frequent tagged DFS questions on platforms like LeetCode. Time yourself. In interviews, you must articulate your reasoning, explain time/space complexity (typically O(N) nodes visited), and write bug-free code quickly.

Practice Depth-First Search at TikTok