Breadth-First Search Questions at Pinterest: What to Expect

Breadth-First Search (BFS) is a core algorithm for traversing graphs and trees level-by-level, making it essential for problems involving shortest paths, social networks, and hierarchical data. At Pinterest, with its vast network of user connections, content boards, and recommendation systems, BFS is a practical tool for modeling relationships and distances. The platform's engineering challenges—like finding the shortest connection path between users, discovering related pins within a certain "distance," or analyzing board hierarchies—often map directly to BFS problems. This explains why roughly 15% of Pinterest's technical interview questions (7 out of 48) test this algorithm. Mastering BFS demonstrates you can handle real-world scenarios involving layered exploration and optimal reachability.

What to Expect — Types of Problems

Pinterest's BFS questions typically fall into two categories, both emphasizing practical application over theoretical trickery.

Shortest Path in Unweighted Graphs: This is the most common pattern. You'll be given a scenario—like navigating between user profiles, pins, or boards—where each "step" has equal cost. The task is to find the minimum number of steps between two nodes. Problems may involve grids (e.g., finding the shortest path for a content moderator to reach a flagged item) or adjacency lists representing social connections.

Level-Order Traversal and Analysis: Here, BFS is used to process data in tiers. This could mean collecting all nodes at each depth of a tree (like a board's sub-category structure), calculating the minimum depth of a tree, or finding all nodes within K steps from a starting point (modeling content recommendation radius). These questions test your ability to track levels during traversal, often requiring slight modifications to the standard BFS template.

Expect the problems to be framed within a plausible Pinterest context, but the core will be a standard graph or tree traversal.

How to Prepare — Study Tips with One Code Example

Internalize the standard BFS template using a queue. Your mental checklist should be: 1) Initialize queue and visited set, 2) While queue is not empty, 3) Process nodes level by level (often using a loop over the queue's current size). Practice writing this from memory.

The most critical pattern is BFS for shortest path in an unweighted graph. Below is the template for finding the minimum steps from a start node to a target node, given an adjacency list. Note the use of a level counter and the loop to process each level.

from collections import deque

def bfs_shortest_path(adj_list, start, target):
    if start == target:
        return 0
    queue = deque([start])
    visited = set([start])
    steps = 0

    while queue:
        # Process all nodes at the current level
        for _ in range(len(queue)):
            node = queue.popleft()
            if node == target:
                return steps
            for neighbor in adj_list[node]:
                if neighbor not in visited:
                    visited.add(neighbor)
                    queue.append(neighbor)
        steps += 1
    return -1  # Target not reachable

Recommended Practice Order

Build proficiency systematically. Start with basic tree traversals (Binary Tree Level Order Traversal). Move to simple graph shortest path problems (Word Ladder is a classic). Then practice multi-source BFS (rotting oranges, walls and gates) which is useful for problems starting from multiple points. Finally, tackle BFS on implicit graphs (like 2D grids for maze navigation). For each problem, first solve it, then adapt the scenario to a Pinterest-like context—for example, imagine the grid represents a board layout or the words represent different pin categories.

Practice Breadth-First Search at Pinterest