Breadth-First Search Questions at Lyft: What to Expect

Breadth-First Search (BFS) is a core algorithm for navigating graphs and trees level by level. At Lyft, where engineering problems often model real-world networks—like mapping city streets, coordinating rides, or managing service dependencies—BFS is a practical and frequently assessed tool. Out of approximately 25 common coding questions at Lyft, 5 involve BFS. This 20% representation signals that interviewers value your ability to reason about shortest paths, level-order traversal, and state-space exploration efficiently. Mastering BFS demonstrates you can handle the spatial and relational logic central to Lyft's platform.

What to Expect — types of problems

Lyft’s BFS questions typically fall into two categories. First, shortest path problems in grid or graph-based maps. You might be given a 2D grid representing a city map with obstacles, free cells, and special points (like a driver's location and a passenger's destination). The task is to find the shortest number of steps or the minimum distance between points. Variations include multiple points of interest or moving obstacles.

Second, level-order traversal in trees or hierarchical data structures. While trees are a subset of graphs, Lyft often uses them to model hierarchical relationships, like organizational structures or nested geographical zones. Problems may ask you to collect nodes per level, find the minimum depth of a tree, or perform operations that require processing siblings before moving deeper.

Expect constraints that make BFS the optimal choice: unweighted edges (where each "step" has equal cost) and the need for the shortest path in terms of number of edges. You'll need to avoid common pitfalls like infinite loops in graphs by tracking visited nodes.

How to Prepare — study tips with one code example

Focus on the BFS template pattern. It uses a queue to explore nodes level by level and a visited set to prevent reprocessing. Practice implementing it from memory in your language of choice. Key variations to drill: handling 2D grid directions (up, down, left, right), dequeuing nodes in batches to track levels explicitly, and integrating distance tracking within the queue or a separate structure.

Here is the core BFS pattern for finding the shortest path in an unweighted graph, applied to a simple "find if a path exists" problem. This pattern adapts directly to grids and adjacency lists.

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

Recommended Practice Order

Start with fundamental BFS on binary trees (level-order traversal) to internalize the queue-based level processing. Move to shortest path on simple adjacency-list graphs. Then tackle 2D grid problems with obstacles, practicing directional movement arrays. Next, combine BFS with additional constraints, like multi-source BFS (starting from multiple points) or BFS with state tracking (e.g., carrying a key through a grid). Finally, simulate Lyft's interview environment by timing yourself on problems that model ride-matching or geographic navigation.

Practice Breadth-First Search at Lyft