Breadth-First Search Questions at Databricks: What to Expect

Breadth-First Search (BFS) is a core algorithmic technique for exploring graphs and trees level by level. At Databricks, a company built on processing large-scale, interconnected data, this pattern is directly relevant. Many of their systems involve navigating dependencies, analyzing network structures, or finding shortest paths in state spaces—all classic BFS domains. With 3 out of their 31 tagged interview questions involving BFS, it's a pattern you must have ready. Mastery demonstrates you can think systematically about data relationships and optimal traversal, a valuable skill for working with distributed data graphs and pipeline orchestration.

What to Expect — Types of Problems

Databricks BFS questions typically model real-world data navigation. Expect problems in these categories:

1. Shortest Path in an Unweighted Graph: The most direct application. Given a start and target (e.g., a database table, a processing node), find the minimum number of steps or hops to reach it. Problems may involve transforming one state to another or navigating a social/user graph.
2. Level-Order Traversal & Analysis: Processing a tree or graph in layers. This could be calculating something per level, finding the minimum depth of a tree, or identifying nodes at a specific distance—useful for hierarchical data or dependency resolution.
3. Matrix Traversal: Treating a grid as a graph where each cell is a node connected to its neighbors. BFS efficiently finds the shortest path to a target cell or propagates a condition (like a signal or access rights) through adjacent cells. This models data spreading across a network or cluster.

The constraints will often be large, requiring an efficient O(N) or O(M*N) solution. You must avoid depth-first recursion pitfalls where shortest path isn't guaranteed.

How to Prepare — Study Tips with One Code Example

Internalize the BFS template: use a queue, track visited nodes, and process nodes level by level. Practice writing it from memory. Focus on identifying the "node" and "neighbors" in any problem context—they might be strings, tuples, or custom objects.

A key pattern is BFS for shortest path in an unweighted graph. Here is the standard implementation:

from collections import deque

def bfs_shortest_path(graph, start, target):
    if start == target:
        return 0
    queue = deque([start])
    visited = {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  # Target not reachable

Recommended Practice Order

1. Start with fundamental tree level-order traversal.
2. Move to classic grid shortest-path problems (e.g., Knight moves, shortest path in binary matrix).
3. Practice word-ladder style problems, which combine graph construction with BFS.
4. Finally, tackle Databricks-specific questions to familiarize yourself with their problem framing and common constraints.

Practice Breadth-First Search at Databricks