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Databricks·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Databricks software engineer interview with a grid BFS problem. Pretty clean problem once you see the multi-source angle, but I definitely overthought the initialization phase for a bit.

Questions Asked (1)

Q1

Given a city grid where cells are either open streets, buildings, or transit stations, find the minimum number of moves from each open street cell to the nearest transit station. Buildings and unreachable cells return -1.

Algorithms & Data Structures
Author's notes

My first instinct was to BFS from every open cell individually and that would've been a disaster at 300x300.

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AI HintsAI Generated

Suggested Approach

Use multi-source BFS starting from all transit stations simultaneously to compute shortest distances to all reachable open streets. Initialize a distance matrix with -1, set transit stations to 0, and enqueue them; then BFS outward, updating distances for unvisited open streets. Buildings and unreachable cells remain -1.

Pro tip: Clarify edge cases upfront (e.g., no transit stations, all buildings) and mention that multi-source BFS is optimal because it processes each cell once, achieving O(R*C) time. Also, discuss memory optimization by using a 2D array for distances and a queue for BFS.

1. Understand the problem and define grid representation

Clarify that the grid contains open streets (0), buildings (1), and transit stations (2). The goal is to return a grid of same dimensions where each open street cell contains the minimum moves to the nearest transit station, and buildings or unreachable cells are -1.

2. Choose multi-source BFS as the algorithm

Explain that multi-source BFS from all transit stations simultaneously computes shortest distances efficiently. This avoids running BFS from each open street, which would be less efficient.

3. Initialize data structures

Create a distance matrix initialized to -1. Enqueue all transit station coordinates and set their distance to 0. Use a queue for BFS.

4. Perform BFS traversal

While the queue is not empty, dequeue a cell, and for each of its four neighbors, if the neighbor is within bounds, is an open street, and has distance -1, set its distance to current distance + 1 and enqueue it.

5. Return the distance matrix

After BFS completes, the distance matrix contains the minimum moves for each open street, with -1 for buildings and unreachable cells. Return this matrix.

Key Points to Mention

  • Multi-source BFS efficiently computes distances from multiple sources in one pass.
  • Time complexity is O(R*C) where R and C are grid dimensions, as each cell is processed at most once.
  • Space complexity is O(R*C) for the distance matrix and queue.
  • Edge cases: no transit stations (all -1), all transit stations (all 0), disconnected open areas.
  • Use of a queue for BFS and a 2D array for distances.
  • Buildings are treated as obstacles and are not enqueued or updated.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.