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Databricks·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Databricks SWE interview with a BFS grid problem. Pretty standard algorithmic round but there was a specific optimization they were pushing for.

Questions Asked (1)

Q1

Given a 2D grid, use BFS to find the shortest path (by time or cost) from a source to a destination. Follow-up: optimize the traversal so you only scan the grid once instead of making a separate pass for each direction.

Algorithms & Data Structures
Author's notes

I got the basic BFS working fine but the optimization tripped me up a bit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (grid size, movement directions, uniform vs. weighted costs) and then outline a standard BFS approach using a queue to explore level by level, tracking visited cells and distances. For the follow-up, explain how to optimize by using a single BFS pass that processes all directions simultaneously, avoiding redundant scans.

Pro tip: Mention that BFS guarantees the shortest path only for unweighted graphs; if costs vary, you'd need Dijkstra or 0-1 BFS. Also, emphasize that the single-pass optimization is essentially a multi-source BFS or a bidirectional BFS, which can reduce time complexity.

1. Clarify the problem

Ask about grid dimensions, allowed movements (4-directional or 8-directional), whether costs are uniform, and if obstacles exist. Confirm that the goal is to find the shortest path by time or cost.

2. Outline standard BFS

Describe using a queue to explore cells level by level, marking visited cells and storing distances. Explain that BFS finds the shortest path in an unweighted grid.

3. Address the follow-up

Explain that the standard BFS already scans the grid once; the 'separate pass for each direction' likely refers to a naive approach that runs BFS separately for each direction. Instead, use a single BFS that considers all directions at each step.

4. Discuss optimizations

Mention bidirectional BFS to reduce search space, or A* with a heuristic if costs vary. For uniform costs, a single BFS is optimal; for weighted grids, use Dijkstra or 0-1 BFS.

5. Analyze complexity

State time and space complexity: O(R*C) for BFS on a grid with R rows and C columns, as each cell is visited once. For bidirectional BFS, it can be faster in practice.

Key Points to Mention

  • BFS guarantees shortest path in unweighted graphs; for weighted, use Dijkstra or 0-1 BFS.
  • Use a queue and a visited set to avoid revisiting cells.
  • Single-pass BFS processes all directions simultaneously, avoiding redundant scans.
  • Bidirectional BFS can reduce time and space by searching from both source and destination.
  • Time complexity: O(R*C) for standard BFS; space complexity: O(R*C) for the queue and visited set.
  • Handle edge cases: source equals destination, unreachable destination, obstacles.

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