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Uber·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
May 2026

Summary

Uber SWE interview that threw a spatial data structures problem at me, specifically building a point quadtree from scratch with range queries. Not your typical LeetCode grind session, this one required actually understanding how the data structure works under the hood, including some tricky edge cases around coincident points and boundary semantics.

Questions Asked (1)

Q1

Design and implement a point quadtree for storing 2D geographic coordinates that supports efficient rectangular range queries, including insert and query operations with correct handling of edge cases like duplicate coordinates and boundary points.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

I knew what a quadtree was in theory but had never actually coded one.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then explain the quadtree structure and its operations. Walk through the insert and query algorithms, emphasizing edge cases like duplicate coordinates and boundary points. Finally, discuss trade-offs and potential optimizations.

Pro tip: Mention that you would handle duplicate coordinates by storing a list of points per node or by using a counter, and explicitly define boundary inclusion rules (e.g., points exactly on the query rectangle's edge are included) to avoid ambiguity.

1. Clarify Requirements and Constraints

Ask about expected data volume, query patterns, coordinate precision, and whether duplicates are allowed. Confirm that the quadtree should support dynamic inserts and rectangular range queries.

2. Design the Quadtree Structure

Define the node structure: each node represents a rectangular region and stores points (or a list for duplicates) and four children (NW, NE, SW, SE). Explain splitting logic when capacity is exceeded.

3. Implement Insert Operation

Describe the recursive insert: if node is a leaf and has capacity, add point; if full, split and redistribute points, then insert into appropriate child. Handle duplicates by storing multiple points or incrementing a count.

4. Implement Range Query Operation

Explain recursive query: if node's region does not intersect the query rectangle, return; if leaf, check each point for inclusion; otherwise, recurse into children that intersect. Define boundary inclusion (e.g., inclusive on all edges).

5. Analyze Edge Cases and Trade-offs

Discuss handling of duplicate coordinates, points on boundaries, and degenerate cases (e.g., all points collinear). Compare quadtree with alternatives like k-d tree or R-tree, and mention balancing and performance considerations.

Key Points to Mention

  • Node structure with region boundaries and point storage (list or count for duplicates)
  • Splitting strategy when node exceeds capacity (e.g., bucket size)
  • Recursive insert and query algorithms with pruning based on region intersection
  • Boundary inclusion rules: points exactly on the query rectangle's edges are included
  • Duplicate handling: store multiple points per node or use a counter
  • Time complexity: O(log n) average for insert/query, but can degrade to O(n) in worst case; trade-offs vs. other spatial indexes

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