Quadtree
maang.io System Design Series
A quadtree recursively splits a 2D region into four quadrants, subdividing only where the data is dense. That structure turns "find everything near this point" and range queries from a full scan into a logarithmic descent — which is why quadtrees power maps, "nearby" search, collision detection, and image compression.
This deep dive comes in three parts:
- Foundations & Core Concepts — recursive subdivision, node capacity, and point vs region quadtrees.
- Internals — range and nearest-neighbour queries, the skew/degenerate cases, and comparisons to k-d trees and R-trees, with worked numbers.
- Interview — Staff/Principal Q&A: choosing a spatial index and the trade-offs vs geohashing.
Quadtree — Part 3: System Design & Interview Mastery
maang.io System Design Series
What is this?
You now know the quadtree from the root box down to the degenerate airport-parking-lot case. This chapter is where that knowledge pays off on a whiteboard. Interviewers rarely say "implement a quadtree" — they say "design a service that finds nearby drivers / restaurants / players," and they're watching to see whether you reach for a spatial index at the right moment, pick the right flavour for the constraints, and stay honest about the trade-offs when they push.
We'll do two things: first place the quadtree in real designs — the proximity service, and its close relationship to the Geohashing topic — then run a Staff/Principal mock interview where every question comes with what they're really asking, a model answer, and the senior line that signals you've built this before.
The one-line idea: interviewers use spatial indexing to test whether you can turn a fuzzy real-world need — "what's near me?" — into a concrete data-structure choice, reason about its cost and failure modes at scale, and know when a quadtree is the answer versus a geohash, a k-d tree, or an R-tree.
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