fastquadtree 1.3.1

Rust-accelerated quadtree for Python with fast inserts, range queries, and k-NN search.

fastquadtree

Rust-optimized quadtree with a clean Python API

👉 Check out the Docs: https://elan456.github.io/fastquadtree/

Why use fastquadtree

• Clean Python API with no external dependencies and modern typing hints
• The fastest quadtree Python package (>10x faster than pyqtree)
• Prebuilt wheels for Windows, macOS, and Linux
• Support for inserting bounding boxes or points
• Fast KNN and range queries
• Optional object tracking for id ↔ object mapping
• Fast serialization to/from bytes
• Support for multiple data types (f32, f64, i32, i64) for coordinates
• 100% test coverage and CI on GitHub Actions
• Offers a drop-in pyqtree shim that is 6.567x faster while keeping the same API

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Examples

See examples of how fastquadtree can be used in the runnables section.

Install

pip install fastquadtree

from fastquadtree import QuadTree  # Point handling
from fastquadtree import RectQuadTree  # Bounding box handling
from fastquadtree.pyqtree import Index  # Drop-in pyqtree shim (6.567x faster while keeping the same API)

Benchmarks

fastquadtree outperforms all other quadtree Python packages, including the Rtree spatial index.

Library comparison

Summary (largest dataset, PyQtree baseline)

• Points: 250,000, Queries: 500
• Fastest total: fastquadtree at 0.120 s

Library	Build (s)	Query (s)	Total (s)	Speed vs PyQtree
fastquadtree	0.031	0.089	0.120	14.64×
Shapely STRtree	0.179	0.100	0.279	6.29×
nontree-QuadTree	0.595	0.605	1.200	1.46×
Rtree	0.961	0.300	1.261	1.39×
e-pyquadtree	1.005	0.660	1.665	1.05×
PyQtree	1.492	0.263	1.755	1.00×
quads	1.407	0.484	1.890	0.93×

See the benchmark section for details, including configurations, system info, and native vs shim benchmarks.

Quickstart

See the quickstart guide

API

See the full API

QuadTree(bounds, capacity, max_depth=None, track_objects=False, start_id=1)

• bounds — tuple (min_x, min_y, max_x, max_y) defines the 2D area covered by the quadtree
• capacity — max number of points kept in a leaf before splitting
• max_depth — optional depth cap. If omitted, the tree can keep splitting as needed
• track_objects — if True, the wrapper maintains an id → object map for convenience.
• start_id — starting value for auto-assigned ids

Key Methods

• insert(xy, *, id=None, obj=None) -> int

• query(rect, *, as_items=False) -> list

• nearest_neighbor(xy, *, as_item=False) -> (id, x, y) | Item | None

• delete(id, xy) -> bool

There are more methods and object tracking versions in the docs.

Geometric conventions

• Rectangles are (min_x, min_y, max_x, max_y).
• Containment rule is closed on the min edge and open on the max edge (x >= min_x and x < max_x and y >= min_y and y < max_y). This only matters for points exactly on edges.

Performance tips

• Choose capacity so that leaves keep a small batch of points. Typical values are 8 to 64.
• If your data is very skewed, set a max_depth to prevent long chains.
• For fastest local runs, use maturin develop --release.
• The wrapper maintains an object map only if the quadtree was constructed with track_objects=True. If you don't need it, leave it off for best performance.
• Refer to the Native vs Shim Benchmark for overhead details.

Pygame Ball Pit Demo

A simple demo of moving objects with collision detection using fastquadtree. You can toggle between quadtree mode and brute-force mode to see the performance difference.

See the runnables guide for setup instructions.

FAQ

What happens if I insert the same id more than once? Allowed. For k-nearest, duplicates are de-duplicated by id. For range queries you will see every inserted point.

Can I delete items from the quadtree? Yes! Use delete(id, xy) to remove specific items. You must provide both the ID and exact location for precise deletion. This handles cases where multiple items exist at the same location. If you're using track_objects=True, you can also use delete_by_object(obj) for convenient object-based deletion with O(1) lookup. The tree automatically merges nodes when item counts drop below capacity.

Can I store rectangles or circles? Yes, you can store rectangles using the RectQuadTree class. Circles can be approximated with bounding boxes. See the RectQuadTree docs for details.

License

MIT. See LICENSE.

Acknowledgments

• Python libraries compared: PyQtree, e-pyquadtree, Rtree, nontree, quads, Shapely
• Built with PyO3 and maturin