boostree

A fast in-memory R*-tree index over points or axis-aligned bounding boxes, backed by Boost.Geometry via pybind11.

Two surfaces:

Native API (boostree.RTree) — a clean, batched, numpy-first interface designed for new code.
rtree.index drop-in (boostree.index.Index, boostree.index.Property) — method-for-method compatible with the subset of the PyPI rtree package.

On that subset it's ~6× faster per query and ~4× faster to build than libspatialindex (which the PyPI rtree package wraps), plus:

Feature	`rtree` (PyPI)	`boostree`
Bulk `nearest` over many query points	yes (`nearest_v`)	yes
Bulk `intersection` over many query bboxes	yes (`intersection_v`)	yes
GIL released during batch queries	no	yes
Free-threaded CPython (PEP 703)	no	yes
Bulk `delete` by id-set	no — one at a time, needs original coords	yes
Bulk `delete` by bbox range	no	yes
`update` (move entry by id)	no — hand-roll `delete` + `insert`	yes
`copy.copy` / `copy.deepcopy`	aliases C++ handle	independent deep copy
`pickle`	unsupported	works (state is `(ids, mins, maxs)`, or `(ids, points)` under point storage)

Native API

import boostree
import numpy as np

# Build from points or boxes
tree = boostree.RTree.from_points(pts)               # zero-volume boxes
tree = boostree.RTree.from_points(pts, ids=eids)     # with ids
tree = boostree.RTree.from_boxes(mins, maxs)         # explicit boxes
tree = boostree.RTree.from_pairs(items, dim=3)       # iterable form

# A point-only tree: entries hold no extent, so it uses less memory
tree = boostree.RTree.from_points(pts, storage="point")

# k-nearest, rows sorted by ascending distance; exactly tied candidates
# are picked and ordered by coordinates and then by id, so the answer
# never depends on the order the tree was built in; optional per-query
# radius cap (radius<=0 disables)
ids, dists = tree.knn(queries, k=8)
ids, dists = tree.knn(queries, k=8, radius=0.5)
ids, dists = tree.knn(queries, k=8, radius=per_query_radii)

# k-nearest under elliptical (Mahalanobis) metrics: transforms maps
# world coordinates into each metric class's frame; radius is then in
# metric-frame units (radius=1.0 keeps hits inside the ellipse)
ids, dists = tree.knn(queries, k=8, transforms=tmats, class_ids=cids)

# Box queries: pass maxs alongside queries (Euclidean only)
ids, dists = tree.knn(qmins, k=4, maxs=qmaxs)

# Bbox-vs-bbox intersection (ids flat, counts per query)
ids, counts = tree.intersect(qmins, qmaxs)

# Existence-only variant: (n,) bool, stops at the first hit per query
hit = tree.intersect_any(qmins, qmaxs)

# All queries are batched — no separate single-query method.  For one
# query, just pass a length-1 array
ids, dists = tree.knn(np.array([[0.5, 0.5, 0.5]]), k=4)

# Mutation — batched and id-based, no need to remember the original
# coordinates the way rtree.index requires
tree.delete(ids_to_drop)                             # by id-set
tree.delete_in(qmins, qmaxs)                         # by bbox-range
tree.update(ids, new_points)                         # upsert by id
tree.add(new_ids, new_points)                        # incremental insert

# copy.copy / copy.deepcopy both produce fully independent trees
import copy
tree2 = copy.deepcopy(tree)

# Pickle round-trips through (ids, mins, maxs), or (ids, points) under
# point storage — independent of host architecture / endianness /
# pointer width
import pickle
blob = pickle.dumps(tree)
tree3 = pickle.loads(blob)

# Or the same arrays directly, e.g. for HDF5 or numpy.savez
ids_arr, mins_arr, maxs_arr = tree.dump()             # box storage
ids_arr, pts_arr = point_tree.dump()                  # point storage

The native API differs from rtree.index in several ways:

Output shape is fixed (n, k) for both ids and distances, rows sorted by ascending distance and padded with -1 / +inf. No variable-length flat returns.
Elliptical metrics are first-class: knn(..., transforms=..., class_ids=...) runs an exact best-first search under per-query Mahalanobis metrics — no oversample-and-re-rank workarounds.
radius does not mutate the caller's array (the rtree-compat shim does, matching libspatialindex; the native API doesn't because callers can read dists instead).
radius <= 0 disables the cap (matches libspatialindex), so the zero-radius sentinel works without surprises.
Property is gone. dim is inferred from your input; the rtree capacities are not user-tunable in Boost's rstar.
Mutation is id-based and batched. rtree.index.delete(id, coords) needs the original bbox alongside the id and removes one entry per call; tree.delete(ids) accepts an array and removes every match in one pass. There's no update in rtree.index at all — callers hand-roll delete + insert; tree.update(ids, points) does it for you with upsert semantics.

rtree.index drop-in

Existing code that uses from rtree.index import Index, Property can switch to boostree without source changes:

import boostree
boostree.install_rtree_compat()        # one-line shim into sys.modules

# from this point onwards the rtree imports resolve to boostree:
from rtree.index import Index, Property
tree = Index((ids, mins, maxs), properties=Property(dimension=3))
nidxs, counts, dists = tree.nearest_v(qpts, qpts, num_results=8)
ids, counts = tree.intersection_v(qmins, qmaxs)

Covered:

Bulk batched paths — bulk-load (numpy (ids, mins, maxs) or (id, bbox, obj)-iterable), nearest_v with max_dists / return_max_dists, intersection_v.
Single-query rtree methods — intersection(bbox, objects=False), nearest(point, num_results=1), insert(id, coords), and delete(eid, coordinates=None) (boostree ignores the coordinates argument since it can look the id up directly; rtree requires it).
copy.copy / copy.deepcopy and pickle — work too, unlike upstream rtree.index where copy aliases the C++ index handle and pickle isn't supported at all.

Plus four boostree-only batched paths exposed on the same Index:

idx.delete_v(ids)                    # bulk delete by id
idx.delete_range_v(pmins, pmaxs)     # bulk delete by bbox intersection
idx.update_v(ids, pmins, pmaxs)      # bulk upsert
idx.intersection_any_v(pmins, pmaxs) # existence test, early exit

Notably not supported by either surface:

dimension other than 2, 3, or 4
persistent / disk-backed indexes
user objects carried through queries (objects=True)
arbitrary point types other than float64
nearest_v(strict=False)'s overflow-on-equidistant-ties behaviour (we always return exactly num_results ids, padded with -1)

Property.index_capacity / leaf_capacity are accepted for API compatibility but ignored — Boost picks its own internal layout.

Install

pip install boostree

Wheels are published for Linux (x86_64, aarch64), macOS (x86_64, arm64), and Windows (AMD64) on CPython 3.10–3.14. No Boost or compiler is required at install time — the minimal Boost.Geometry header subset is vendored in the source distribution.

To build from source you only need a C++17 compiler and CMake ≥ 3.18:

pip install .

To rebuild the vendored Boost subset against a newer Boost release, install bcp (e.g. apt install libboost-tools-dev) and run:

python scripts/vendor_boost.py --boost-root /path/to/boost_x_y_z

To build against system Boost instead of the vendored subset, pass -DBOOSTREE_USE_SYSTEM_BOOST=ON to CMake.

OpenMP is auto-detected at build time and used to parallelise batched queries (knn, nearest_v, intersect, intersection_v) across threads — typically ~10× speedup at 16 threads on aircraft-class workloads. macOS clang ships without OpenMP, so the macOS wheels run serial (still faster than libspatialindex).

Development

Source is formatted via black (Python) and clang-format (C++) — a project-local .clang-format and [tool.black] block in pyproject.toml pin the styles.

To run both formatters at once:

pip install pre-commit
pre-commit run --all-files

Or hand-run:

black boostree/ tests/ scripts/
clang-format -i src/*.cc benchmarks/*.cc

Threading

The C++ extension declares itself compatible with PEP 703 free-threaded CPython (py::mod_gil_not_used()), and the batched query methods (nearest_v / intersection_v / knn / intersect) release the GIL so they parallelise across Python threads even on the regular GIL build.

Each tree carries a std::shared_mutex: read methods take a shared lock, mutators (insert_point, delete_*, update_*) take an exclusive lock. Concurrent reads run in parallel; reads block during a write and vice versa. No external synchronisation is required, even on free-threaded CPython.

Benchmarks

On 161 368 wall faces × 1 811 835 query points × k=8:

	build	k=8 query (1T)	k=8 query (16T)
`rtree` (PyPI)	1.10 s	80 s	n/a (serial)
`boostree`	0.03 s	~12 s	~1 s

Changes in 0.3

CPython 3.14 is supported and gets published wheels.
Point storage. RTree.from_points(pts, storage="point") holds a bare point per entry rather than a zero-volume box, giving the same answers in less memory. Entries hold no extent, so update() refuses a maxs.
Order-independent ties. Candidates at exactly equal distances are selected and ordered by coordinates and then by id, so knn no longer depends on the order the tree was built in.
Non-finite coordinates are refused. A stored NaN read as an exact hit from anywhere along that axis; inserting one now raises.
Breaking: the keyword naming the far corner of a query or update box is now maxs throughout the native API, matching from_boxes / intersect / delete_in. RTree.knn(..., query_max=) and RTree.update(..., query_max=) are now maxs=. The boostree.index drop-in is unaffected.

License

Boost Software License 1.0 (matches Boost.Geometry).

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boostree 0.3.1

boostree

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rtree.index drop-in

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BLAKE2b-256	`070258e13b67a637dd971276ec5a54aa7b9b80536f5f6e8054dbaecdf926560e`

Algorithm	Hash digest
SHA256	`83671a7bd519bf9019399055e6123f2a6b2ed614cd185ecd073e84c9dd4e349f`
MD5	`40d21e361ee213aa42cb7eea043f262b`
BLAKE2b-256	`ebbdf8797b783c7351a83c4045f5ff5732ea492d61814ce90c8c85529151ce8b`

Algorithm	Hash digest
SHA256	`c6a25cdb0f24fdde31c4d3ef2bcaa3a63964c42b363b503e81fc894bf16b38bf`
MD5	`70072162f7c6b3b4ffe9e1931545154e`
BLAKE2b-256	`56b1d4bc9bc716abf2ea80b62533e2407fead2a1e8a55e43c2b4e32ff8c1aeff`

Algorithm	Hash digest
SHA256	`e992149fac0544beb9e2c28c2554a9c7351dc360db086dfc512ff91e66d4c59c`
MD5	`8700bc228ae8d2834b89ee80b30e31f8`
BLAKE2b-256	`95b3c0bace7ce7c3ac9c1301ee8fd66139f1c3bb56e05681ba800e396183102a`

Algorithm	Hash digest
SHA256	`620d422888bdb86455ab8edad3f94bf09e4aa483549b30fd301b2dfb8889859d`
MD5	`eced509e08d376241d35d25dbe82e810`
BLAKE2b-256	`cf80b3775d16ffb3dfb2293f4ebe732d635dfeb0899d26fd18583d5efc0667d4`