flatbush
v4.4.0
Published
Fast static spatial index for rectangles
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Flatbush
A really fast static spatial index for 2D points and rectangles in JavaScript.
An efficient implementation of the packed Hilbert R-tree algorithm. Enables fast spatial queries on a very large number of objects (e.g. millions), which is very useful in maps, data visualizations and computational geometry algorithms. Similar to RBush, with the following key differences:
- Static: you can't add/remove items after initial indexing.
- Faster indexing and search, with much lower memory footprint.
- Index is stored as a single array buffer (to transfer between threads or save as a compact binary file).
Supports geographic locations with the geoflatbush extension. See also: KDBush, a similar library for points.
Usage
// initialize Flatbush for 1000 items
const index = new Flatbush(1000);
// fill it with 1000 rectangles
for (const p of items) {
index.add(p.minX, p.minY, p.maxX, p.maxY);
}
// perform the indexing
index.finish();
// make a bounding box query
const found = index.search(minX, minY, maxX, maxY).map((i) => items[i]);
// make a k-nearest-neighbors query
const neighborIds = index.neighbors(x, y, 5);
// instantly transfer the index from a worker to the main thread
postMessage(index.data, [index.data]);
// reconstruct the index from a raw array buffer
const index = Flatbush.from(e.data);
Install
Install with NPM: npm install flatbush
, then import as a module:
import Flatbush from 'flatbush';
Or use as a module directly in the browser with jsDelivr:
<script type="module">
import Flatbush from 'https://cdn.jsdelivr.net/npm/flatbush/+esm';
</script>
Alternatively, there's a browser bundle with a Flatbush
global variable:
<script src="https://cdn.jsdelivr.net/npm/flatbush"></script>
API
new Flatbush(numItems[, nodeSize, ArrayType, ArrayBufferType])
Creates a Flatbush index that will hold a given number of items (numItems
). Additionally accepts:
nodeSize
: size of the tree node (16
by default); experiment with different values for best performance (increasing this value makes indexing faster and queries slower, and vise versa).ArrayType
: the array type used for coordinates storage (Float64Array
by default); other types may be faster in certain cases (e.g.Int32Array
when your data is integer).ArrayBufferType
: the array buffer type used to store data (ArrayBuffer
by default); you may preferSharedArrayBuffer
if you want to share the index between threads (multipleWorker
,SharedWorker
orServiceWorker
).
index.add(minX, minY[, maxX, maxY])
Adds a given rectangle to the index. Returns a zero-based, incremental number that represents the newly added rectangle.
If not provided, maxX
and maxY
default to minX
and minY
(essentially adding a point).
index.finish()
Performs indexing of the added rectangles.
Their number must match the one provided when creating a Flatbush
object.
index.search(minX, minY, maxX, maxY[, filterFn])
Returns an array of indices of items intersecting or touching a given bounding box. Item indices refer to the value returned by index.add()
.
const ids = index.search(10, 10, 20, 20);
If given a filterFn
, calls it on every found item (passing an item index)
and only includes it if the function returned a truthy value.
const ids = index.search(10, 10, 20, 20, (i) => items[i].foo === 'bar');
index.neighbors(x, y[, maxResults, maxDistance, filterFn])
Returns an array of item indices in order of distance from the given x, y
(known as K nearest neighbors, or KNN). Item indices refer to the value returned by index.add()
.
const ids = index.neighbors(10, 10, 5); // returns 5 ids
maxResults
and maxDistance
are Infinity
by default.
Also accepts a filterFn
similar to index.search
.
Flatbush.from(data[, byteOffset])
Recreates a Flatbush index from raw ArrayBuffer
or SharedArrayBuffer
data
(that's exposed as index.data
on a previously indexed Flatbush instance).
Very useful for transferring or sharing indices between threads or storing them in a file.
Properties
data
: array buffer that holds the index.minX
,minY
,maxX
,maxY
: bounding box of the data.numItems
: number of stored items.nodeSize
: number of items in a node tree.ArrayType
: array type used for internal coordinates storage.IndexArrayType
: array type used for internal item indices storage.
Performance
Running node bench.js
with Node v14:
bench | flatbush | rbush --- | --- | --- index 1,000,000 rectangles | 273ms | 1143ms 1000 searches 10% | 575ms | 781ms 1000 searches 1% | 63ms | 155ms 1000 searches 0.01% | 6ms | 17ms 1000 searches of 100 neighbors | 24ms | 43ms 1 search of 1,000,000 neighbors | 133ms | 280ms 100,000 searches of 1 neighbor | 710ms | 1170ms
Ports
- chusitoo/flatbush (C++ port)
- jbuckmccready/static_aabb2d_index (Rust port)
- jbuckmccready/Flatbush (C# port)
- bmharper/flatbush-python (Python port)
- FlatGeobuf (a geospatial format inspired by Flatbush)
- IMQS/flatbush (C++ port, no longer maintained)
- msfstef/flatbush-dart (Dart port)