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 (so you can transfer it between threads or store it as a compact binary file).
// 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 using NPM (npm install flatbush
) or Yarn (yarn add flatbush
), then:
// import as an ES module
import Flatbush from 'flatbush';
// or require in Node / Browserify
const Flatbush = require('flatbush');
Or use a browser build directly:
<script src="https://unpkg.com/[email protected]/flatbush.min.js"></script>
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.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).
Adds a given rectangle to the index. Returns a zero-based, incremental number that represents the newly-added rectangle.
Performs indexing of the added rectangles.
Their number must match the one provided when creating a Flatbush
object.
Returns an array of indices of items in 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');
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
.
Recreates a Flatbush index from raw ArrayBuffer
data
(that's exposed as index.data
on a previously indexed Flatbush instance).
Very useful for transferring indices between threads or storing them in a file.
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.
Running npm run bench
with Node v10.11.0:
bench | flatbush | rbush |
---|---|---|
index 1,000,000 rectangles | 263ms | 1208ms |
1000 searches 10% | 594ms | 1105ms |
1000 searches 1% | 68ms | 213ms |
1000 searches 0.01% | 9ms | 27ms |
1000 searches of 100 neighbors | 29ms | 58ms |
1 search of 1,000,000 neighbors | 148ms | 781ms |
100,000 searches of 1 neighbor | 870ms | 1548ms |