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 (so you can transfer it between threads or store it as a compact binary file).

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]);

// 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 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/flatbush@3.0.0/flatbush.min.js"></script>

API

new Flatbush(numItems[, nodeSize, ArrayType, data])

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 tree storage (Float64Array by default); other types may be faster in certain cases (e.g. Int32Array when your data is integer).
data: if provided an array or an array buffer from a previously indexed Flatbush object (index.data or index.data.buffer), an index will be recreated from this data (useful for transfering indices between threads).

index.add(minX, minY, maxX, maxY)

Adds a given rectangle to the index.

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 in a given bounding box.

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');

Flatbush.from(data)

Recreates a Flatbush index from raw ArrayBuffer data (that's exposed as index.data on a previously indexed Flatbush instance). Very useful for transfering 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.

Performance

Running npm run bench with Node v8.10.0:

1000000 rectangles

flatbush: 252.849ms
1000 searches 10%: 617.473ms
1000 searches 1%: 66.968ms
1000 searches 0.01%: 7.818ms

rbush: 1083.758ms
1000 searches 10%: 920.252ms
1000 searches 1%: 173.104ms
1000 searches 0.01%: 19.057ms

Nanonid/flatbush