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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.

Build Status minzipped size Simply Awesome


// 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

// make a bounding box query
const found =, 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(, []);

// reconstruct the index from a raw array buffer
const index = Flatbush.from(;


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 '';

Alternatively, there's a browser bundle with a Flatbush global variable:

<script src=""></script>


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 prefer SharedArrayBuffer if you want to share the index between threads (multiple Worker, SharedWorker or ServiceWorker).

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).


Performs indexing of the added rectangles. Their number must match the one provided when creating a Flatbush object., 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 =, 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 =, 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

Flatbush.from(data[, byteOffset])

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