空间查询算法RBush

方案

https://github.com/mourner/rbush

Usage

Importing RBush

// as a ES module
import RBush from 'rbush';

// as a CommonJS module
const RBush = require('rbush');

Creating a Tree

const tree = new RBush();

An optional argument to RBush defines the maximum number of entries in a tree node. 9 (used by default) is a reasonable choice for most applications. Higher value means faster insertion and slower search, and vice versa.

const tree = new RBush(16);

Adding Data

Insert an item:

const item = {
    minX: 20,
    minY: 40,
    maxX: 30,
    maxY: 50,
    foo: 'bar'
};
tree.insert(item);

Removing Data

Remove a previously inserted item:

tree.remove(item);

By default, RBush removes objects by reference. However, you can pass a custom equals function to compare by value for removal, which is useful when you only have a copy of the object you need removed (e.g. loaded from server):

tree.remove(itemCopy, (a, b) => {
    return a.id === b.id;
});

Remove all items:

tree.clear();

Data Format

By default, RBush assumes the format of data points to be an object with minX, minY, maxX and maxY properties. You can customize this by overriding toBBox, compareMinX and compareMinY methods like this:

class MyRBush extends RBush {
    toBBox([x, y]) { return {minX: x, minY: y, maxX: x, maxY: y}; }
    compareMinX(a, b) { return a.x - b.x; }
    compareMinY(a, b) { return a.y - b.y; }
}
const tree = new MyRBush();
tree.insert([20, 50]); // accepts [x, y] points

If you're indexing a static list of points (you don't need to add/remove points after indexing), you should use kdbush which performs point indexing 5-8x faster than RBush.

Bulk-Inserting Data

Bulk-insert the given data into the tree:

tree.load([item1, item2, ...]);

Bulk insertion is usually ~2-3 times faster than inserting items one by one. After bulk loading (bulk insertion into an empty tree), subsequent query performance is also ~20-30% better.

Note that when you do bulk insertion into an existing tree, it bulk-loads the given data into a separate tree and inserts the smaller tree into the larger tree. This means that bulk insertion works very well for clustered data (where items in one update are close to each other), but makes query performance worse if the data is scattered.

Search

const result = tree.search({
    minX: 40,
    minY: 20,
    maxX: 80,
    maxY: 70
});

Returns an array of data items (points or rectangles) that the given bounding box intersects.

Note that the search method accepts a bounding box in {minX, minY, maxX, maxY} format regardless of the data format.

const allItems = tree.all();

Returns all items of the tree.

Collisions

const result = tree.collides({minX: 40, minY: 20, maxX: 80, maxY: 70});

Returns true if there are any items intersecting the given bounding box, otherwise false.

Export and Import

// export data as JSON object
const treeData = tree.toJSON();

// import previously exported data