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an R-Tree library for Go

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A library for efficiently storing and querying spatial data in the Go programming language.

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The R-tree is a popular data structure for efficiently storing and querying spatial objects; one common use is implementing geospatial indexes in database management systems. Both bounding-box queries and k-nearest-neighbor queries are supported.

R-trees are balanced, so maximum tree height is guaranteed to be logarithmic in the number of entries; however, good worst-case performance is not guaranteed. Instead, a number of rebalancing heuristics are applied that perform well in practice. For more details please refer to the references.

This implementation handles the general N-dimensional case; for a more efficient implementation for the 3-dimensional case, see Patrick Higgins' fork.

Getting Started

Get the source code from GitHub or, with Go 1 installed, run

go get

Make sure you

in your Go source files.


Storing, updating, and deleting objects

To create a new tree, specify the number of spatial dimensions and the minimum and maximum branching factor:

rt := rtreego.NewTree(2, 25, 50)

You can also bulk-load the tree when creating it by passing the objects as a parameter.

rt := rtreego.NewTree(2, 25, 50, objects...)

Any type that implements the

interface can be stored in the tree:
type Spatial interface {
  Bounds() *Rect

s are data structures for representing spatial objects, while
s represent spatial locations. Creating
s is easy--they're just slices of
p1 := rtreego.Point{0.4, 0.5}
p2 := rtreego.Point{6.2, -3.4}

To create a

, specify a location and the lengths of the sides:
r1, _ := rtreego.NewRect(p1, []float64{1, 2})
r2, _ := rtreego.NewRect(p2, []float64{1.7, 2.7})

To demonstrate, let's create and store some test data.

type Thing struct {
  where *Rect
  name string

func (t *Thing) Bounds() *Rect { return t.where }

rt.Insert(&Thing{r1, "foo"}) rt.Insert(&Thing{r2, "bar"})

size := rt.Size() // returns 2

We can insert and delete objects from the tree in any order.

// do some stuff...

Note that

function does the equality comparison by comparing the memory addresses of the objects. If you do not have a pointer to the original object anymore, you can define a custom comparator.
type Comparator func(obj1, obj2 Spatial) (equal bool)

You can use a custom comparator with

cmp := func(obj1, obj2 Spatial) bool {
  sp1 := obj1.(*IDRect)
  sp2 := obj2.(*IDRect)

return sp1.ID == sp2.ID }

rt.DeleteWithComparator(obj, cmp)

If you want to store points instead of rectangles, you can easily convert a point into a rectangle using the

var tol = 0.01

type Somewhere struct { location rtreego.Point name string wormhole chan int }

func (s *Somewhere) Bounds() *Rect { // define the bounds of s to be a rectangle centered at s.location // with side lengths 2 * tol: return s.location.ToRect(tol) }

rt.Insert(&Somewhere{rtreego.Point{0, 0}, "Someplace", nil})

If you want to update the location of an object, you must delete it, update it, and re-insert. Just modifying the object so that the

returned by
changes, without deleting and re-inserting the object, will corrupt the tree.


Bounding-box and k-nearest-neighbors queries are supported.

Bounding-box queries require a search

. It returns all objects that touch the search rectangle.
bb, _ := rtreego.NewRect(rtreego.Point{1.7, -3.4}, []float64{3.2, 1.9})

// Get a slice of the objects in rt that intersect bb: results := rt.SearchIntersect(bb)


You can filter out values during searches by implementing Filter functions.

type Filter func(results []Spatial, object Spatial) (refuse, abort bool)

A filter for limiting results by result count is included in the package for backwards compatibility.

// maximum of three results will be returned
tree.SearchIntersect(bb, LimitFilter(3))

Nearest-neighbor queries find the objects in a tree closest to a specified query point.

q := rtreego.Point{6.5, -2.47}
k := 5

// Get a slice of the k objects in rt closest to q: results = rt.NearestNeighbors(k, q)

More information

See GoDoc for full API documentation.


  • A. Guttman. R-trees: A Dynamic Index Structure for Spatial Searching. Proceedings of ACM SIGMOD, pages 47-57, 1984.

  • N. Beckmann, H .P. Kriegel, R. Schneider and B. Seeger. The R*-tree: An Efficient and Robust Access Method for Points and Rectangles. Proceedings of ACM SIGMOD, pages 323-331, May 1990.

  • N. Roussopoulos, S. Kelley and F. Vincent. Nearest Neighbor Queries. ACM SIGMOD, pages 71-79, 1995.


Written by Daniel Connelly ([email protected]).


rtreego is released under a BSD-style license, described in the


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