mirror of https://github.com/tidwall/tile38.git
95 lines
3.2 KiB
Markdown
95 lines
3.2 KiB
Markdown
# `BoxTree`
|
|
|
|
[](https://godoc.org/github.com/tidwall/boxtree)
|
|
|
|
**EXPERIMENTAL**
|
|
|
|
This package provides an in-memory R-Tree implementation for Go. It's designed
|
|
for [Tile38](https://github.com/tidwall/tile38).
|
|
|
|
<img src="/res/cities.png" width="512" height="256" border="0" alt="Cities">
|
|
|
|
## Features
|
|
|
|
- Support for 2 and 3 dimensions
|
|
- Optimized for fast box inserts and replacements.
|
|
|
|
## Usage
|
|
|
|
### Installing
|
|
|
|
To start using BoxTree, install Go and run `go get`:
|
|
|
|
```sh
|
|
$ go get -u github.com/tidwall/boxtree
|
|
```
|
|
|
|
### Basic operations
|
|
|
|
```go
|
|
// create a 2D BoxTree
|
|
tr := boxtree.New(2)
|
|
|
|
// insert a point
|
|
tr.Insert([]float64{-112.0078, 33.4373}, nil, "PHX")
|
|
|
|
// insert a box
|
|
tr.Insert([]float64{10, 10}, []float64{20, 20}, "rect")
|
|
|
|
// search
|
|
tr.Search([]float64{-112.1, 33.4}, []float64{-112.0, 33.5},
|
|
func(min, max []float64, value interface{}) bool {
|
|
println(value.(string)) // prints "PHX"
|
|
},
|
|
)
|
|
|
|
// delete
|
|
tr.Delete([]float64{-112.0078, 33.4373}, []float64{-112.0078, 33.4373}, "PHX")
|
|
```
|
|
|
|
## Algorithms
|
|
|
|
This implementation is a variant of the original paper:
|
|
[R-TREES. A DYNAMIC INDEX STRUCTURE FOR SPATIAL SEARCHING](http://www-db.deis.unibo.it/courses/SI-LS/papers/Gut84.pdf)
|
|
|
|
### Inserting
|
|
|
|
Same as the original algorithm. From the root to the leaf, the boxes which will incur the least enlargment are chosen. Ties go to boxes with the smallest area.
|
|
|
|
### Deleting
|
|
|
|
Same as the original algorithm. A target box is deleted directly. When the number of children in a box falls below it's minumum entries, it is removed from the tree and it's items are re-inserted.
|
|
|
|
### Splitting
|
|
|
|
This is a custom algorithm.
|
|
It attempts to minimize intensive operations such as pre-sorting the children and comparing overlaps & area sizes.
|
|
The desire is to do simple single axis distance calculations each child only once, with a target 50/50 chance that the child might be moved in-memory.
|
|
|
|
When a box has reached it's max number of entries it's largest axis is calculated and the box is split into two smaller boxes, named `left` and `right`.
|
|
Each child boxes is then evaluated to determine which smaller box it should be placed into.
|
|
Two values, `min-dist` and `max-dist`, are calcuated for each child.
|
|
|
|
- `min-dist` is the distance from the parent's minumum value of it's largest axis to the child's minumum value of the parent largest axis.
|
|
- `max-dist` is the distance from the parent's maximum value of it's largest axis to the child's maximum value of the parent largest axis.
|
|
|
|
When the `min-dist` is less than `max-dist` then the child is placed into the `left` box.
|
|
When the `max-dist` is less than `min-dist` then the child is placed into the `right` box.
|
|
When the `min-dist` is equal to `max-dist` then the child is placed into an `equal` bucket until all of the children are evaluated.
|
|
Each `equal` box is then one-by-one placed in either `left` or `right`, whichever has less children.
|
|
|
|
|
|
## Performance
|
|
|
|
In my testing:
|
|
|
|
- Insert show similar performance as the quadratic R-tree and ~1.2x - 1.5x faster than R*tree.
|
|
- Search and Delete is ~1.5x - 2x faster than quadratic and about the same as R*tree.
|
|
|
|
I hope to provide more details in the future.
|
|
|
|
## License
|
|
|
|
`BoxTree` source code is available under the MIT License.
|
|
|