This library provides a Go implementation of the Adaptive Radix Tree (ART).
Features:
- Lookup performance surpasses highly tuned alternatives
- Support for highly efficient insertions and deletions
- Space efficient
- Performance is comparable to hash tables
- Maintains the data in sorted order, which enables additional operations like range scan and prefix lookup
Keys are sorted lexicographically based on their byte values.
O(k)search/insert/delete operations, wherekis the length of the key- Minimum / Maximum value lookups
- Ordered iteration
- Prefix-based iteration
- Reverse iteration support
- Support for keys with null bytes, any byte array could be a key
package main
import (
"fmt"
art "github.com/plar/go-adaptive-radix-tree/v2"
)
func main() {
// Initialize a new Adaptive Radix Tree
tree := art.New()
// Insert key-value pairs into the tree
tree.Insert(art.Key("apple"), "A sweet red fruit")
tree.Insert(art.Key("banana"), "A long yellow fruit")
tree.Insert(art.Key("cherry"), "A small red fruit")
tree.Insert(art.Key("date"), "A sweet brown fruit")
// Search for a value by key
if value, found := tree.Search(art.Key("banana")); found {
fmt.Println("Found:", value)
} else {
fmt.Println("Key not found")
}
// Iterate over the tree in ascending order
fmt.Println("\nAscending order iteration:")
tree.ForEach(func(node art.Node) bool {
fmt.Printf("Key: %s, Value: %s\n", node.Key(), node.Value())
return true
})
// Iterate over the tree in descending order using reverse traversal
fmt.Println("\nDescending order iteration:")
tree.ForEach(func(node art.Node) bool {
fmt.Printf("Key: %s, Value: %s\n", node.Key(), node.Value())
return true
}, art.TraverseReverse)
// Iterate over keys with a specific prefix
fmt.Println("\nIteration with prefix 'c':")
tree.ForEachPrefix(art.Key("c"), func(node art.Node) bool {
fmt.Printf("Key: %s, Value: %s\n", node.Key(), node.Value())
return true
})
}
// Expected Output:
// Found: A long yellow fruit
//
// Ascending order iteration:
// Key: apple, Value: A sweet red fruit
// Key: banana, Value: A long yellow fruit
// Key: cherry, Value: A small red fruit
// Key: date, Value: A sweet brown fruit
//
// Descending order iteration:
// Key: date, Value: A sweet brown fruit
// Key: cherry, Value: A small red fruit
// Key: banana, Value: A long yellow fruit
// Key: apple, Value: A sweet red fruit
//
// Iteration with prefix 'c':
// Key: cherry, Value: A small red fruitCheck out the documentation on pkg.go.dev/github.com/plar/go-adaptive-radix-tree/v2.
- update
importstatement
from `art "github.com/plar/go-adaptive-radix-tree"`
to `art "github.com/plar/go-adaptive-radix-tree/v2"`
- update go module dependency
$ go get github.com/plar/go-adaptive-radix-tree/v2
$ go mod tidy
If you had implemented your own version of the Tree interface, then you need to update the following method to support options. These are the only changes in the interface.
ForEachPrefix(keyPrefix Key, callback Callback, options ...int)
plar/go-adaptive-radix-tree outperforms kellydunn/go-art by avoiding memory allocations during search operations. It also provides prefix based and reverse iteration over the tree.
Benchmarks were performed on datasets extracted from different projects:
- The "Words" dataset contains a list of 235,886 english words. [2]
- The "UUIDs" dataset contains 100,000 uuids. [2]
- The "HSK Words" dataset contains 4,995 words. [4]
| go-adaptive-radix-tree | # | Average time | Bytes per operation | Allocs per operation |
|---|---|---|---|---|
| Tree Insert Words | 9 | 117,888,698 ns/op | 37,942,744 B/op | 1,214,541 allocs/op |
| Tree Search Words | 26 | 44,555,608 ns/op | 0 B/op | 0 allocs/op |
| Tree Insert UUIDs | 18 | 59,360,135 ns/op | 18,375,723 B/op | 485,057 allocs/op |
| Tree Search UUIDs | 54 | 21,265,931 ns/op | 0 B/op | 0 allocs/op |
| go-art | ||||
| Tree Insert Words | 5 | 272,047,975 ns/op | 81,628,987 B/op | 2,547,316 allocs/op |
| Tree Search Words | 10 | 129,011,177 ns/op | 13,272,278 B/op | 1,659,033 allocs/op |
| Tree Insert UUIDs | 10 | 140,309,246 ns/op | 33,678,160 B/op | 874,561 allocs/op |
| Tree Search UUIDs | 20 | 82,120,943 ns/op | 3,883,131 B/op | 485,391 allocs/op |
To see more benchmarks just run
$ ./make qa/benchmarks
[1] The Adaptive Radix Tree: ARTful Indexing for Main-Memory Databases (Specification)
[2] C99 implementation of the Adaptive Radix Tree
[3] Another Adaptive Radix Tree implementation in Go
[4] HSK Words. HSK(Hanyu Shuiping Kaoshi) - Standardized test of Standard Mandarin Chinese proficiency.