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, wherek
is 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 fruit
Check out the documentation on pkg.go.dev/github.com/plar/go-adaptive-radix-tree/v2.
- update
import
statement
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.