go-adaptive-radix-tree

Adaptive Radix Trees implemented in Go

Programming language: Go

License: MIT License

Tags: Data Structures    

Latest version: v1.0.4

README

An Adaptive Radix Tree Implementation in Go

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
• O(k) search/insert/delete operations, where k is the length of the key
• Minimum / Maximum value lookups
• Ordered iteration
• Prefix-based iteration
• Support for keys with null bytes, any byte array could be a key

Usage

package main

import (
    "fmt"
    "github.com/plar/go-adaptive-radix-tree"
)

func main() {

    tree := art.New()

    tree.Insert(art.Key("Hi, I'm Key"), "Nice to meet you, I'm Value")
    value, found := tree.Search(art.Key("Hi, I'm Key"))
    if found {
        fmt.Printf("Search value=%v\n", value)
    }

    tree.ForEach(func(node art.Node) bool {
        fmt.Printf("Callback value=%v\n", node.Value())
        return true
    })

    for it := tree.Iterator(); it.HasNext(); {
        value, _ := it.Next()
        fmt.Printf("Iterator value=%v\n", value.Value())
    }
}

// Output:
// Search value=Nice to meet you, I'm Value
// Callback value=Nice to meet you, I'm Value
// Iterator value=Nice to meet you, I'm Value

Documentation

Check out the documentation on godoc.org

Performance

plar/go-adaptive-radix-tree outperforms kellydunn/go-art by avoiding memory allocations during search operations. It also provides prefix based 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 benchmark

References

[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.