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A Left-Leaning Red-Black (LLRB) implementation of balanced binary search trees for Google Go
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GoLLRB is a Left-Leaning Red-Black (LLRB) implementation of 2-3 balanced binary search trees in Go Language.


As of this writing and to the best of the author's knowledge, Go still does not have a balanced binary search tree (BBST) data structure. These data structures are quite useful in a variety of cases. A BBST maintains elements in sorted order under dynamic updates (inserts and deletes) and can support various order-specific queries. Furthermore, in practice one often implements other common data structures like Priority Queues, using BBST's.

2-3 trees (a type of BBST's), as well as the runtime-similar 2-3-4 trees, are the de facto standard BBST algoritms found in implementations of Python, Java, and other libraries. The LLRB method of implementing 2-3 trees is a recent improvement over the traditional implementation. The LLRB approach was discovered relatively recently (in 2008) by Robert Sedgewick of Princeton University.

GoLLRB is a Go implementation of LLRB 2-3 trees.


GoLLRB has been used in some pretty heavy-weight machine learning tasks over many gigabytes of data. I consider it to be in stable, perhaps even production, shape. There are no known bugs.


With a healthy Go Language installed, simply run go get


package main

import (

func lessInt(a, b interface{}) bool { return a.(int) < b.(int) }

func main() {
    tree := llrb.New(lessInt)
    c := tree.IterAscend()
    for {
        u := <-c
        if u == nil {
        fmt.Printf("%d\n", int(u.(int)))


GoLLRB was written by Petar Maymounkov.

Follow me on Twitter @maymounkov!

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