btree

An efficient B-tree implementation in Go.

Features

Copy() method with copy-on-write support.
Fast bulk loading for pre-ordered data using the Load() method.
All operations are thread-safe.
Path hinting optimization for operations with nearby keys.

Installing

To start using btree, install Go and run go get:

$ go get -u github.com/tidwall/btree

Usage

package main

import (
	"fmt"

	"github.com/tidwall/btree"
)

type Item struct {
	Key, Val string
}

// byKeys is a comparison function that compares item keys and returns true
// when a is less than b.
func byKeys(a, b interface{}) bool {
	i1, i2 := a.(*Item), b.(*Item)
	return i1.Key < i2.Key
}

// byVals is a comparison function that compares item values and returns true
// when a is less than b.
func byVals(a, b interface{}) bool {
	i1, i2 := a.(*Item), b.(*Item)
	if i1.Val < i2.Val {
		return true
	}
	if i1.Val > i2.Val {
		return false
	}
	// Both vals are equal so we should fall though
	// and let the key comparison take over.
	return byKeys(a, b)
}

func main() {
	// Create a tree for keys and a tree for values.
	// The "keys" tree will be sorted on the Keys field.
	// The "values" tree will be sorted on the Values field.
	keys := btree.New(byKeys)
	vals := btree.New(byVals)

	// Create some items.
	users := []*Item{
		&Item{Key: "user:1", Val: "Jane"},
		&Item{Key: "user:2", Val: "Andy"},
		&Item{Key: "user:3", Val: "Steve"},
		&Item{Key: "user:4", Val: "Andrea"},
		&Item{Key: "user:5", Val: "Janet"},
		&Item{Key: "user:6", Val: "Andy"},
	}

	// Insert each user into both trees
	for _, user := range users {
		keys.Set(user)
		vals.Set(user)
	}

	// Iterate over each user in the key tree
	keys.Ascend(nil, func(item interface{}) bool {
		kvi := item.(*Item)
		fmt.Printf("%s %s\n", kvi.Key, kvi.Val)
		return true
	})

	fmt.Printf("\n")
	// Iterate over each user in the val tree
	vals.Ascend(nil, func(item interface{}) bool {
		kvi := item.(*Item)
		fmt.Printf("%s %s\n", kvi.Key, kvi.Val)
		return true
	})

	// Output:
	// user:1 Jane
	// user:2 Andy
	// user:3 Steve
	// user:4 Andrea
	// user:5 Janet
	// user:6 Andy
	//
	// user:4 Andrea
	// user:2 Andy
	// user:6 Andy
	// user:1 Jane
	// user:5 Janet
	// user:3 Steve
}

Operations

Basic

Len()                   # return the number of items in the btree
Set(item)               # insert or replace an existing item
Get(item)               # get an existing item
Delete(item)            # delete an item

Iteration

Ascend(pivot, iter)     # scan items in ascending order starting at pivot.
Descend(pivot, iter)    # scan items in descending order starting at pivot.

Queues

Min()                   # return the first item in the btree
Max()                   # return the last item in the btree
PopMin()                # remove and return the first item in the btree
PopMax()                # remove and return the last item in the btree

Bulk loading

Load(item)              # load presorted items into tree

Path hints

SetHint(item, *hint)    # insert or replace an existing item
GetHint(item, *hint)    # get an existing item
DeleteHint(item, *hint) # delete an item

Performance

This implementation was designed with performance in mind.

The following benchmarks were run on my 2019 Macbook Pro (2.4 GHz 8-Core Intel Core i9) using Go 1.17. The items are simple 8-byte ints.

google: The google/btree package
tidwall: The tidwall/btree package
go-arr: Just a simple Go array

** sequential set **
google:  set-seq        1,000,000 ops in 129ms, 7,761,884/sec, 128 ns/op, 31.0 MB, 32 bytes/op
tidwall: set-seq        1,000,000 ops in 116ms, 8,655,931/sec, 115 ns/op, 36.6 MB, 38 bytes/op
tidwall: set-seq-hint   1,000,000 ops in 52ms, 19,219,654/sec, 52 ns/op, 36.6 MB, 38 bytes/op
tidwall: load-seq       1,000,000 ops in 22ms, 45,096,800/sec, 22 ns/op, 36.6 MB, 38 bytes/op
go-arr:  append         1,000,000 ops in 48ms, 20,860,238/sec, 47 ns/op

** random set **
google:  set-rand       1,000,000 ops in 533ms, 1,876,341/sec, 532 ns/op, 21.5 MB, 22 bytes/op
tidwall: set-rand       1,000,000 ops in 495ms, 2,020,118/sec, 495 ns/op, 26.7 MB, 27 bytes/op
tidwall: set-rand-hint  1,000,000 ops in 537ms, 1,863,372/sec, 536 ns/op, 26.4 MB, 27 bytes/op
tidwall: set-again      1,000,000 ops in 350ms, 2,857,997/sec, 349 ns/op, 27.1 MB, 28 bytes/op
tidwall: set-after-copy 1,000,000 ops in 373ms, 2,682,891/sec, 372 ns/op, 27.9 MB, 29 bytes/op
tidwall: load-rand      1,000,000 ops in 504ms, 1,984,558/sec, 503 ns/op, 26.1 MB, 27 bytes/op

** sequential get **
google:  get-seq        1,000,000 ops in 92ms, 10,851,246/sec, 92 ns/op
tidwall: get-seq        1,000,000 ops in 82ms, 12,224,334/sec, 81 ns/op
tidwall: get-seq-hint   1,000,000 ops in 29ms, 34,086,961/sec, 29 ns/op

** random get **
google:  get-rand       1,000,000 ops in 106ms, 9,426,080/sec, 106 ns/op
tidwall: get-rand       1,000,000 ops in 104ms, 9,641,568/sec, 103 ns/op
tidwall: get-rand-hint  1,000,000 ops in 113ms, 8,819,336/sec, 113 ns/op

You can find the benchmark utility at tidwall/btree-benchmark

Contact

Josh Baker @tidwall

License

Source code is available under the MIT License.

bachhh / btree