ergo/vendor/github.com/tidwall/btree

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 1.16.5. 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 163ms, 6,140,597/sec, 162 ns/op, 30.9 MB, 32 bytes/op
tidwall: set-seq        1,000,000 ops in 141ms, 7,075,240/sec, 141 ns/op, 36.6 MB, 38 bytes/op
tidwall: set-seq-hint   1,000,000 ops in 79ms, 12,673,902/sec, 78 ns/op, 36.6 MB, 38 bytes/op
tidwall: load-seq       1,000,000 ops in 40ms, 24,887,293/sec, 40 ns/op, 36.6 MB, 38 bytes/op
go-arr:  append         1,000,000 ops in 51ms, 19,617,269/sec, 50 ns/op

** random set **
google:  set-rand       1,000,000 ops in 666ms, 1,501,583/sec, 665 ns/op, 21.5 MB, 22 bytes/op
tidwall: set-rand       1,000,000 ops in 569ms, 1,756,845/sec, 569 ns/op, 26.7 MB, 27 bytes/op
tidwall: set-rand-hint  1,000,000 ops in 670ms, 1,491,637/sec, 670 ns/op, 26.4 MB, 27 bytes/op
tidwall: set-again      1,000,000 ops in 488ms, 2,050,667/sec, 487 ns/op, 27.1 MB, 28 bytes/op
tidwall: set-after-copy 1,000,000 ops in 494ms, 2,022,980/sec, 494 ns/op, 27.9 MB, 29 bytes/op
tidwall: load-rand      1,000,000 ops in 594ms, 1,682,937/sec, 594 ns/op, 26.1 MB, 27 bytes/op

** sequential get **
google:  get-seq        1,000,000 ops in 141ms, 7,078,690/sec, 141 ns/op
tidwall: get-seq        1,000,000 ops in 124ms, 8,075,925/sec, 123 ns/op
tidwall: get-seq-hint   1,000,000 ops in 40ms, 25,142,979/sec, 39 ns/op

** random get **
google:  get-rand       1,000,000 ops in 152ms, 6,593,518/sec, 151 ns/op
tidwall: get-rand       1,000,000 ops in 128ms, 7,783,293/sec, 128 ns/op
tidwall: get-rand-hint  1,000,000 ops in 135ms, 7,403,823/sec, 135 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.