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internal | ||
btree.go | ||
LICENSE | ||
PATH_HINT.md | ||
README.md |
btree
An efficient B-tree implementation in Go.
Check out the generics branch if you want to try out btree with generic support for Go 1.18+
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 {
, Val string
Key}
// byKeys is a comparison function that compares item keys and returns true
// when a is less than b.
func byKeys(a, b interface{}) bool {
, i2 := a.(*Item), b.(*Item)
i1return 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 {
, i2 := a.(*Item), b.(*Item)
i1if 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.
:= btree.New(byKeys)
keys := btree.New(byVals)
vals
// Create some items.
:= []*Item{
users &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 {
.Set(user)
keys.Set(user)
vals}
// Iterate over each user in the key tree
.Ascend(nil, func(item interface{}) bool {
keys:= item.(*Item)
kvi .Printf("%s %s\n", kvi.Key, kvi.Val)
fmtreturn true
})
.Printf("\n")
fmt// Iterate over each user in the val tree
.Ascend(nil, func(item interface{}) bool {
vals:= item.(*Item)
kvi .Printf("%s %s\n", kvi.Key, kvi.Val)
fmtreturn 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
Get(item) # get an existing item
Set(item) # insert or replace an existing item
Delete(item) # delete an item
Len() # return the number of items in the btree
Iteration
Ascend(pivot, iter) # scan items in ascending order starting at pivot.
Descend(pivot, iter) # scan items in descending order starting at pivot.
Iter() # returns a read-only iterator for for-loops.
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
Array-like operations
GetAt(index) # returns the value at index
DeleteAt(index) # deletes the item at index
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 1.17.3. The items are simple 8-byte ints.
google
: The google/btree packagetidwall
: The tidwall/btree packagego-arr
: Just a simple Go array
** sequential set **
google: set-seq 1,000,000 ops in 178ms, 5,618,049/sec, 177 ns/op, 39.0 MB, 40 bytes/op
tidwall: set-seq 1,000,000 ops in 156ms, 6,389,837/sec, 156 ns/op, 23.5 MB, 24 bytes/op
tidwall: set-seq-hint 1,000,000 ops in 78ms, 12,895,355/sec, 77 ns/op, 23.5 MB, 24 bytes/op
tidwall: load-seq 1,000,000 ops in 53ms, 18,937,400/sec, 52 ns/op, 23.5 MB, 24 bytes/op
go-arr: append 1,000,000 ops in 78ms, 12,843,432/sec, 77 ns/op
** random set **
google: set-rand 1,000,000 ops in 555ms, 1,803,133/sec, 554 ns/op, 29.7 MB, 31 bytes/op
tidwall: set-rand 1,000,000 ops in 545ms, 1,835,818/sec, 544 ns/op, 29.6 MB, 31 bytes/op
tidwall: set-rand-hint 1,000,000 ops in 670ms, 1,493,473/sec, 669 ns/op, 29.6 MB, 31 bytes/op
tidwall: set-again 1,000,000 ops in 681ms, 1,469,038/sec, 680 ns/op
tidwall: set-after-copy 1,000,000 ops in 670ms, 1,493,230/sec, 669 ns/op
tidwall: load-rand 1,000,000 ops in 569ms, 1,756,187/sec, 569 ns/op, 29.6 MB, 31 bytes/op
** sequential get **
google: get-seq 1,000,000 ops in 165ms, 6,048,307/sec, 165 ns/op
tidwall: get-seq 1,000,000 ops in 144ms, 6,940,120/sec, 144 ns/op
tidwall: get-seq-hint 1,000,000 ops in 78ms, 12,815,243/sec, 78 ns/op
** random get **
google: get-rand 1,000,000 ops in 701ms, 1,427,507/sec, 700 ns/op
tidwall: get-rand 1,000,000 ops in 679ms, 1,473,531/sec, 678 ns/op
tidwall: get-rand-hint 1,000,000 ops in 824ms, 1,213,805/sec, 823 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.