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200 lines
5.5 KiB
Markdown
200 lines
5.5 KiB
Markdown
# btree
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[](https://godoc.org/github.com/tidwall/btree)
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An [efficient](#performance) [B-tree](https://en.wikipedia.org/wiki/B-tree) implementation in Go.
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## Features
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- `Copy()` method with copy-on-write support.
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- Fast bulk loading for pre-ordered data using the `Load()` method.
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- All operations are thread-safe.
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- [Path hinting](PATH_HINT.md) optimization for operations with nearby keys.
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## Installing
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To start using btree, install Go and run `go get`:
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```sh
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$ go get -u github.com/tidwall/btree
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```
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## Usage
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```go
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package main
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import (
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"fmt"
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"github.com/tidwall/btree"
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)
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type Item struct {
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Key, Val string
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}
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// byKeys is a comparison function that compares item keys and returns true
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// when a is less than b.
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func byKeys(a, b interface{}) bool {
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i1, i2 := a.(*Item), b.(*Item)
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return i1.Key < i2.Key
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}
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// byVals is a comparison function that compares item values and returns true
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// when a is less than b.
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func byVals(a, b interface{}) bool {
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i1, i2 := a.(*Item), b.(*Item)
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if i1.Val < i2.Val {
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return true
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}
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if i1.Val > i2.Val {
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return false
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}
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// Both vals are equal so we should fall though
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// and let the key comparison take over.
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return byKeys(a, b)
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}
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func main() {
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// Create a tree for keys and a tree for values.
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// The "keys" tree will be sorted on the Keys field.
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// The "values" tree will be sorted on the Values field.
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keys := btree.New(byKeys)
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vals := btree.New(byVals)
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// Create some items.
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users := []*Item{
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&Item{Key: "user:1", Val: "Jane"},
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&Item{Key: "user:2", Val: "Andy"},
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&Item{Key: "user:3", Val: "Steve"},
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&Item{Key: "user:4", Val: "Andrea"},
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&Item{Key: "user:5", Val: "Janet"},
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&Item{Key: "user:6", Val: "Andy"},
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}
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// Insert each user into both trees
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for _, user := range users {
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keys.Set(user)
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vals.Set(user)
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}
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// Iterate over each user in the key tree
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keys.Ascend(nil, func(item interface{}) bool {
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kvi := item.(*Item)
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fmt.Printf("%s %s\n", kvi.Key, kvi.Val)
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return true
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})
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fmt.Printf("\n")
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// Iterate over each user in the val tree
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vals.Ascend(nil, func(item interface{}) bool {
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kvi := item.(*Item)
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fmt.Printf("%s %s\n", kvi.Key, kvi.Val)
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return true
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})
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// Output:
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// user:1 Jane
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// user:2 Andy
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// user:3 Steve
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// user:4 Andrea
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// user:5 Janet
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// user:6 Andy
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//
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// user:4 Andrea
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// user:2 Andy
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// user:6 Andy
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// user:1 Jane
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// user:5 Janet
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// user:3 Steve
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}
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```
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## Operations
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### Basic
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```
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Len() # return the number of items in the btree
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Set(item) # insert or replace an existing item
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Get(item) # get an existing item
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Delete(item) # delete an item
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```
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### Iteration
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```
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Ascend(pivot, iter) # scan items in ascending order starting at pivot.
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Descend(pivot, iter) # scan items in descending order starting at pivot.
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```
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### Queues
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```
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Min() # return the first item in the btree
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Max() # return the last item in the btree
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PopMin() # remove and return the first item in the btree
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PopMax() # remove and return the last item in the btree
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```
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### Bulk loading
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```
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Load(item) # load presorted items into tree
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```
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### Path hints
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```
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SetHint(item, *hint) # insert or replace an existing item
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GetHint(item, *hint) # get an existing item
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DeleteHint(item, *hint) # delete an item
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```
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## Performance
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This implementation was designed with performance in mind.
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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.
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- `google`: The [google/btree](https://github.com/google/btree) package
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- `tidwall`: The [tidwall/btree](https://github.com/tidwall/btree) package
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- `go-arr`: Just a simple Go array
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```
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** sequential set **
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google: set-seq 1,000,000 ops in 163ms, 6,140,597/sec, 162 ns/op, 30.9 MB, 32 bytes/op
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tidwall: set-seq 1,000,000 ops in 141ms, 7,075,240/sec, 141 ns/op, 36.6 MB, 38 bytes/op
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tidwall: set-seq-hint 1,000,000 ops in 79ms, 12,673,902/sec, 78 ns/op, 36.6 MB, 38 bytes/op
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tidwall: load-seq 1,000,000 ops in 40ms, 24,887,293/sec, 40 ns/op, 36.6 MB, 38 bytes/op
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go-arr: append 1,000,000 ops in 51ms, 19,617,269/sec, 50 ns/op
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** random set **
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google: set-rand 1,000,000 ops in 666ms, 1,501,583/sec, 665 ns/op, 21.5 MB, 22 bytes/op
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tidwall: set-rand 1,000,000 ops in 569ms, 1,756,845/sec, 569 ns/op, 26.7 MB, 27 bytes/op
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tidwall: set-rand-hint 1,000,000 ops in 670ms, 1,491,637/sec, 670 ns/op, 26.4 MB, 27 bytes/op
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tidwall: set-again 1,000,000 ops in 488ms, 2,050,667/sec, 487 ns/op, 27.1 MB, 28 bytes/op
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tidwall: set-after-copy 1,000,000 ops in 494ms, 2,022,980/sec, 494 ns/op, 27.9 MB, 29 bytes/op
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tidwall: load-rand 1,000,000 ops in 594ms, 1,682,937/sec, 594 ns/op, 26.1 MB, 27 bytes/op
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** sequential get **
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google: get-seq 1,000,000 ops in 141ms, 7,078,690/sec, 141 ns/op
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tidwall: get-seq 1,000,000 ops in 124ms, 8,075,925/sec, 123 ns/op
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tidwall: get-seq-hint 1,000,000 ops in 40ms, 25,142,979/sec, 39 ns/op
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** random get **
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google: get-rand 1,000,000 ops in 152ms, 6,593,518/sec, 151 ns/op
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tidwall: get-rand 1,000,000 ops in 128ms, 7,783,293/sec, 128 ns/op
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tidwall: get-rand-hint 1,000,000 ops in 135ms, 7,403,823/sec, 135 ns/op
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```
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*You can find the benchmark utility at [tidwall/btree-benchmark](https://github.com/tidwall/btree-benchmark)*
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## Contact
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Josh Baker [@tidwall](http://twitter.com/tidwall)
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## License
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Source code is available under the MIT [License](/LICENSE).
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