mirror of https://github.com/ergochat/ergo.git
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 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 1.15.3. 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 160ms, 6,262,097/sec, 159 ns/op, 31.0 MB, 32 bytes/op
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tidwall: set-seq 1,000,000 ops in 142ms, 7,020,721/sec, 142 ns/op, 36.6 MB, 38 bytes/op
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tidwall: set-seq-hint 1,000,000 ops in 87ms, 11,503,315/sec, 86 ns/op, 36.6 MB, 38 bytes/op
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tidwall: load-seq 1,000,000 ops in 37ms, 27,177,242/sec, 36 ns/op, 36.6 MB, 38 bytes/op
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go-arr: append 1,000,000 ops in 49ms, 20,574,760/sec, 48 ns/op
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** random set **
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google: set-rand 1,000,000 ops in 606ms, 1,649,921/sec, 606 ns/op, 21.5 MB, 22 bytes/op
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tidwall: set-rand 1,000,000 ops in 543ms, 1,841,590/sec, 543 ns/op, 26.7 MB, 27 bytes/op
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tidwall: set-rand-hint 1,000,000 ops in 573ms, 1,745,624/sec, 572 ns/op, 26.4 MB, 27 bytes/op
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tidwall: set-again 1,000,000 ops in 452ms, 2,212,581/sec, 451 ns/op, 27.1 MB, 28 bytes/op
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tidwall: set-after-copy 1,000,000 ops in 472ms, 2,117,457/sec, 472 ns/op, 27.9 MB, 29 bytes/op
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tidwall: load-rand 1,000,000 ops in 551ms, 1,816,498/sec, 550 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 133ms, 7,497,604/sec, 133 ns/op
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tidwall: get-seq 1,000,000 ops in 110ms, 9,082,972/sec, 110 ns/op
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tidwall: get-seq-hint 1,000,000 ops in 55ms, 18,289,945/sec, 54 ns/op
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** random get **
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google: get-rand 1,000,000 ops in 149ms, 6,704,337/sec, 149 ns/op
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tidwall: get-rand 1,000,000 ops in 131ms, 7,616,296/sec, 131 ns/op
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tidwall: get-rand-hint 1,000,000 ops in 216ms, 4,632,532/sec, 215 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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