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https://github.com/openacid/slim
Surprisingly space efficient trie in Golang(11 bits/key; 100 ns/get).
https://github.com/openacid/slim
compacted compress datastructure go golang memory tree trie
Last synced: 6 days ago
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Surprisingly space efficient trie in Golang(11 bits/key; 100 ns/get).
- Host: GitHub
- URL: https://github.com/openacid/slim
- Owner: openacid
- License: mit
- Created: 2019-02-02T06:12:39.000Z (almost 6 years ago)
- Default Branch: main
- Last Pushed: 2023-11-24T10:01:22.000Z (about 1 year ago)
- Last Synced: 2024-11-29T11:07:59.735Z (13 days ago)
- Topics: compacted, compress, datastructure, go, golang, memory, tree, trie
- Language: Go
- Homepage: https://openacid.github.io/
- Size: 18.1 MB
- Stars: 1,923
- Watchers: 39
- Forks: 105
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
Awesome Lists containing this project
- my-awesome - openacid/slim - 11 star:1.9k fork:0.1k Surprisingly space efficient trie in Golang(11 bits/key; 100 ns/get). (Go)
- go-awesome - Slim - Data structure with high space usage (Open source library / Data Structure)
README
# Slim - surprisingly space efficient data types in Golang
[](https://travis-ci.com/openacid/slim)
[](https://goreportcard.com/report/github.com/openacid/slim)
[](https://coveralls.io/github/openacid/slim?branch=main&service=github)
[](http://godoc.org/github.com/openacid/slim)
[](https://pkg.go.dev/github.com/openacid/slim)
[](https://sourcegraph.com/github.com/openacid/slim?badge)
Slim is collection of surprisingly space efficient data types, with
corresponding serialization APIs to persisting them on-disk or for transport.
- [Why slim](#why-slim)
- [Performance and memory overhead](#performance-and-memory-overhead)
- [Synopsis](#synopsis)
- [1. Index on-disk key-values](#1-index-on-disk-key-values)
- [2. Sparse index](#2-sparse-index)
- [3. Range scan](#3-range-scan)
- [Filter mode and KV mode.](#filter-mode-and-kv-mode)
- [Try it](#try-it)
- [Versions](#versions)
- [Who are using slim](#who-are-using-slim)
- [Feedback and contributions](#feedback-and-contributions)
- [Authors](#authors)
- [License](#license)
## Why slim
As data on internet keeps increasing exponentially,
the capacity gap between memory and disk becomes greater.
Most of the time, a data itself does not need to be loaded into expensive main memory.
Only the much more important information, WHERE-A-DATA-IS, deserve a seat in
main memory.
This is what `slim` does, keeps as little information as possible in main
memory, as a minimized index of huge amount external data.
- `SlimIndex`: is a common index structure, building on top of `SlimTrie`.
[](http://godoc.org/github.com/openacid/slim/index)
- `SlimTrie` is the underlying index data structure, evolved from [trie][].
[](http://godoc.org/github.com/openacid/slim/trie)
**Features**:
- **Minimized**:
**11 bits per key**(far less than an 64-bits pointer!!).
- **Stable**:
memory consumption is stable in various scenarios.
The Worst case converges to average consumption tightly.
See benchmark.
- **Loooong keys**:
You can have **VERY** long keys(`16K bytes`), without any waste of memory(and money).
Do not waste your life writing another prefix compression`:)`.
([aws-s3][] limits key length to 1024 bytes).
Memory consumption only relates to key count, **not to key length**.
- **Ordered**:
like [btree][], keys are stored.
Range-scan will be ready in `0.6.0`.
- **Fast**:
**~150 ns** per `Get()`.
Time complexity for a get is `O(log(n) + k); n: key count; k: key length`.
- **Ready for transport**:
a single `proto.Marshal()` is all it requires to serialize, transport or persisting on disk etc.
## Performance and memory overhead
- **3.3 times faster** than the [btree][].
- **2.3 times faster** than binary search.
- **Memory overhead is about 11 bit per key**.
The data struct in this benchmark is a slice of key-value pairs with a `SlimTrie` serving as the index.
The slim itself is built in the **filter** mode, to maximize memory reduction and performance.
The whole struct `slimKV` is a fully functional kv-store, just like a static `btree`.
```go
type slimKV struct {
slim *trie.SlimTrie
Elts []*KVElt
}
type KVElt struct {
Key string
Val int32
}
```
You can find the benchmark code in [benchmark](trie/benchmark/benchmark.go);
Read more about [Performance](docs/performance.md)
## Synopsis
### 1. Index on-disk key-values
One of the typical usages of slim is to index serialized data on disk(e.g., key value records in a SSTable).
By keeping a slim in memory, one can quickly find the on-disk offset of the record by a key.
Show me the code ......
```go
package index_test
import (
"fmt"
"strings"
"github.com/openacid/slim/index"
)
type Data string
func (d Data) Read(offset int64, key string) (string, bool) {
kv := strings.Split(string(d)[offset:], ",")[0:2]
if kv[0] == key {
return kv[1], true
}
return "", false
}
func Example() {
// Accelerate external data accessing (in memory or on disk) by indexing
// them with a SlimTrie:
// `data` is a sample of some unindexed data. In our example it is a comma
// separated key value series.
//
// In order to let SlimTrie be able to read data, `data` should have
// a `Read` method:
// Read(offset int64, key string) (string, bool)
data := Data("Aaron,1,Agatha,1,Al,2,Albert,3,Alexander,5,Alison,8")
// keyOffsets is a prebuilt index that stores key and its offset in data accordingly.
keyOffsets := []index.OffsetIndexItem{
{Key: "Aaron", Offset: 0},
{Key: "Agatha", Offset: 8},
{Key: "Al", Offset: 17},
{Key: "Albert", Offset: 22},
{Key: "Alexander", Offset: 31},
{Key: "Alison", Offset: 43},
}
// `SlimIndex` is simply a container of SlimTrie and its data.
st, err := index.NewSlimIndex(keyOffsets, data)
if err != nil {
fmt.Println(err)
}
// Lookup
v, found := st.Get("Alison")
fmt.Printf("key: %q\n found: %t\n value: %q\n", "Alison", found, v)
v, found = st.Get("foo")
fmt.Printf("key: %q\n found: %t\n value: %q\n", "foo", found, v)
// Output:
// key: "Alison"
// found: true
// value: "8"
// key: "foo"
// found: false
// value: ""
}
```
### 2. Sparse index
Create an index item for every 4(or more as you wish) keys.
Let several adjacent keys share one index item reduces a lot memory
cost if there are huge amount keys in external data.
Such as to index billions of 4KB objects on a 4TB disk(because one disk IO
costs 20ms for either reading 4KB or reading 1MB).
Show me the code ......
```go
package index_test
import (
"fmt"
"strings"
"github.com/openacid/slim/index"
)
type RangeData string
func (d RangeData) Read(offset int64, key string) (string, bool) {
for i := 0; i < 4; i++ {
if int(offset) >= len(d) {
break
}
kv := strings.Split(string(d)[offset:], ",")[0:2]
if kv[0] == key {
return kv[1], true
}
offset += int64(len(kv[0]) + len(kv[1]) + 2)
}
return "", false
}
func Example_indexRanges() {
// Index ranges instead of keys:
// In this example at most 4 keys shares one index item.
data := RangeData("Aaron,1,Agatha,1,Al,2,Albert,3,Alexander,5,Alison,8")
// keyOffsets is a prebuilt index that stores range start, range end and its offset.
keyOffsets := []index.OffsetIndexItem{
// Aaron +--> 0
// Agatha |
// Al |
// Albert |
// Alexander +--> 31
// Alison |
{Key: "Aaron", Offset: 0},
{Key: "Agatha", Offset: 0},
{Key: "Al", Offset: 0},
{Key: "Albert", Offset: 0},
{Key: "Alexander", Offset: 31},
{Key: "Alison", Offset: 31},
}
st, err := index.NewSlimIndex(keyOffsets, data)
if err != nil {
panic(err)
}
v, found := st.RangeGet("Aaron")
fmt.Printf("key: %q\n found: %t\n value: %q\n", "Aaron", found, v)
v, found = st.RangeGet("Al")
fmt.Printf("key: %q\n found: %t\n value: %q\n", "Al", found, v)
v, found = st.RangeGet("foo")
fmt.Printf("key: %q\n found: %t\n value: %q\n", "foo", found, v)
// Output:
// key: "Aaron"
// found: true
// value: "1"
// key: "Al"
// found: true
// value: "2"
// key: "foo"
// found: false
// value: ""
}
```
### 3. Range scan
Slim can also be used as a traditional in-memory kv-store:
Building a slim with `Opt{ Complete: Bool(true) }`,
it won't strip out any information(e.g., it won't eliminate single-branch labels)
and it will functions the same as a `btree`.
This snippet shows how to iterate key values.
Show me the code ......
```go
package trie
import (
"fmt"
"github.com/openacid/slim/encode"
)
func ExampleSlimTrie_ScanFrom() {
var keys = []string{
"",
"`",
"a",
"ab",
"abc",
"abca",
"abcd",
"abcd1",
"abce",
"be",
"c",
"cde0",
"d",
}
values := makeI32s(len(keys))
codec := encode.I32{}
st, _ := NewSlimTrie(codec, keys, values, Opt{
Complete: Bool(true),
})
// untilD stops when encountering "d".
untilD := func(k, v []byte) bool {
if string(k) == "d" {
return false
}
_, i32 := codec.Decode(v)
fmt.Println(string(k), i32)
return true
}
fmt.Println("scan (ab, +∞):")
st.ScanFrom("ab", false, true, untilD)
fmt.Println()
fmt.Println("scan [be, +∞):")
st.ScanFrom("be", true, true, untilD)
fmt.Println()
fmt.Println("scan (ab, be):")
st.ScanFromTo(
"ab", false,
"be", false,
true, untilD)
// Output:
//
// scan (ab, +∞):
// abc 4
// abca 5
// abcd 6
// abcd1 7
// abce 8
// be 9
// c 10
// cde0 11
//
// scan [be, +∞):
// be 9
// c 10
// cde0 11
//
// scan (ab, be):
// abc 4
// abca 5
// abcd 6
// abcd1 7
// abce 8
}
```
## Filter mode and KV mode.
Slim can be built into either a filter(like `bloom filter` but with key order preserved.) or a real kv-store(like `btree`)
There is an `option` in `NewSlimTrie(..., option)` to control the building behavior.
Ref: [Opt](https://pkg.go.dev/github.com/openacid/[email protected]/trie#Opt)
- To use slim as a kv-store, set the option to `Complete` then there won't be false positives.
- To use it as a filter, set `InnerPrefix`, `LeafPrefix` to false(`Complete` implies `InnerPrefix==true` and `LeafPrefix==true`).
Then slim won't store any single branch label in the trie it builds.
With `InnerPrefix==true`, it does not reduce a single label branch that leads to an inner node.
With `LeafPrefix==true`, it does not reduce a single label branch that leads to a leaf node.
E.g.:
```
// Complete
InnerPrefix: true
LeafPrefix: true
^ -a-> 1 -b-> $
`-c-> 2 -x-> 3 -y-> $
`-z-> $
InnerPrefix: true
LeafPrefix: false
^ -a-> $
`-c-> 2 -x-> 3 -y-> $
`-z-> $
InnerPrefix: false
LeafPrefix: true
^ -a-> 1 -b-> $
`-c-> 3 -y-> $
`-z-> $
InnerPrefix: false
LeafPrefix: false
^ -a-> $
`-c-> 3 -y-> $
`-z-> $
```
The memory consumption in filter mode and kv mode differs significantly.
The following chart shows memory consumption by 1 million var-length string, 10 to 20 byte in different mode:
| - | size | gzip-size |
| :-- | --: | --: |
| sample data size | 15.0M | 14.0M |
| Complete:true | 14.0M | 10.0M |
| InnerPrefix:ture | 1.3M | 0.9M |
| all false | 1.3M | 0.8M |
## Try it
**Install**
```sh
go get github.com/openacid/slim/trie
```
Change-log: [Change-log](docs/change-log.yaml)
### Versions
A newer version `y` being compatible with an older version `x` means `y` can
load data serialized by `x`. But `x` should never try to load data serialized by
a newer version `y`.
- `v0.5.*` is compatible with `0.2.*`, `0.3.*`, `0.4.*`, `0.5.*`.
- `v0.4.*` is compatible with `0.2.*`, `0.3.*`, `0.4.*`.
- `v0.3.*` is compatible with `0.2.*`, `0.3.*`.
- `v0.2.*` is compatible with `0.2.*`.
## Who are using slim
![][baishancloud-favicon] [baishancloud][]
## Feedback and contributions
**Feedback and Contributions are greatly appreciated**.
At this stage, the maintainers are most interested in feedback centered on:
- Do you have a real life scenario that `slim` supports well, or doesn't support at all?
- Do any of the APIs fulfill your needs well?
Let us know by filing an issue, describing what you did or wanted to do, what
you expected to happen, and what actually happened:
- [bug-report][]
- [improve-document][]
- [feature-request][]
Or other type of [issue][new-issue].
## Authors
- ![][刘保海-img-sml] **[刘保海][]** *marshaling*
- ![][吴义谱-img-sml] **[吴义谱][]** *array*
- ![][张炎泼-img-sml] **[张炎泼][]** *slimtrie design*
- ![][李文博-img-sml] **[李文博][]** *trie-compressing, trie-search*
- ![][李树龙-img-sml] **[李树龙][]** *marshaling*
See also the list of [contributors][] who participated in this project.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
[刘保海]: https://github.com/liubaohai
[吴义谱]: https://github.com/pengsven
[张炎泼]: https://github.com/drmingdrmer
[李文博]: https://github.com/wenbobuaa
[李树龙]: https://github.com/lishulong
[刘保海-img-sml]: https://avatars1.githubusercontent.com/u/26271283?s=36&v=4
[吴义谱-img-sml]: https://avatars3.githubusercontent.com/u/6927668?s=36&v=4
[张炎泼-img-sml]: https://avatars3.githubusercontent.com/u/44069?s=36&v=4
[李文博-img-sml]: https://avatars1.githubusercontent.com/u/11748387?s=36&v=4
[李树龙-img-sml]: https://avatars2.githubusercontent.com/u/13903162?s=36&v=4
[contributors]: https://github.com/openacid/slim/contributors
[dep]: https://github.com/golang/dep
[protobuf]: https://github.com/protocolbuffers/protobuf
[semver]: http://semver.org/
[protoc-install]: http://google.github.io/proto-lens/installing-protoc.html
[dep-install]: https://github.com/golang/dep#installation
[CONTRIBUTING.md]: CONTRIBUTING.md
[baishancloud]: http://www.baishancdnx.com
[baishancloud-favicon]: http://www.baishancdnx.com/public/favicon.ico
[golang-standards-project-layout]: https://github.com/golang-standards/project-layout
[bug-report]: https://github.com/openacid/slim/issues/new?labels=bug&template=bug_report.md
[improve-document]: https://github.com/openacid/slim/issues/new?labels=doc&template=doc_improve.md
[feature-request]: https://github.com/openacid/slim/issues/new?labels=feature&template=feature_request.md
[new-issue]: https://github.com/openacid/slim/issues/new/choose
[benchmark-get-png]: docs/trie/charts/bench_get_2019_06_03.png
[trie]: https://en.wikipedia.org/wiki/Trie
[btree]: https://github.com/google/btree
[aws-s3]: https://aws.amazon.com/s3/
[red-black-tree]: https://en.wikipedia.org/wiki/Red%E2%80%93black_tree