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  • Language
    Assembly
  • License
    Apache License 2.0
  • Created over 3 years ago
  • Updated about 1 month ago

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Repository Details

A blazingly fast JSON serializing & deserializing library

Sonic

English | 中文

A blazingly fast JSON serializing & deserializing library, accelerated by JIT (just-in-time compiling) and SIMD (single-instruction-multiple-data).

Requirement

  • Go 1.16~1.21
  • Linux / MacOS / Windows(need go1.17 above)
  • Amd64 ARCH

Features

  • Runtime object binding without code generation
  • Complete APIs for JSON value manipulation
  • Fast, fast, fast!

Benchmarks

For all sizes of json and all scenarios of usage, Sonic performs best.

  • Medium (13KB, 300+ key, 6 layers)
goversion: 1.17.1
goos: darwin
goarch: amd64
cpu: Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz
BenchmarkEncoder_Generic_Sonic-16                      32393 ns/op         402.40 MB/s       11965 B/op          4 allocs/op
BenchmarkEncoder_Generic_Sonic_Fast-16                 21668 ns/op         601.57 MB/s       10940 B/op          4 allocs/op
BenchmarkEncoder_Generic_JsonIter-16                   42168 ns/op         309.12 MB/s       14345 B/op        115 allocs/op
BenchmarkEncoder_Generic_GoJson-16                     65189 ns/op         199.96 MB/s       23261 B/op         16 allocs/op
BenchmarkEncoder_Generic_StdLib-16                    106322 ns/op         122.60 MB/s       49136 B/op        789 allocs/op
BenchmarkEncoder_Binding_Sonic-16                       6269 ns/op        2079.26 MB/s       14173 B/op          4 allocs/op
BenchmarkEncoder_Binding_Sonic_Fast-16                  5281 ns/op        2468.16 MB/s       12322 B/op          4 allocs/op
BenchmarkEncoder_Binding_JsonIter-16                   20056 ns/op         649.93 MB/s        9488 B/op          2 allocs/op
BenchmarkEncoder_Binding_GoJson-16                      8311 ns/op        1568.32 MB/s        9481 B/op          1 allocs/op
BenchmarkEncoder_Binding_StdLib-16                     16448 ns/op         792.52 MB/s        9479 B/op          1 allocs/op
BenchmarkEncoder_Parallel_Generic_Sonic-16              6681 ns/op        1950.93 MB/s       12738 B/op          4 allocs/op
BenchmarkEncoder_Parallel_Generic_Sonic_Fast-16         4179 ns/op        3118.99 MB/s       10757 B/op          4 allocs/op
BenchmarkEncoder_Parallel_Generic_JsonIter-16           9861 ns/op        1321.84 MB/s       14362 B/op        115 allocs/op
BenchmarkEncoder_Parallel_Generic_GoJson-16            18850 ns/op         691.52 MB/s       23278 B/op         16 allocs/op
BenchmarkEncoder_Parallel_Generic_StdLib-16            45902 ns/op         283.97 MB/s       49174 B/op        789 allocs/op
BenchmarkEncoder_Parallel_Binding_Sonic-16              1480 ns/op        8810.09 MB/s       13049 B/op          4 allocs/op
BenchmarkEncoder_Parallel_Binding_Sonic_Fast-16         1209 ns/op        10785.23 MB/s      11546 B/op          4 allocs/op
BenchmarkEncoder_Parallel_Binding_JsonIter-16           6170 ns/op        2112.58 MB/s        9504 B/op          2 allocs/op
BenchmarkEncoder_Parallel_Binding_GoJson-16             3321 ns/op        3925.52 MB/s        9496 B/op          1 allocs/op
BenchmarkEncoder_Parallel_Binding_StdLib-16             3739 ns/op        3486.49 MB/s        9480 B/op          1 allocs/op

BenchmarkDecoder_Generic_Sonic-16                      66812 ns/op         195.10 MB/s       57602 B/op        723 allocs/op
BenchmarkDecoder_Generic_Sonic_Fast-16                 54523 ns/op         239.07 MB/s       49786 B/op        313 allocs/op
BenchmarkDecoder_Generic_StdLib-16                    124260 ns/op         104.90 MB/s       50869 B/op        772 allocs/op
BenchmarkDecoder_Generic_JsonIter-16                   91274 ns/op         142.81 MB/s       55782 B/op       1068 allocs/op
BenchmarkDecoder_Generic_GoJson-16                     88569 ns/op         147.17 MB/s       66367 B/op        973 allocs/op
BenchmarkDecoder_Binding_Sonic-16                      32557 ns/op         400.38 MB/s       28302 B/op        137 allocs/op
BenchmarkDecoder_Binding_Sonic_Fast-16                 28649 ns/op         455.00 MB/s       24999 B/op         34 allocs/op
BenchmarkDecoder_Binding_StdLib-16                    111437 ns/op         116.97 MB/s       10576 B/op        208 allocs/op
BenchmarkDecoder_Binding_JsonIter-16                   35090 ns/op         371.48 MB/s       14673 B/op        385 allocs/op
BenchmarkDecoder_Binding_GoJson-16                     28738 ns/op         453.59 MB/s       22039 B/op         49 allocs/op
BenchmarkDecoder_Parallel_Generic_Sonic-16             12321 ns/op        1057.91 MB/s       57233 B/op        723 allocs/op
BenchmarkDecoder_Parallel_Generic_Sonic_Fast-16        10644 ns/op        1224.64 MB/s       49362 B/op        313 allocs/op
BenchmarkDecoder_Parallel_Generic_StdLib-16            57587 ns/op         226.35 MB/s       50874 B/op        772 allocs/op
BenchmarkDecoder_Parallel_Generic_JsonIter-16          38666 ns/op         337.12 MB/s       55789 B/op       1068 allocs/op
BenchmarkDecoder_Parallel_Generic_GoJson-16            30259 ns/op         430.79 MB/s       66370 B/op        974 allocs/op
BenchmarkDecoder_Parallel_Binding_Sonic-16              5965 ns/op        2185.28 MB/s       27747 B/op        137 allocs/op
BenchmarkDecoder_Parallel_Binding_Sonic_Fast-16         5170 ns/op        2521.31 MB/s       24715 B/op         34 allocs/op
BenchmarkDecoder_Parallel_Binding_StdLib-16            27582 ns/op         472.58 MB/s       10576 B/op        208 allocs/op
BenchmarkDecoder_Parallel_Binding_JsonIter-16          13571 ns/op         960.51 MB/s       14685 B/op        385 allocs/op
BenchmarkDecoder_Parallel_Binding_GoJson-16            10031 ns/op        1299.51 MB/s       22111 B/op         49 allocs/op

BenchmarkGetOne_Sonic-16                                3276 ns/op        3975.78 MB/s          24 B/op          1 allocs/op
BenchmarkGetOne_Gjson-16                                9431 ns/op        1380.81 MB/s           0 B/op          0 allocs/op
BenchmarkGetOne_Jsoniter-16                            51178 ns/op         254.46 MB/s       27936 B/op        647 allocs/op
BenchmarkGetOne_Parallel_Sonic-16                      216.7 ns/op       60098.95 MB/s          24 B/op          1 allocs/op
BenchmarkGetOne_Parallel_Gjson-16                       1076 ns/op        12098.62 MB/s          0 B/op          0 allocs/op
BenchmarkGetOne_Parallel_Jsoniter-16                   17741 ns/op         734.06 MB/s       27945 B/op        647 allocs/op
BenchmarkSetOne_Sonic-16                               9571 ns/op         1360.61 MB/s        1584 B/op         17 allocs/op
BenchmarkSetOne_Sjson-16                               36456 ns/op         357.22 MB/s       52180 B/op          9 allocs/op
BenchmarkSetOne_Jsoniter-16                            79475 ns/op         163.86 MB/s       45862 B/op        964 allocs/op
BenchmarkSetOne_Parallel_Sonic-16                      850.9 ns/op       15305.31 MB/s        1584 B/op         17 allocs/op
BenchmarkSetOne_Parallel_Sjson-16                      18194 ns/op         715.77 MB/s       52247 B/op          9 allocs/op
BenchmarkSetOne_Parallel_Jsoniter-16                   33560 ns/op         388.05 MB/s       45892 B/op        964 allocs/op
BenchmarkLoadNode/LoadAll()-16                         11384 ns/op        1143.93 MB/s        6307 B/op         25 allocs/op
BenchmarkLoadNode_Parallel/LoadAll()-16                 5493 ns/op        2370.68 MB/s        7145 B/op         25 allocs/op
BenchmarkLoadNode/Interface()-16                       17722 ns/op         734.85 MB/s       13323 B/op         88 allocs/op
BenchmarkLoadNode_Parallel/Interface()-16              10330 ns/op        1260.70 MB/s       15178 B/op         88 allocs/op
  • Small (400B, 11 keys, 3 layers) small benchmarks
  • Large (635KB, 10000+ key, 6 layers) large benchmarks

See bench.sh for benchmark codes.

How it works

See INTRODUCTION.md.

Usage

Marshal/Unmarshal

Default behaviors are mostly consistent with encoding/json, except HTML escaping form (see Escape HTML) and SortKeys feature (optional support see Sort Keys) that is NOT in conformity to RFC8259.

import "github.com/bytedance/sonic"

var data YourSchema
// Marshal
output, err := sonic.Marshal(&data)
// Unmarshal
err := sonic.Unmarshal(output, &data)

Streaming IO

Sonic supports decoding json from io.Reader or encoding objects into io.Writer`, aims at handling multiple values as well as reducing memory consumption.

  • encoder
var o1 = map[string]interface{}{
    "a": "b",
}
var o2 = 1
var w = bytes.NewBuffer(nil)
var enc = sonic.ConfigDefault.NewEncoder(w)
enc.Encode(o1)
enc.Encode(o2)
fmt.Println(w.String())
// Output:
// {"a":"b"}
// 1
  • decoder
var o =  map[string]interface{}{}
var r = strings.NewReader(`{"a":"b"}{"1":"2"}`)
var dec = sonic.ConfigDefault.NewDecoder(r)
dec.Decode(&o)
dec.Decode(&o)
fmt.Printf("%+v", o)
// Output:
// map[1:2 a:b]

Use Number/Use Int64

import "github.com/bytedance/sonic/decoder"

var input = `1`
var data interface{}

// default float64
dc := decoder.NewDecoder(input)
dc.Decode(&data) // data == float64(1)
// use json.Number
dc = decoder.NewDecoder(input)
dc.UseNumber()
dc.Decode(&data) // data == json.Number("1")
// use int64
dc = decoder.NewDecoder(input)
dc.UseInt64()
dc.Decode(&data) // data == int64(1)

root, err := sonic.GetFromString(input)
// Get json.Number
jn := root.Number()
jm := root.InterfaceUseNumber().(json.Number) // jn == jm
// Get float64
fn := root.Float64()
fm := root.Interface().(float64) // jn == jm

Sort Keys

On account of the performance loss from sorting (roughly 10%), sonic doesn't enable this feature by default. If your component depends on it to work (like zstd), Use it like this:

import "github.com/bytedance/sonic"
import "github.com/bytedance/sonic/encoder"

// Binding map only
m := map[string]interface{}{}
v, err := encoder.Encode(m, encoder.SortMapKeys)

// Or ast.Node.SortKeys() before marshal
var root := sonic.Get(JSON)
err := root.SortKeys()

Escape HTML

On account of the performance loss (roughly 15%), sonic doesn't enable this feature by default. You can use encoder.EscapeHTML option to open this feature (align with encoding/json.HTMLEscape).

import "github.com/bytedance/sonic"

v := map[string]string{"&&":"<>"}
ret, err := Encode(v, EscapeHTML) // ret == `{"\u0026\u0026":{"X":"\u003c\u003e"}}`

Compact Format

Sonic encodes primitive objects (struct/map...) as compact-format JSON by default, except marshaling json.RawMessage or json.Marshaler: sonic ensures validating their output JSON but DONOT compacting them for performance concerns. We provide the option encoder.CompactMarshaler to add compacting process.

Print Error

If there invalid syntax in input JSON, sonic will return decoder.SyntaxError, which supports pretty-printing of error position

import "github.com/bytedance/sonic"
import "github.com/bytedance/sonic/decoder"

var data interface{}
err := sonic.UnmarshalString("[[[}]]", &data)
if err != nil {
    /* One line by default */
    println(e.Error()) // "Syntax error at index 3: invalid char\n\n\t[[[}]]\n\t...^..\n"
    /* Pretty print */
    if e, ok := err.(decoder.SyntaxError); ok {
        /*Syntax error at index 3: invalid char

            [[[}]]
            ...^..
        */
        print(e.Description())
    } else if me, ok := err.(*decoder.MismatchTypeError); ok {
        // decoder.MismatchTypeError is new to Sonic v1.6.0
        print(me.Description())
    }
}

Mismatched Types [Sonic v1.6.0]

If there a mismatch-typed value for a given key, sonic will report decoder.MismatchTypeError (if there are many, report the last one), but still skip wrong the value and keep decoding next JSON.

import "github.com/bytedance/sonic"
import "github.com/bytedance/sonic/decoder"

var data = struct{
    A int
    B int
}{}
err := UnmarshalString(`{"A":"1","B":1}`, &data)
println(err.Error())    // Mismatch type int with value string "at index 5: mismatched type with value\n\n\t{\"A\":\"1\",\"B\":1}\n\t.....^.........\n"
fmt.Printf("%+v", data) // {A:0 B:1}

Ast.Node

Sonic/ast.Node is a completely self-contained AST for JSON. It implements serialization and deserialization both and provides robust APIs for obtaining and modification of generic data.

Get/Index

Search partial JSON by given paths, which must be non-negative integer or string, or nil

import "github.com/bytedance/sonic"

input := []byte(`{"key1":[{},{"key2":{"key3":[1,2,3]}}]}`)

// no path, returns entire json
root, err := sonic.Get(input)
raw := root.Raw() // == string(input)

// multiple paths
root, err := sonic.Get(input, "key1", 1, "key2")
sub := root.Get("key3").Index(2).Int64() // == 3

Tip: since Index() uses offset to locate data, which is much faster than scanning like Get(), we suggest you use it as much as possible. And sonic also provides another API IndexOrGet() to underlying use offset as well as ensure the key is matched.

Set/Unset

Modify the json content by Set()/Unset()

import "github.com/bytedance/sonic"

// Set
exist, err := root.Set("key4", NewBool(true)) // exist == false
alias1 := root.Get("key4")
println(alias1.Valid()) // true
alias2 := root.Index(1)
println(alias1 == alias2) // true

// Unset
exist, err := root.UnsetByIndex(1) // exist == true
println(root.Get("key4").Check()) // "value not exist"

Serialize

To encode ast.Node as json, use MarshalJson() or json.Marshal() (MUST pass the node's pointer)

import (
    "encoding/json"
    "github.com/bytedance/sonic"
)

buf, err := root.MarshalJson()
println(string(buf))                // {"key1":[{},{"key2":{"key3":[1,2,3]}}]}
exp, err := json.Marshal(&root)     // WARN: use pointer
println(string(buf) == string(exp)) // true

APIs

  • validation: Check(), Error(), Valid(), Exist()
  • searching: Index(), Get(), IndexPair(), IndexOrGet(), GetByPath()
  • go-type casting: Int64(), Float64(), String(), Number(), Bool(), Map[UseNumber|UseNode](), Array[UseNumber|UseNode](), Interface[UseNumber|UseNode]()
  • go-type packing: NewRaw(), NewNumber(), NewNull(), NewBool(), NewString(), NewObject(), NewArray()
  • iteration: Values(), Properties(), ForEach(), SortKeys()
  • modification: Set(), SetByIndex(), Add()

Ast.Visitor

Sonic provides an advanced API for fully parsing JSON into non-standard types (neither struct not map[string]interface{}) without using any intermediate representation (ast.Node or interface{}). For example, you might have the following types which are like interface{} but actually not interface{}:

type UserNode interface {}

// the following types implement the UserNode interface.
type (
    UserNull    struct{}
    UserBool    struct{ Value bool }
    UserInt64   struct{ Value int64 }
    UserFloat64 struct{ Value float64 }
    UserString  struct{ Value string }
    UserObject  struct{ Value map[string]UserNode }
    UserArray   struct{ Value []UserNode }
)

Sonic provides the following API to return the preorder traversal of a JSON AST. The ast.Visitor is a SAX style interface which is used in some C++ JSON library. You should implement ast.Visitor by yourself and pass it to ast.Preorder() method. In your visitor you can make your custom types to represent JSON values. There may be an O(n) space container (such as stack) in your visitor to record the object / array hierarchy.

func Preorder(str string, visitor Visitor, opts *VisitorOptions) error

type Visitor interface {
    OnNull() error
    OnBool(v bool) error
    OnString(v string) error
    OnInt64(v int64, n json.Number) error
    OnFloat64(v float64, n json.Number) error
    OnObjectBegin(capacity int) error
    OnObjectKey(key string) error
    OnObjectEnd() error
    OnArrayBegin(capacity int) error
    OnArrayEnd() error
}

See ast/visitor.go for detailed usage. We also implement a demo visitor for UserNode in ast/visitor_test.go.

Compatibility

Sonic DOES NOT ensure to support all environments, due to the difficulty of developing high-performance codes. For developers who use sonic to build their applications in different environments, we have the following suggestions:

  • Developing on Mac M1: Make sure you have Rosetta 2 installed on your machine, and set GOARCH=amd64 when building your application. Rosetta 2 can automatically translate x86 binaries to arm64 binaries and run x86 applications on Mac M1.
  • Developing on Linux arm64: You can install qemu and use the qemu-x86_64 -cpu max command to convert x86 binaries to amr64 binaries for applications built with sonic. The qemu can achieve a similar transfer effect to Rosetta 2 on Mac M1.

For developers who want to use sonic on Linux arm64 without qemu, or those who want to handle JSON strictly consistent with encoding/json, we provide some compatible APIs as sonic.API

  • ConfigDefault: the sonic's default config (EscapeHTML=false,SortKeys=false...) to run on sonic-supporting environment. It will fall back to encoding/json with the corresponding config, and some options like SortKeys=false will be invalid.
  • ConfigStd: the std-compatible config (EscapeHTML=true,SortKeys=true...) to run on sonic-supporting environment. It will fall back to encoding/json.
  • ConfigFastest: the fastest config (NoQuoteTextMarshaler=true) to run on sonic-supporting environment. It will fall back to encoding/json with the corresponding config, and some options will be invalid.

Tips

Pretouch

Since Sonic uses golang-asm as a JIT assembler, which is NOT very suitable for runtime compiling, first-hit running of a huge schema may cause request-timeout or even process-OOM. For better stability, we advise using Pretouch() for huge-schema or compact-memory applications before Marshal()/Unmarshal().

import (
    "reflect"
    "github.com/bytedance/sonic"
    "github.com/bytedance/sonic/option"
)

func init() {
    var v HugeStruct

    // For most large types (nesting depth <= option.DefaultMaxInlineDepth)
    err := sonic.Pretouch(reflect.TypeOf(v))

    // with more CompileOption...
    err := sonic.Pretouch(reflect.TypeOf(v),
        // If the type is too deep nesting (nesting depth > option.DefaultMaxInlineDepth),
        // you can set compile recursive loops in Pretouch for better stability in JIT.
        option.WithCompileRecursiveDepth(loop),
        // For a large nested struct, try to set a smaller depth to reduce compiling time.
        option.WithCompileMaxInlineDepth(depth),
    )
}

Copy string

When decoding string values without any escaped characters, sonic references them from the origin JSON buffer instead of mallocing a new buffer to copy. This helps a lot for CPU performance but may leave the whole JSON buffer in memory as long as the decoded objects are being used. In practice, we found the extra memory introduced by referring JSON buffer is usually 20% ~ 80% of decoded objects. Once an application holds these objects for a long time (for example, cache the decoded objects for reusing), its in-use memory on the server may go up. We provide the option decoder.CopyString() for users to choose not to reference the JSON buffer, which may cause a decline in CPU performance to some degree.

Pass string or []byte?

For alignment to encoding/json, we provide API to pass []byte as an argument, but the string-to-bytes copy is conducted at the same time considering safety, which may lose performance when the origin JSON is huge. Therefore, you can use UnmarshalString() and GetFromString() to pass a string, as long as your origin data is a string or nocopy-cast is safe for your []byte. We also provide API MarshalString() for convenient nocopy-cast of encoded JSON []byte, which is safe since sonic's output bytes is always duplicated and unique.

Accelerate encoding.TextMarshaler

To ensure data security, sonic.Encoder quotes and escapes string values from encoding.TextMarshaler interfaces by default, which may degrade performance much if most of your data is in form of them. We provide encoder.NoQuoteTextMarshaler to skip these operations, which means you MUST ensure their output string escaped and quoted following RFC8259.

Better performance for generic data

In fully-parsed scenario, Unmarshal() performs better than Get()+Node.Interface(). But if you only have a part of the schema for specific json, you can combine Get() and Unmarshal() together:

import "github.com/bytedance/sonic"

node, err := sonic.GetFromString(_TwitterJson, "statuses", 3, "user")
var user User // your partial schema...
err = sonic.UnmarshalString(node.Raw(), &user)

Even if you don't have any schema, use ast.Node as the container of generic values instead of map or interface:

import "github.com/bytedance/sonic"

root, err := sonic.GetFromString(_TwitterJson)
user := root.GetByPath("statuses", 3, "user")  // === root.Get("status").Index(3).Get("user")
err = user.Check()

// err = user.LoadAll() // only call this when you want to use 'user' concurrently...
go someFunc(user)

Why? Because ast.Node stores its children using array:

  • Array's performance is much better than Map when Inserting (Deserialize) and Scanning (Serialize) data;
  • Hashing (map[x]) is not as efficient as Indexing (array[x]), which ast.Node can conduct on both array and object;
  • Using Interface()/Map() means Sonic must parse all the underlying values, while ast.Node can parse them on demand.

CAUTION: ast.Node DOESN'T ensure concurrent security directly, due to its lazy-load design. However, you can call Node.Load()/Node.LoadAll() to achieve that, which may bring performance reduction while it still works faster than converting to map or interface{}

Ast.Node or Ast.Visitor?

For generic data, ast.Node should be enough for your needs in most cases.

However, ast.Node is designed for partially processing JSON string. It has some special designs such as lazy-load which might not be suitable for directly parsing the whole JSON string like Unmarshal(). Although ast.Node is better then map or interface{}, it's also a kind of intermediate representation after all if your final types are customized and you have to convert the above types to your custom types after parsing.

For better performance, in previous case the ast.Visitor will be the better choice. It performs JSON decoding like Unmarshal() and you can directly use your final types to represents a JSON AST without any intermediate representations.

But ast.Visitor is not a very handy API. You might need to write a lot of code to implement your visitor and carefully maintain the tree hierarchy during decoding. Please read the comments in ast/visitor.go carefully if you decide to use this API.

Community

Sonic is a subproject of CloudWeGo. We are committed to building a cloud native ecosystem.

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iBOT 🤖: Image BERT Pre-Training with Online Tokenizer (ICLR 2022)
Jupyter Notebook
663
star
37

RealRichText

A Tricky Solution for Implementing Inline-Image-In-Text Feature in Flutter.
Dart
658
star
38

guide

A new feature guide component by react 🧭
TypeScript
651
star
39

mockey

a simple and easy-to-use golang mock library
Go
622
star
40

magic-microservices

Make Web Components easier and powerful!😘
TypeScript
570
star
41

Fastbot_iOS

About Fastbot(2.0) is a model-based testing tool for modeling GUI transitions to discover app stability problems
Objective-C
553
star
42

flow-builder

A highly customizable streaming flow builder.
TypeScript
526
star
43

MVDream-threestudio

3D generation code for MVDream
Python
473
star
44

effective_transformer

Running BERT without Padding
C++
457
star
45

ByteTransformer

optimized BERT transformer inference on NVIDIA GPU. https://arxiv.org/abs/2210.03052
C++
449
star
46

Next-ViT

Python
426
star
47

matxscript

A high-performance, extensible Python AOT compiler.
C++
408
star
48

byteir

A model compilation solution for various hardware
MLIR
362
star
49

syllepsis

Syllepsis is an out-of-the-box rich text editor.
TypeScript
355
star
50

uss

This is the PyTorch implementation of the Universal Source Separation with Weakly labelled Data.
Python
324
star
51

OMGD

Online Multi-Granularity Distillation for GAN Compression (ICCV2021)
Python
323
star
52

neurst

Neural end-to-end Speech Translation Toolkit
Python
298
star
53

danmu.js

HTML5 danmu (danmaku) plugin for any DOM element
JavaScript
292
star
54

vArmor

vArmor is a cloud native container sandbox system based on AppArmor/BPF/Seccomp. It also includes multiple built-in protection rules that are ready to use out of the box.
Go
263
star
55

particle-sfm

ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild. ECCV 2022.
C++
263
star
56

CloudShuffleService

Cloud Shuffle Service(CSS) is a general purpose remote shuffle solution for compute engines, including Spark/Flink/MapReduce.
Java
245
star
57

lynx-llm

paper: https://arxiv.org/abs/2307.02469 page: https://lynx-llm.github.io/
Python
227
star
58

g3

Enterprise-oriented Generic Proxy Solutions
Rust
227
star
59

xgplayer-vue

Vue component for xgplayer, a HTML5 video player with a parser that saves traffic
JavaScript
219
star
60

DEADiff

[CVPR 2024] Official implementation of "DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations"
Python
209
star
61

flux

A fast communication-overlapping library for tensor parallelism on GPUs.
C++
201
star
62

trace-irqoff

Interrupts-off or softirqs-off latency tracer
C
195
star
63

ParaGen

ParaGen is a PyTorch deep learning framework for parallel sequence generation.
Python
186
star
64

ByteMLPerf

AI Accelerator Benchmark focuses on evaluating AI Accelerators from a practical production perspective, including the ease of use and versatility of software and hardware.
Python
181
star
65

MoMA

MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation
Jupyter Notebook
177
star
66

AWERTL

An non-invasive iOS framework for quickly adapting Right-To-Left style UI
Objective-C
175
star
67

Bytedance-UnionAD

Ruby
170
star
68

keyhouse

Keyhouse is a skeleton of general-purpose Key Management System written in Rust.
Rust
163
star
69

react-model

The next generation state management library for React
TypeScript
162
star
70

LargeBatchCTR

Large batch training of CTR models based on DeepCTR with CowClip.
Python
162
star
71

ic_flow_platform

IFP (ic flow platform) is an integrated circuit design flow platform, mainly used for IC process specification management and data flow contral.
Python
154
star
72

DanmakuRenderEngine

DanmakuRenderEngine is a lightweight and scalable Android danmaku library. 轻量级高扩展安卓弹幕渲染引擎
Kotlin
149
star
73

primus

Java
148
star
74

diat

A CLI tool to help with diagnosing Node.js processes basing on inspector.
JavaScript
146
star
75

coconut_cvpr2024

Jupyter Notebook
143
star
76

Hammer

An efficient toolkit for training deep models.
Python
138
star
77

ns-x

An easy-to-use, flexible network simulator library in Go.
Go
116
star
78

pv3d

Python
113
star
79

fc-clip

This repo contains the code for our paper Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP
Python
109
star
80

RLFN

Winner of runtime track in NTIRE 2022 challenge on Efficient Super-Resolution
Python
106
star
81

DCFrame

DCFrame is a Swift UI collection framework, which can easily create complex UI.
Swift
100
star
82

trace-noschedule

Trace noschedule thread
C
99
star
83

decoupleQ

A quantization algorithm for LLM
Cuda
99
star
84

tar-wasm

A faster experimental wasm-based tar implementation for browsers.
Rust
95
star
85

TWIST

Official codes: Self-Supervised Learning by Estimating Twin Class Distribution
Python
95
star
86

magic-portal

⚡ A blazing fast micro-component and micro-frontend solution uses web-components under the hood.
TypeScript
91
star
87

xgplayer-react

React component for xgplayer, a HTML5 video player with a parser that saves traffic
JavaScript
84
star
88

fe-foundation

UI Foundation for React Hooks and Vue Composition Api
TypeScript
80
star
89

nnproxy

Scalable NameNode RPC Proxy for HDFS Federation
Java
79
star
90

dbatman

Go
74
star
91

Elkeid-HUB

Elkeid HUB is a rule/event processing engine maintained by the Elkeid Team that supports streaming/offline (not yet supported by the community edition) data processing. The original intention is to solve complex data/event processing and external system linkage requirements through standardized rules.
Python
74
star
92

FreeSeg

Python
69
star
93

pull_to_refresh

Flutter pull_to_refresh widget
Dart
67
star
94

Jeddak-DPSQL

DPSQL (Privacy Protection SQL Query Service) - This project is a microservice Middleware located between the database engine ( Hive , Clickhouse , etc.) and the application system. It provides transparent SQL query result desensitization capabilities.
Python
62
star
95

terark-zip

A data structure and algorithm library built for TerarkDB
C++
62
star
96

trace-runqlat

C
61
star
97

ipmb

An interprocess message bus system built in Rust.
Rust
60
star
98

X-Portrait

Source code for the SIGGRAPH 2024 paper "X-Portrait: Expressive Portrait Animation with Hierarchical Motion Attention"
Python
59
star
99

kernel

ByteDance kernel for use on cloud.
C
57
star
100

scroll_kit

Dart
56
star