Metadata-Version: 2.4
Name: ssrjson
Version: 0.0.2
Summary: A high-performance Python JSON library that fully leverages modern processor capabilities.
Author-email: Antares <antares0982@gmail.com>
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: LICENSE.orjson-APACHE
License-File: LICENSE.orjson.MIT
License-File: LICENSE.yyjson
Dynamic: license-file

# ssrJSON

A SIMD boosted high-performance and correct Python JSON library that fully leverages modern processor capabilities.

## Introduction

ssrJSON is a Python JSON library that leverages modern hardware capabilities to achieve peak performance, implemented primarily in C with some components written in C++. It offers a fully compatible interface to Python’s standard `json` module, making it a seamless drop-in replacement, while providing exceptional performance for JSON encoding and decoding.

### How Fast is ssrJSON?

TL;DR: ssrJSON is faster than or nearly as fast as [orjson](https://github.com/ijl/orjson) (which announces itself as the fastest Python library for JSON) on most benchmark cases.

`ssrjson.dumps()` is about 4x-26x as fast as `json.dumps()` (Python3.13, x86-64, AVX2). `ssrjson.loads()` is about 2x-8x as fast as `json.loads()` for `str` input and is about 2x-8x as fast as `json.loads()` for `bytes` input (Python3.13, x86-64, AVX2). ssrJSON also provides `ssrjson.dumps_to_bytes()`, which encode Python objects directly to `bytes` object using SIMD instructions, similar to `orjson.dumps` but without calling slow CPython functions to do the UTF-8 encoding. Typically, ssrJSON is capable of processing non-ASCII strings directly without invoking any slow CPython UTF-8 encoding and decoding interfaces, eliminating the need for intermediate representations. Furthermore, the underlying implementation leverages SIMD acceleration to optimize this process. Details of benchmarking can be found in the [benchmark repository](https://github.com/Nambers/ssrJSON-benchmark). If you wish to run the benchmark tests yourself, you can execute the following commands:

```bash
pip install ssrjson-benchmark
python -m ssrjson_benchmark
```

This will generate a PDF report of the results. If you choose to, you may submit this report to the benchmark repository, allowing others to view the performance metrics of ssrJSON on your device.

### Design Goal

The design goal of ssrJSON is to provide a straightforward and highly compatible approach to replace the inherently slower Python standard JSON encoding and decoding implementation with a significantly more efficient and high-performance alternative. If your module exclusively utilizes `dumps` and `loads`, you can replace the current JSON implementation by importing ssrJSON as `import ssrjson as json`. To facilitate this, ssrJSON maintains compatibility with the argument formats of `json.dumps` and `json.loads`; however, it does not guarantee identical results to the standard JSON module, as many features are either intentionally omitted or not yet supported. For further information, please refer to the section [Implementation Details](#implementation-details).

### Current Status

ssrJSON is currently operational, although some potentially useful features have yet to be implemented. The development of ssrJSON is still actively ongoing, and your code contributions are highly appreciated.

## How To Install

Pre-built wheels are available on PyPI.

```
pip install ssrjson
```

Note: ssrJSON requires at least SSE4.2 on x86-64 ([x86-64-v2](https://en.wikipedia.org/wiki/X86-64#Microarchitecture_levels:~:text=their%20encryption%20extensions.-,Microarchitecture%20levels,-%5Bedit%5D)). ssrJSON does not work with Python implementations other than CPython. Currently supported CPython versions are 3.9, 3.10, 3.11, 3.12, 3.13, 3.14. For Python 3.14, you need to build it from source.

### Build From Source

Since ssrJSON utilizes LLVM's vectorization extensions, it requires compilation with Clang and cannot be compiled in GCC or pure MSVC environments. On Windows, `clang-cl` can be used for this purpose. Build can be easily done by the following commands (make sure CMake, Clang and Python are already installed)

```bash
# On Linux:
# export CC=clang
# export CXX=clang++
mkdir build
cmake -S . -B build  # On Windows, configure with `cmake -T ClangCL`
cmake --build build
```

## Usage

### Basic

```python
>>> import ssrjson
>>> ssrjson.dumps({"key": "value"})
'{"key":"value"}'
>>> ssrjson.loads('{"key":"value"}')
{'key': 'value'}
>>> ssrjson.dumps_to_bytes({"key": "value"})
b'{"key":"value"}'
>>> ssrjson.loads(b'{"key":"value"}')
{'key': 'value'}
```

### Indent

ssrJSON only supports encoding with indent = 2, 4 or no indent (indent=0). When indent is used, a space is inserted between each key and value.

```python
>>> import ssrjson
>>> ssrjson.dumps({"a": "b", "c": {"d": True}, "e": [1, 2]})
'{"a":"b","c":{"d":true},"e":[1,2]}'
>>> print(ssrjson.dumps({"a": "b", "c": {"d": True}, "e": [1, 2]}, indent=2))
{
  "a": "b",
  "c": {
    "d": true
  },
  "e": [
    1,
    2
  ]
}
>>> print(ssrjson.dumps({"a": "b", "c": {"d": True}, "e": [1, 2]}, indent=4))
{
    "a": "b",
    "c": {
        "d": true
    },
    "e": [
        1,
        2
    ]
}
>>> ssrjson.dumps({"a": "b", "c": {"d": True}, "e": [1, 2]}, indent=3)
Traceback (most recent call last):
  File "<python-input>", line 1, in <module>
    ssrjson.dumps({"a": "b", "c": {"d": True}, "e": [1, 2]}, indent=3)
    ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: indent must be 0, 2, or 4
```

### Other Arguments Supported by Python's json

Arguments like `ensure_ascii`, `parse_float` provided by `json` can be recognized but ignored *by design*.

The functionality of `object_hook` in `json.loads` will be supported in future.

## Implementation Details

The implementations of ssrJSON's `dumps` and `loads` functions are designed to perform in-place processing as much as possible, avoiding intermediate representations. The `dumps` function employs SIMD instructions for rapid encoding in a single step. Similarly, `dumps_to_bytes` uses SIMD to efficiently handle both UTF-8 encoding and JSON serialization at the same time. With some modifications, the code used by `dumps_to_bytes` can also serve as a SIMD-accelerated replacement for `str.encode("utf-8")`.

The implementation of ssrJSON's `loads` draws inspiration from [yyjson](https://github.com/ibireme/yyjson), and also [orjson](https://github.com/ijl/orjson)'s caching algorithm for short dictionary keys. When the input type is `str`, `loads` avoids any UTF-8 encoding or decoding operations on non-ASCII strings. If the input is bytes, loads utilizes a modified string decoding algorithm based on yyjson. The main control flow and number decoding of `loads` are also modified from yyjson.

Generally, `ssrjson.dumps` behaves like `json.dumps` with `ensure_ascii=False`, and `ssrjson.loads` behaves like `json.loads`.

## Features

Below we explain some feature details of ssrJSON, which might be different from `json` module or other third-party JSON libraries.

### Strings

Code points within the range `[0xd800, 0xdfff]` cannot be represented in UTF-8 encoding, and the standard JSON specification typically prohibits the presence of such characters. However, since Python's `str` type is not encoded in UTF-8, ssrJSON aims to maintain compatibility with the Python json module's behavior, while other third-party Python JSON libraries may complain about this. In contrast, for the `dumps_to_bytes` function, which encodes output in UTF-8, the inclusion of these characters in the input is considered invalid.

```python
>>> s = chr(0xd800)
>>> (json.dumps(s, ensure_ascii=False) == '"' + s + '"', json.dumps(s, ensure_ascii=False))
(True, '"\ud800"')
>>> (ssrjson.dumps(s) == '"' + s + '"', ssrjson.dumps(s))
(True, '"\ud800"')
>>> ssrjson.dumps_to_bytes(s)
Traceback (most recent call last):
  File "<python-input>", line 1, in <module>
    ssrjson.dumps_to_bytes(s)
    ~~~~~~~~~~~~~~~~~~~~~~^^^
ssrjson.JSONEncodeError: Cannot encode unicode character in range [0xd800, 0xdfff] to utf-8
>>> json.loads(json.dumps(s, ensure_ascii=False)) == s
True
>>> ssrjson.loads(ssrjson.dumps(s)) == s
True
```

### Integers

`ssrjson.dumps` can only handle integers that can be expressed by either `uint64_t` or `int64_t` in C.

```python
>>> ssrjson.dumps(-(1<<63)-1)
Traceback (most recent call last):
  File "<python-input>", line 1, in <module>
    ssrjson.dumps(-(1<<63)-1)
    ~~~~~~~~~~~~~^^^^^^^^^^^^
ssrjson.JSONEncodeError: convert value to long long failed
>>> ssrjson.dumps(-(1<<63))
'-9223372036854775808'
>>> ssrjson.dumps((1<<64)-1)
'18446744073709551615'
>>> ssrjson.dumps(1<<64)
Traceback (most recent call last):
  File "<python-input>", line 1, in <module>
    ssrjson.dumps(1<<64)
    ~~~~~~~~~~~~~^^^^^^^
ssrjson.JSONEncodeError: convert value to unsigned long long failed
```

`ssrjson.loads` treats overflow integers as `float` objects.

```python
>>> ssrjson.loads('-9223372036854775809')  # -(1<<63)-1
-9.223372036854776e+18
>>> ssrjson.loads('-9223372036854775808')  # -(1<<63)
-9223372036854775808
>>> ssrjson.loads('18446744073709551615')  # (1<<64)-1
18446744073709551615
>>> ssrjson.loads('18446744073709551616')  # 1<<64
1.8446744073709552e+19
```

### Floats

For floating-point encoding, ssrJSON employs a slightly modified version of the [Dragonbox](https://github.com/jk-jeon/dragonbox) algorithm. Dragonbox is a highly efficient algorithm for converting floating-point to strings, typically producing output in scientific notation. ssrJSON has partially adapted this algorithm to enhance readability by outputting a more user-friendly format when no exponent is present.

Encoding and decoding `math.inf` are supported. `ssrjson.dumps` outputs the same result as `json.dumps`. The input of `ssrjson.loads` should be `"infinity"` with lower or upper cases (for each character), and cannot be `"inf"`.

```python
>>> json.dumps(math.inf)
'Infinity'
>>> ssrjson.dumps(math.inf)
'Infinity'
>>> ssrjson.loads("[infinity, Infinity, InFiNiTy, INFINITY]")
[inf, inf, inf, inf]
```

The case of `math.nan` is similar.

```python
>>> json.dumps(math.nan)
'NaN'
>>> ssrjson.dumps(math.nan)
'NaN'
>>> ssrjson.loads("[nan, Nan, NaN, NAN]")
[nan, nan, nan, nan]
```

## Limitations

Please note that ssrJSON is currently in the **beta stage** of development.

Several commonly used features are still under development, including serialization of subclasses of `str`, the `object_hook` functionality, and error location reporting during decoding. Additionally, ssrJSON will not support encoding or decoding of third-party data structures.

The ARM64 architecture is not yet supported but will be supported in the near future.

## Contributing

Contributions are welcome! Please open issues or submit pull requests for bug fixes, performance improvements, or new features. There will soon be a development documentation.

## License

This project is licensed under the MIT License. Licenses of other repositories are under [licenses](licenses) directory.

## Acknowledgments

We would like to express our gratitude to the outstanding libraries and their authors:

- [CPython](https://github.com/python/cpython)
- [yyjson](https://github.com/ibireme/yyjson): ssrJSON draws extensively from yyjson’s highly optimized implementations, including the core decoding logic, the decoding of bytes objects, and the number decoding routines.
- [orjson](https://github.com/ijl/orjson): ssrJSON references parts of orjson’s SIMD-based ASCII string encoding and decoding algorithms, as well as the dictionary key caching mechanism. Additionally, ssrJSON utilizes orjson’s pytest framework for testing purposes.
- [Dragonbox](https://github.com/jk-jeon/dragonbox): ssrJSON employs Dragonbox for high-performance floating-point encoding.
- [xxHash](https://github.com/Cyan4973/xxHash): ssrJSON leverages xxHash to efficiently compute hash values for key caching.

