Metadata-Version: 2.1
Name: serpyco-rs
Version: 0.2.2
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python
Classifier: Programming Language :: Rust
Requires-Dist: typing-inspect>=0.8.0
Requires-Dist: attributes-doc
Requires-Dist: jsonschema_rs
Requires-Dist: typing-extensions
Requires-Python: >=3.9
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM

# serpyco-rs: a serializer for python dataclasses
[![PyPI version](https://img.shields.io/pypi/v/serpyco-rs.svg)](https://pypi.org/project/serpyco-rs) [![Python
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## What is serpyco-rs ?


Serpyco is a serialization library for [Python 3.9+ dataclasses](https://docs.python.org/3/library/dataclasses.html) that works just by defining your dataclasses:

```python
import dataclasses
import serpyco_rs

@dataclasses.dataclass
class Example:
    name: str
    num: int
    tags: list[str]


serializer = serpyco_rs.Serializer(Example)

result = serializer.dump(Example(name="foo", num=2, tags=["hello", "world"]))
print(result)

>> {'name': 'foo', 'num': 2, 'tags': ['hello', 'world']}
```

serpyco-rs works by analysing the dataclass fields and can recognize many types : `list`, `tuple`, `Optional`... 
You can also embed other dataclasses in a definition.

The main use-case for serpyco-rs is to serialize objects for an API, but it can be helpful whenever you need to transform objects to/from builtin Python types.

## Installation
Use pip to install:

```bash
$ pip install serpyco-rs
```


## Features

- Serialization and unserialization of dataclasses
- Validation of input/output data
- Very fast
- Support recursive schemas

## Supported field types
There is support for generic types from the standard typing module:

* Decimal
* UUID
* Time
* Date
* DateTime
* Enum
* List
* Dict
* Mapping
* Sequence
* Tuple (fixed size)


## Benchmark

macOS Monterey / Apple M1 Pro / 16GB RAM / Python 3.11.0

#### dump

| Library     |   Median latency (milliseconds) |   Operations per second |   Relative (latency) |
|-------------|---------------------------------|-------------------------|----------------------|
| serpyco_rs  |                            0.05 |                 22188.2 |                 1    |
| serpyco     |                            0.05 |                 20878.5 |                 1.06 |
| mashumaro   |                            0.06 |                 15602.7 |                 1.42 |
| pydantic    |                            2.66 |                   375.6 |                59    |
| marshmallow |                            1.05 |                   951.7 |                23.33 |


#### load with validate

| Library     |   Median latency (milliseconds) |   Operations per second |   Relative (latency) |
|-------------|---------------------------------|-------------------------|----------------------|
| serpyco_rs  |                            0.23 |                  4400.1 |                 1    |
| serpyco     |                            0.28 |                  3546.4 |                 1.24 |
| mashumaro   |                            0.23 |                  4377.7 |                 1.01 |
| pydantic    |                            2.01 |                   497.3 |                 8.86 |
| marshmallow |                            4.55 |                   219.9 |                20.03 |


#### load (only serpyco and serpyco_rs supported load without validate)

| Library     |   Median latency (milliseconds) |   Operations per second |   Relative (latency) |
|-------------|---------------------------------|-------------------------|----------------------|
| serpyco_rs  |                            0.07 |                 13882.9 |                 1    |
| serpyco     |                            0.08 |                 12424.5 |                 1.12 |
| mashumaro   |                            0.23 |                  4382.9 |                 3.17 |
| pydantic    |                            2.02 |                   494.4 |                28.09 |
| marshmallow |                            4.59 |                   217.5 |                63.8  |

