serpyco-rs 0.10.0

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serpyco-rs: a serializer for python dataclasses

What is serpyco-rs ?

Serpyco is a serialization library for Python 3.9+ dataclasses that works just by defining your dataclasses:

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:

$ pip install serpyco-rs

Features

• Serialization and deserialization of dataclasses
• Validation of input/output data
• Very fast
• Support recursive schemas
• Generate JSON Schema Specification (Draft 2020-12)
• Support custom encoders/decoders for fields

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)
• Literal[str, ...]
• Tagged unions (restricted)

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

Supported annotations

serpyco-rs supports changing load/dump behavior with typing.Annotated.

Currently available:

• Alias
• FiledFormat (CamelCase / NoFormat)
• NoneFormat (OmitNone / KeepNone)
• Discriminator
• Min / Max
• MinLength / MaxLength

Alias

Alias is needed to override the field name in the structure used for load / dump.

from dataclasses import dataclass
from typing import Annotated
from serpyco_rs import Serializer
from serpyco_rs.metadata import Alias

@dataclass
class A:
    foo: Annotated[int, Alias('bar')]

ser = Serializer(A)

print(ser.load({'bar': 1}))
>> A(foo=1)

print(ser.dump(A(foo=1)))
>> {'bar': 1}

FiledFormat

Used to have response bodies in camelCase while keeping your python code in snake_case.

from dataclasses import dataclass
from typing import Annotated
from serpyco_rs import Serializer
from serpyco_rs.metadata import CamelCase, NoFormat

@dataclass
class B:
    buz_filed: str

@dataclass
class A:
    foo_filed: int
    bar_filed: Annotated[B, NoFormat]

ser = Serializer(Annotated[A, CamelCase])  # or ser = Serializer(A, camelcase_fields=True)

print(ser.dump(A(foo_filed=1, bar_filed=B(buz_filed='123'))))
>> {'fooFiled': 1, 'barFiled': {'buz_filed': '123'}}

print(ser.load({'fooFiled': 1, 'barFiled': {'buz_filed': '123'}}))
>> A(foo_filed=1, bar_filed=B(buz_filed='123'))

NoneFormat

Via OmitNone we can drop None values for non required fields in the serialized dicts

from dataclasses import dataclass
from serpyco_rs import Serializer

@dataclass
class A:
    required_val: bool | None
    optional_val: bool | None = None

ser = Serializer(A, omit_none=True) # or Serializer(Annotated[A, OmitNone])

print(ser.dump(A(required_val=None, optional_val=None)))
>>> {'required_val': None}

Tagged unions

Supports tagged joins with discriminator field.

All classes in the union must be dataclasses or attrs with discriminator field Literal[str].

The discriminator field is always mandatory.

from typing import Annotated, Literal
from dataclasses import dataclass
from serpyco_rs import Serializer
from serpyco_rs.metadata import Discriminator

@dataclass
class Foo:
    type: Literal['foo']
    value: int

@dataclass(kw_only=True)
class Bar:
    type: Literal['bar'] = 'bar'
    value: str

ser = Serializer(list[Annotated[Foo | Bar, Discriminator('type')]])

print(ser.load([{'type': 'foo', 'value': 1}, {'type': 'bar', 'value': 'buz'}]))
>>> [Foo(type='foo', value=1), Bar(type='bar', value='buz')]

Min / Max

Supported for int / float / Decimal types and only for validation on load.

from typing import Annotated
from serpyco_rs import Serializer
from serpyco_rs.metadata import Min, Max

ser = Serializer(Annotated[int, Min(1), Max(10)])

ser.load(123)
>> SchemaValidationError: [ErrorItem(message='123 is greater than the maximum of 10', instance_path='', schema_path='maximum')]

MinLength / MaxLength

MinLength / MaxLength can be used to restrict the length of loaded strings.

from typing import Annotated
from serpyco_rs import Serializer
from serpyco_rs.metadata import MinLength

ser = Serializer(Annotated[str, MinLength(5)])

ser.load("1234")
>> SchemaValidationError: [ErrorItem(message='"1234" is shorter than 5 characters', instance_path='', schema_path='minLength')]

Getting JSON Schema

serpyco-rs can generate JSON Schema for your dataclasses (Draft 2020-12).

from dataclasses import dataclass
from serpyco_rs import Serializer

@dataclass
class A:
    """Description of A"""
    foo: int
    bar: str

ser = Serializer(A)

print(ser.get_json_schema())
>> {
    '$schema': 'https://json-schema.org/draft/2020-12/schema', 
    '$ref': '#/components/schemas/A[no_format,keep_nones]', 
    'components': {
        'schemas': {
            'A[no_format,keep_nones]': {
                'properties': {
                    'foo': {'type': 'integer'}, 
                    'bar': {'type': 'string'}
                }, 
                'required': ['foo', 'bar'], 
                'type': 'object', 
                'description': 'Description of A'
            }
        }
    }
}

Custom encoders for fields

You can provide CustomEncoder with serialize and deserialize functions, or serialize_with and deserialize_with annotations.

from typing import Annotated
from dataclasses import dataclass
from serpyco_rs import Serializer
from serpyco_rs.metadata import CustomEncoder

@dataclass
class Foo:
    val: Annotated[str, CustomEncoder[str, str](serialize=str.upper, deserialize=str.lower)]

ser = Serializer(Foo)
val = ser.dump(Foo(val='bar'))
>> {'val': 'BAR'}
assert ser.load(val) == Foo(val='bar') 

Note: CustomEncoder has no effect to validation and JSON Schema generation.