mashumaro

Fast and well tested serialization library

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Project description

Fast and well tested serialization library

In Python, you often need to dump and load objects based on the schema you have. It can be a dataclass model, a list of third-party generic classes or whatever. Mashumaro not only lets you save and load things in different ways, but it also does it super quick.

Key features

🚀 One of the fastest libraries
☝️ Mature and time-tested
👶 Easy to use out of the box
⚙️ Highly customizable
🎉 Built-in support for JSON, YAML, TOML, MessagePack
📦 Built-in support for almost all Python types including typing-extensions
📝 JSON Schema generation

Table of contents
Introduction
Installation
Changelog
Supported data types
Usage example
How does it work?
Benchmark
Supported serialization formats
- Basic form
- JSON
  - json library
  - orjson library
- YAML
- TOML
- MessagePack
Customization
JSON Schema

Introduction

This library provides two fundamentally different approaches to converting your data to and from various formats. Each of them is useful in different situations:

Codecs
Mixins

Codecs are represented by a set of decoder / encoder classes and decode / encode functions for each supported format. You can use them to convert data of any python built-in and third-party type to JSON, YAML, TOML, MessagePack or a basic form accepted by other serialization formats. For example, you can convert a list of datetime objects to JSON array containing string-represented datetimes and vice versa.

Mixins are primarily for dataclass models. They are represented by mixin classes that add methods for converting to and from JSON, YAML, TOML, MessagePack or a basic form accepted by other serialization formats. If you have a root dataclass model, then it will be the easiest way to make it serializable. All you have to do is inherit a particular mixin class.

In addition to serialization functionality, this library also provides JSON Schema builder that can be used in places where interoperability matters.

Installation

Use pip to install:

$ pip install mashumaro

The current version of mashumaro supports Python versions 3.9 — 3.13.

It's not recommended to use any version of Python that has reached its end of life and is no longer receiving security updates or bug fixes from the Python development team. For convenience, there is a table below that outlines the last version of mashumaro that can be installed on unmaintained versions of Python.

Python Version	Last Version of mashumaro	Python EOL
3.8	3.14	2024-10-07
3.7	3.9.1	2023-06-27
3.6	3.1.1	2021-12-23

Changelog

This project follows the principles of Semantic Versioning. Changelog is available on GitHub Releases page.

Supported data types

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

for standard generic types on PEP 585 compatible Python (3.9+):

for special primitives from the typing module:

for standard interpreter types from types module:

for enumerations based on classes from the standard enum module:

for common built-in types:

for built-in datetime oriented types (see more details):

for pathlike types:

for other less popular built-in types:

for backported types from typing-extensions:

for arbitrary types:

user-defined types
third-party types
user-defined generic types
third-party generic types

Usage example

Suppose we're developing a financial application and we operate with currencies and stocks:

from dataclasses import dataclass
from enum import Enum

class Currency(Enum):
    USD = "USD"
    EUR = "EUR"

@dataclass
class CurrencyPosition:
    currency: Currency
    balance: float

@dataclass
class StockPosition:
    ticker: str
    name: str
    balance: int

Now we want a dataclass for portfolio that will be serialized to and from JSON. We inherit DataClassJSONMixin that adds this functionality:

from mashumaro.mixins.json import DataClassJSONMixin

...

@dataclass
class Portfolio(DataClassJSONMixin):
    currencies: list[CurrencyPosition]
    stocks: list[StockPosition]

Let's create a portfolio instance and check methods from_json and to_json:

portfolio = Portfolio(
    currencies=[
        CurrencyPosition(Currency.USD, 238.67),
        CurrencyPosition(Currency.EUR, 361.84),
    ],
    stocks=[
        StockPosition("AAPL", "Apple", 10),
        StockPosition("AMZN", "Amazon", 10),
    ]
)

portfolio_json = portfolio.to_json()
assert Portfolio.from_json(portfolio_json) == portfolio

If we need to serialize something different from a root dataclass, we can use codecs. In the following example we create a JSON decoder and encoder for a list of currencies:

from mashumaro.codecs.json import JSONDecoder, JSONEncoder

...

decoder = JSONDecoder(list[CurrencyPosition])
encoder = JSONEncoder(list[CurrencyPosition])

currencies = [
    CurrencyPosition(Currency.USD, 238.67),
    CurrencyPosition(Currency.EUR, 361.84),
]
currencies_json = encoder.encode(currencies)
assert decoder.decode(currencies_json) == currencies

How does it work?

This library works by taking the schema of the data and generating a specific decoder and encoder for exactly that schema, taking into account the specifics of serialization format. This is much faster than inspection of data types on every call of decoding or encoding at runtime.

These specific decoders and encoders are generated by codecs and mixins:

When using codecs, these methods are compiled during the creation of the decoder or encoder.
When using serialization mixins, these methods are compiled during import time (or at runtime in some cases) and are set as attributes to your dataclasses. To minimize the import time, you can explicitly enable lazy compilation.

Benchmark

macOS 15.1 Sequoia
Apple M3 Max
36GB RAM
Python 3.13.0

Benchmark using pyperf with GitHub Issue model. Please note that the following charts use logarithmic scale, as it is convenient for displaying very large ranges of values.

[!NOTE]
Benchmark results may vary depending on the specific configuration and parameters used for serialization and deserialization. However, we have made an attempt to use the available options that can speed up and smooth out the differences in how libraries work.

To run benchmark in your environment:

git clone git@github.com:Fatal1ty/mashumaro.git
cd mashumaro
python3 -m venv env && source env/bin/activate
pip install -e .
pip install -r requirements-dev.txt
./benchmark/run.sh

Supported serialization formats

This library has built-in support for multiple popular formats:

There are preconfigured codecs and mixin classes. However, you're free to override some settings if necessary.

[!IMPORTANT]
As for codecs, you are offered to choose between convenience and efficiency. When you need to decode or encode typed data more than once, it's highly recommended to create and reuse a decoder or encoder specifically for that data type. For one-time use with default settings it may be convenient to use global functions that create a disposable decoder or encoder under the hood. Remember that you should not use these convenient global functions more that once for the same data type if performance is important to you.

Basic form

Basic form denotes a python object consisting only of basic data types supported by most serialization formats. These types are: str, int, float, bool, list, dict.

This is also a starting point you can play with for a comprehensive transformation of your data.

Efficient decoder and encoder can be used as follows:

from mashumaro.codecs import BasicDecoder, BasicEncoder
# or from mashumaro.codecs.basic import BasicDecoder, BasicEncoder

decoder = BasicDecoder(<shape_type>, ...)
decoder.decode(...)

encoder = BasicEncoder(<shape_type>, ...)
encoder.encode(...)

Convenient functions are recommended to be used as follows:

import mashumaro.codecs.basic as basic_codec

basic_codec.decode(..., <shape_type>)
basic_codec.encode(..., <shape_type>)

Mixin can be used as follows:

from mashumaro import DataClassDictMixin
# or from mashumaro.mixins.dict import DataClassDictMixin

@dataclass
class MyModel(DataClassDictMixin):
    ...

MyModel.from_dict(...)
MyModel(...).to_dict()

[!TIP]
You don't need to inherit DataClassDictMixin along with other serialization mixins because it's a base class for them.

JSON

JSON is a lightweight data-interchange format. You can choose between standard library json for compatibility and third-party dependency orjson for better performance.

json library

Efficient decoder and encoder can be used as follows:

from mashumaro.codecs.json import JSONDecoder, JSONEncoder

decoder = JSONDecoder(<shape_type>, ...)
decoder.decode(...)

encoder = JSONEncoder(<shape_type>, ...)
encoder.encode(...)

Convenient functions can be used as follows:

from mashumaro.codecs.json import json_decode, json_encode

json_decode(..., <shape_type>)
json_encode(..., <shape_type>)

Convenient function aliases are recommended to be used as follows:

import mashumaro.codecs.json as json_codec

json_codec.decode(...<shape_type>)
json_codec.encode(..., <shape_type>)

Mixin can be used as follows:

from mashumaro.mixins.json import DataClassJSONMixin

@dataclass
class MyModel(DataClassJSONMixin):
    ...

MyModel.from_json(...)
MyModel(...).to_json()

orjson library

In order to use orjson library, it must be installed manually or using an extra option for mashumaro:

pip install mashumaro[orjson]

The following data types will be handled by orjson library by default:

Efficient decoder and encoder can be used as follows:

from mashumaro.codecs.orjson import ORJSONDecoder, ORJSONEncoder

decoder = ORJSONDecoder(<shape_type>, ...)
decoder.decode(...)

encoder = ORJSONEncoder(<shape_type>, ...)
encoder.encode(...)

Convenient functions can be used as follows:

from mashumaro.codecs.orjson import json_decode, json_encode

json_decode(..., <shape_type>)
json_encode(..., <shape_type>)

Convenient function aliases are recommended to be used as follows:

import mashumaro.codecs.orjson as json_codec

json_codec.decode(...<shape_type>)
json_codec.encode(..., <shape_type>)

Mixin can be used as follows:

from mashumaro.mixins.orjson import DataClassORJSONMixin

@dataclass
class MyModel(DataClassORJSONMixin):
    ...

MyModel.from_json(...)
MyModel(...).to_json()
MyModel(...).to_jsonb()

YAML

YAML is a human-friendly data serialization language for all programming languages. In order to use this format, the pyyaml package must be installed. You can install it manually or using an extra option for mashumaro:

pip install mashumaro[yaml]

Efficient decoder and encoder can be used as follows:

from mashumaro.codecs.yaml import YAMLDecoder, YAMLEncoder

decoder = YAMLDecoder(<shape_type>, ...)
decoder.decode(...)

encoder = YAMLEncoder(<shape_type>, ...)
encoder.encode(...)

Convenient functions can be used as follows:

from mashumaro.codecs.yaml import yaml_decode, yaml_encode

yaml_decode(..., <shape_type>)
yaml_encode(..., <shape_type>)

Convenient function aliases are recommended to be used as follows:

import mashumaro.codecs.yaml as yaml_codec

yaml_codec.decode(...<shape_type>)
yaml_codec.encode(..., <shape_type>)

Mixin can be used as follows:

from mashumaro.mixins.yaml import DataClassYAMLMixin

@dataclass
class MyModel(DataClassYAMLMixin):
    ...

MyModel.from_yaml(...)
MyModel(...).to_yaml()

TOML

TOML is config file format for humans. In order to use this format, the tomli and tomli-w packages must be installed. In Python 3.11+, tomli is included as tomlib standard library module and is used for this format. You can install the missing packages manually or using an extra option for mashumaro:

pip install mashumaro[toml]

The following data types will be handled by tomli/ tomli-w library by default:

Fields with value None will be omitted on serialization because TOML doesn't support null values.

Efficient decoder and encoder can be used as follows:

from mashumaro.codecs.toml import TOMLDecoder, TOMLEncoder

decoder = TOMLDecoder(<shape_type>, ...)
decoder.decode(...)

encoder = TOMLEncoder(<shape_type>, ...)
encoder.encode(...)

Convenient functions can be used as follows:

from mashumaro.codecs.toml import toml_decode, toml_encode

toml_decode(..., <shape_type>)
toml_encode(..., <shape_type>)

Convenient function aliases are recommended to be used as follows:

import mashumaro.codecs.toml as toml_codec

toml_codec.decode(...<shape_type>)
toml_codec.encode(..., <shape_type>)

Mixin can be used as follows:

from mashumaro.mixins.toml import DataClassTOMLMixin

@dataclass
class MyModel(DataClassTOMLMixin):
    ...

MyModel.from_toml(...)
MyModel(...).to_toml()

MessagePack

MessagePack is an efficient binary serialization format. In order to use this mixin, the msgpack package must be installed. You can install it manually or using an extra option for mashumaro:

pip install mashumaro[msgpack]

The following data types will be handled by msgpack library by default:

Efficient decoder and encoder can be used as follows:

from mashumaro.codecs.msgpack import MessagePackDecoder, MessagePackEncoder

decoder = MessagePackDecoder(<shape_type>, ...)
decoder.decode(...)

encoder = MessagePackEncoder(<shape_type>, ...)
encoder.encode(...)

Convenient functions can be used as follows:

from mashumaro.codecs.msgpack import msgpack_decode, msgpack_encode

msgpack_decode(..., <shape_type>)
msgpack_encode(..., <shape_type>)

Convenient function aliases are recommended to be used as follows:

import mashumaro.codecs.msgpack as msgpack_codec

msgpack_codec.decode(...<shape_type>)
msgpack_codec.encode(..., <shape_type>)

Mixin can be used as follows:

from mashumaro.mixins.msgpack import DataClassMessagePackMixin

@dataclass
class MyModel(DataClassMessagePackMixin):
    ...

MyModel.from_msgpack(...)
MyModel(...).to_msgpack()

Customization

Customization options of mashumaro are extensive and will most likely cover your needs. When it comes to non-standard data types and non-standard serialization support, you can do the following:

Turn an existing regular or generic class into a serializable one by inheriting the SerializableType class
Write different serialization strategies for an existing regular or generic type that is not under your control using SerializationStrategy class
Define serialization / deserialization methods:
- for a specific dataclass field by using field options
- for a specific data type used in the dataclass by using Config class
Alter input and output data with serialization / deserialization hooks
Separate serialization scheme from a dataclass in a reusable manner using dialects
Choose from predefined serialization engines for the specific data types, e.g. datetime and NamedTuple

SerializableType interface

If you have a custom class or hierarchy of classes whose instances you want to serialize with mashumaro, the first option is to implement SerializableType interface.

User-defined types

Let's look at this not very practicable example:

from dataclasses import dataclass
from mashumaro import DataClassDictMixin
from mashumaro.types import SerializableType

class Airport(SerializableType):
    def __init__(self, code, city):
        self.code, self.city = code, city

    def _serialize(self):
        return [self.code, self.city]

    @classmethod
    def _deserialize(cls, value):
        return cls(*value)

    def __eq__(self, other):
        return self.code, self.city == other.code, other.city

@dataclass
class Flight(DataClassDictMixin):
    origin: Airport
    destination: Airport

JFK = Airport("JFK", "New York City")
LAX = Airport("LAX", "Los Angeles")

input_data = {
    "origin": ["JFK", "New York City"],
    "destination": ["LAX", "Los Angeles"]
}
my_flight = Flight.from_dict(input_data)
assert my_flight == Flight(JFK, LAX)
assert my_flight.to_dict() == input_data

You can see how Airport instances are seamlessly created from lists of two strings and serialized into them.

By default _deserialize method will get raw input data without any transformations before. This should be enough in many cases, especially when you need to perform non-standard transformations yourself, but let's extend our example:

class Itinerary(SerializableType):
    def __init__(self, flights):
        self.flights = flights

    def _serialize(self):
        return self.flights

    @classmethod
    def _deserialize(cls, flights):
        return cls(flights)

@dataclass
class TravelPlan(DataClassDictMixin):
    budget: float
    itinerary: Itinerary

input_data = {
    "budget": 10_000,
    "itinerary": [
        {
            "origin": ["JFK", "New York City"],
            "destination": ["LAX", "Los Angeles"]
        },
        {
            "origin": ["LAX", "Los Angeles"],
            "destination": ["SFO", "San Fransisco"]
        }
    ]
}

If we pass the flight list as is into Itinerary._deserialize, our itinerary will have something that we may not expect — list[dict] instead of list[Flight]. The solution is quite simple. Instead of calling Flight._deserialize yourself, just use annotations:

class Itinerary(SerializableType, use_annotations=True):
    def __init__(self, flights):
        self.flights = flights

    def _serialize(self) -> list[Flight]:
        return self.flights

    @classmethod
    def _deserialize(cls, flights: list[Flight]):
        return cls(flights)

my_plan = TravelPlan.from_dict(input_data)
assert isinstance(my_plan.itinerary.flights[0], Flight)
assert isinstance(my_plan.itinerary.flights[1], Flight)
assert my_plan.to_dict() == input_data

Here we add annotations to the only argument of _deserialize method and to the return value of _serialize method as well. The latter is needed for correct serialization.

[!IMPORTANT]
The importance of explicit passing use_annotations=True when defining a class is that otherwise implicit using annotations might break compatibility with old code that wasn't aware of this feature. It will be enabled by default in the future major release.

User-defined generic types

The great thing to note about using annotations in SerializableType is that they work seamlessly with generic and variadic generic types. Let's see how this can be useful:

from datetime import date
from typing import TypeVar
from dataclasses import dataclass
from mashumaro import DataClassDictMixin
from mashumaro.types import SerializableType

KT = TypeVar("KT")
VT = TypeVar("VT")

class DictWrapper(dict[KT, VT], SerializableType, use_annotations=True):
    def _serialize(self) -> dict[KT, VT]:
        return dict(self)

    @classmethod
    def _deserialize(cls, value: dict[KT, VT]) -> 'DictWrapper[KT, VT]':
        return cls(value)

@dataclass
class DataClass(DataClassDictMixin):
    x: DictWrapper[date, str]
    y: DictWrapper[str, date]

input_data = {
    "x": {"2022-12-07": "2022-12-07"},
    "y": {"2022-12-07": "2022-12-07"}
}
obj = DataClass.from_dict(input_data)
assert obj == DataClass(
    x=DictWrapper({date(2022, 12, 7): "2022-12-07"}),
    y=DictWrapper({"2022-12-07": date(2022, 12, 7)})
)
assert obj.to_dict() == input_data

You can see that formatted date is deserialized to date object before passing to DictWrapper._deserialize in a key or value according to the generic parameters.

If you have generic dataclass types, you can use SerializableType for them as well, but it's not necessary since they're supported out of the box.

SerializationStrategy

If you want to add support for a custom third-party type that is not under your control, you can write serialization and deserialization logic inside SerializationStrategy class, which will be reusable and so well suited in case that third-party type is widely used. SerializationStrategy is also good if you want to create strategies that are slightly different from each other, because you can add the strategy differentiator in the __init__ method.

Third-party types

To demonstrate how SerializationStrategy works let's write a simple strategy for datetime serialization in different formats. In this example we will use the same strategy class for two dataclass fields, but a string representing the date and time will be different.

from dataclasses import dataclass, field
from datetime import datetime
from mashumaro import DataClassDictMixin, field_options
from mashumaro.types import SerializationStrategy

class FormattedDateTime(SerializationStrategy):
    def __init__(self, fmt):
        self.fmt = fmt

    def serialize(self, value: datetime) -> str:
        return value.strftime(self.fmt)

    def deserialize(self, value: str) -> datetime:
        return datetime.strptime(value, self.fmt)

@dataclass
class DateTimeFormats(DataClassDictMixin):
    short: datetime = field(
        metadata=field_options(
            serialization_strategy=FormattedDateTime("%d%m%Y%H%M%S")
        )
    )
    verbose: datetime = field(
        metadata=field_options(
            serialization_strategy=FormattedDateTime("%A %B %d, %Y, %H:%M:%S")
        )
    )

formats = DateTimeFormats(
    short=datetime(2019, 1, 1, 12),
    verbose=datetime(2019, 1, 1, 12),
)
dictionary = formats.to_dict()
# {'short': '01012019120000', 'verbose': 'Tuesday January 01, 2019, 12:00:00'}
assert DateTimeFormats.from_dict(dictionary) == formats

Similarly to SerializableType, SerializationStrategy could also take advantage of annotations:

from dataclasses import dataclass
from datetime import datetime
from mashumaro import DataClassDictMixin
from mashumaro.types import SerializationStrategy

class TsSerializationStrategy(SerializationStrategy, use_annotations=True):
    def serialize(self, value: datetime) -> float:
        return value.timestamp()

    def deserialize(self, value: float) -> datetime:
        # value will be converted to float before being passed to this method
        return datetime.fromtimestamp(value)

@dataclass
class Example(DataClassDictMixin):
    dt: datetime

    class Config:
        serialization_strategy = {
            datetime: TsSerializationStrategy(),
        }

example = Example.from_dict({"dt": "1672531200"})
print(example)
# Example(dt=datetime.datetime(2023, 1, 1, 3, 0))
print(example.to_dict())
# {'dt': 1672531200.0}

Here the passed string value "1672531200" will be converted to float before being passed to deserialize method thanks to the float annotation.

[!IMPORTANT]
As well as for SerializableType, the value of use_annotatons will be True by default in the future major release.

Third-party generic types

To create a generic version of a serialization strategy you need to follow these steps:

inherit Generic[...] type with the number of parameters matching the number of parameters of the target generic type
Write generic annotations for serialize method's return type and for deserialize method's argument type
Use the origin type of the target generic type in the serialization_strategy config section (typing.get_origin might be helpful)

There is no need to add use_annotations=True here because it's enabled implicitly for generic serialization strategies.

For example, there is a third-party multidict package that has a generic MultiDict type. A generic serialization strategy for it might look like this:

from dataclasses import dataclass
from datetime import date
from pprint import pprint
from typing import Generic, List, Tuple, TypeVar
from mashumaro import DataClassDictMixin
from mashumaro.types import SerializationStrategy

from multidict import MultiDict

T = TypeVar("T")

class MultiDictSerializationStrategy(SerializationStrategy, Generic[T]):
    def serialize(self, value: MultiDict[T]) -> List[Tuple[str, T]]:
        return [(k, v) for k, v in value.items()]

    def deserialize(self, value: List[Tuple[str, T]]) -> MultiDict[T]:
        return MultiDict(value)


@dataclass
class Example(DataClassDictMixin):
    floats: MultiDict[float]
    date_lists: MultiDict[List[date]]

    class Config:
        serialization_strategy = {
            MultiDict: MultiDictSerializationStrategy()
        }

example = Example(
    floats=MultiDict([("x", 1.1), ("x", 2.2)]),
    date_lists=MultiDict(
        [("x", [date(2023, 1, 1), date(2023, 1, 2)]),
         ("x", [date(2023, 2, 1), date(2023, 2, 2)])]
    ),
)
pprint(example.to_dict())
# {'date_lists': [['x', ['2023-01-01', '2023-01-02']],
#                 ['x', ['2023-02-01', '2023-02-02']]],
#  'floats': [['x', 1.1], ['x', 2.2]]}
assert Example.from_dict(example.to_dict()) == example

Field options

In some cases creating a new class just for one little thing could be excessive. Moreover, you may need to deal with third party classes that you are not allowed to change. You can use dataclasses.field function to configure some serialization aspects through its metadata parameter. Next section describes all supported options to use in metadata mapping.

If you don't want to remember the names of the options you can use field_options helper function:

from dataclasses import dataclass, field
from mashumaro import field_options

@dataclass
class A:
    x: int = field(metadata=field_options(...))

`serialize` option

This option allows you to change the serialization method. When using this option, the serialization behaviour depends on what type of value the option has. It could be either Callable[[Any], Any] or str.

A value of type Callable[[Any], Any] is a generic way to specify any callable object like a function, a class method, a class instance method, an instance of a callable class or even a lambda function to be called for serialization.

A value of type str sets a specific engine for serialization. Keep in mind that all possible engines depend on the data type that this option is used with. At this moment there are next serialization engines to choose from:

Applicable data types	Supported engines	Description
`NamedTuple`, `namedtuple`	`as_list`, `as_dict`	How to pack named tuples. By default `as_list` engine is used that means your named tuple class instance will be packed into a list of its values. You can pack it into a dictionary using `as_dict` engine.
`Any`	`omit`	Skip the field during serialization

[!TIP]
You can pass a field value as is without changes on serialization using pass_through.

Example:

from datetime import datetime
from dataclasses import dataclass, field
from typing import NamedTuple
from mashumaro import DataClassDictMixin

class MyNamedTuple(NamedTuple):
    x: int
    y: float

@dataclass
class A(DataClassDictMixin):
    dt: datetime = field(
        metadata={
            "serialize": lambda v: v.strftime('%Y-%m-%d %H:%M:%S')
        }
    )
    t: MyNamedTuple = field(metadata={"serialize": "as_dict"})

`deserialize` option

This option allows you to change the deserialization method. When using this option, the deserialization behaviour depends on what type of value the option has. It could be either Callable[[Any], Any] or str.

A value of type str sets a specific engine for deserialization. Keep in mind that all possible engines depend on the data type that this option is used with. At this moment there are next deserialization engines to choose from:

Applicable data types	Supported engines	Description
`datetime`, `date`, `time`	`ciso8601`, `pendulum`	How to parse datetime string. By default native `fromisoformat` of corresponding class will be used for `datetime`, `date` and `time` fields. It's the fastest way in most cases, but you can choose an alternative.
`NamedTuple`, `namedtuple`	`as_list`, `as_dict`	How to unpack named tuples. By default `as_list` engine is used that means your named tuple class instance will be created from a list of its values. You can unpack it from a dictionary using `as_dict` engine.

[!TIP]
You can pass a field value as is without changes on deserialization using pass_through.

Example:

from datetime import datetime
from dataclasses import dataclass, field
from typing import List, NamedTuple
from mashumaro import DataClassDictMixin
import ciso8601
import dateutil

class MyNamedTuple(NamedTuple):
    x: int
    y: float

@dataclass
class A(DataClassDictMixin):
    x: datetime = field(
        metadata={"deserialize": "pendulum"}
    )

class B(DataClassDictMixin):
    x: datetime = field(
        metadata={"deserialize": ciso8601.parse_datetime_as_naive}
    )

@dataclass
class C(DataClassDictMixin):
    dt: List[datetime] = field(
        metadata={
            "deserialize": lambda l: list(map(dateutil.parser.isoparse, l))
        }
    )

@dataclass
class D(DataClassDictMixin):
    x: MyNamedTuple = field(metadata={"deserialize": "as_dict"})

`serialization_strategy` option

This option is useful when you want to change the serialization logic for a dataclass field depending on some defined parameters using a reusable serialization scheme. You can find an example in the SerializationStrategy chapter.

[!TIP]
You can pass a field value as is without changes on serialization / deserialization using pass_through.

`alias` option

This option can be used to assign field aliases:

from dataclasses import dataclass, field
from mashumaro import DataClassDictMixin, field_options

@dataclass
class DataClass(DataClassDictMixin):
    a: int = field(metadata=field_options(alias="FieldA"))
    b: int = field(metadata=field_options(alias="#invalid"))

x = DataClass.from_dict({"FieldA": 1, "#invalid": 2})  # DataClass(a=1, b=2)

Config options

If inheritance is not an empty word for you, you'll fall in love with the Config class. You can register serialize and deserialize methods, define code generation options and other things just in one place. Or in some classes in different ways if you need flexibility. Inheritance is always on the first place.

There is a base class BaseConfig that you can inherit for the sake of convenience, but it's not mandatory.

In the following example you can see how the debug flag is changed from class to class: ModelA will have debug mode enabled but ModelB will not.

from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig

class BaseModel(DataClassDictMixin):
    class Config(BaseConfig):
        debug = True

class ModelA(BaseModel):
    a: int

class ModelB(BaseModel):
    b: int

    class Config(BaseConfig):
        debug = False

Next section describes all supported options to use in the config.

`debug` config option

If you enable the debug option the generated code for your data class will be printed.

`code_generation_options` config option

Some users may need functionality that wouldn't exist without extra cost such as valuable cpu time to execute additional instructions. Since not everyone needs such instructions, they can be enabled by a constant in the list, so the fastest basic behavior of the library will always remain by default. The following table provides a brief overview of all the available constants described below.

Constant	Description
`TO_DICT_ADD_OMIT_NONE_FLAG`	Adds `omit_none` keyword-only argument to `to_*` methods.
`TO_DICT_ADD_BY_ALIAS_FLAG`	Adds `by_alias` keyword-only argument to `to_*` methods.
`ADD_DIALECT_SUPPORT`	Adds `dialect` keyword-only argument to `from_` and `to_` methods.
`ADD_SERIALIZATION_CONTEXT`	Adds `context` keyword-only argument to `to_*` methods.

`serialization_strategy` config option

You can register custom SerializationStrategy, serialize and deserialize methods for specific types just in one place. It could be configured using a dictionary with types as keys. The value could be either a SerializationStrategy instance or a dictionary with serialize and deserialize values with the same meaning as in the field options.

from dataclasses import dataclass
from datetime import datetime, date
from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig
from mashumaro.types import SerializationStrategy

class FormattedDateTime(SerializationStrategy):
    def __init__(self, fmt):
        self.fmt = fmt

    def serialize(self, value: datetime) -> str:
        return value.strftime(self.fmt)

    def deserialize(self, value: str) -> datetime:
        return datetime.strptime(value, self.fmt)

@dataclass
class DataClass(DataClassDictMixin):

    x: datetime
    y: date

    class Config(BaseConfig):
        serialization_strategy = {
            datetime: FormattedDateTime("%Y"),
            date: {
                # you can use specific str values for datetime here as well
                "deserialize": "pendulum",
                "serialize": date.isoformat,
            },
        }

instance = DataClass.from_dict({"x": "2021", "y": "2021"})
# DataClass(x=datetime.datetime(2021, 1, 1, 0, 0), y=Date(2021, 1, 1))
dictionary = instance.to_dict()
# {'x': '2021', 'y': '2021-01-01'}

Note that you can register different methods for multiple logical types which are based on the same type using NewType and Annotated, see Extending existing types for details.

It's also possible to define a generic (de)serialization method for a generic type by registering a method for its origin type. Although this technique is widely used when working with third-party generic types using generic strategies, it can also be applied in simple scenarios:

from dataclasses import dataclass
from mashumaro import DataClassDictMixin

@dataclass
class C(DataClassDictMixin):
    ints: list[int]
    floats: list[float]

    class Config:
        serialization_strategy = {
            list: {  # origin type for list[int] and list[float] is list
                "serialize": lambda x: list(map(str, x)),
            }
        }

assert C([1], [2.2]).to_dict() == {'ints': ['1'], 'floats': ['2.2']}

`aliases` config option

Sometimes it's better to write the field aliases in one place. You can mix aliases here with aliases in the field options, but the last ones will always take precedence.

from dataclasses import dataclass
from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig

@dataclass
class DataClass(DataClassDictMixin):
    a: int
    b: int

    class Config(BaseConfig):
        aliases = {
            "a": "FieldA",
            "b": "FieldB",
        }

DataClass.from_dict({"FieldA": 1, "FieldB": 2})  # DataClass(a=1, b=2)

`serialize_by_alias` config option

All the fields with aliases will be serialized by them by default when this option is enabled. You can mix this config option with by_alias keyword argument.

from dataclasses import dataclass, field
from mashumaro import DataClassDictMixin, field_options
from mashumaro.config import BaseConfig

@dataclass
class DataClass(DataClassDictMixin):
    field_a: int = field(metadata=field_options(alias="FieldA"))

    class Config(BaseConfig):
        serialize_by_alias = True

DataClass(field_a=1).to_dict()  # {'FieldA': 1}

`allow_deserialization_not_by_alias` config option

When using aliases, the deserializer defaults to requiring the keys to match what is defined as the alias. If the flexibility to deserialize aliased and unaliased keys is required then the config option allow_deserialization_not_by_alias can be set to enable the feature.

from dataclasses import dataclass, field
from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig


@dataclass
class AliasedDataClass(DataClassDictMixin):
    foo: int = field(metadata={"alias": "alias_foo"})
    bar: int = field(metadata={"alias": "alias_bar"})

    class Config(BaseConfig):
        allow_deserialization_not_by_alias = True


alias_dict = {"alias_foo": 1, "alias_bar": 2}
t1 = AliasedDataClass.from_dict(alias_dict)

no_alias_dict = {"foo": 1, "bar": 2}
# This would raise `mashumaro.exceptions.MissingField`
# if allow_deserialization_not_by_alias was False
t2 = AliasedDataClass.from_dict(no_alias_dict)
assert t1 == t2

`omit_none` config option

All the fields with None values will be skipped during serialization by default when this option is enabled. You can mix this config option with omit_none keyword argument.

from dataclasses import dataclass
from typing import Optional
from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig

@dataclass
class DataClass(DataClassDictMixin):
    x: Optional[int] = 42

    class Config(BaseConfig):
        omit_none = True

DataClass(x=None).to_dict()  # {}

`omit_default` config option

When this option enabled, all the fields that have values equal to the defaults or the default_factory results will be skipped during serialization.

from dataclasses import dataclass, field
from typing import List, Optional, Tuple
from mashumaro import DataClassDictMixin, field_options
from mashumaro.config import BaseConfig

@dataclass
class Foo:
    foo: str

@dataclass
class DataClass(DataClassDictMixin):
    a: int = 42
    b: Tuple[int, ...] = field(default=(1, 2, 3))
    c: List[Foo] = field(default_factory=lambda: [Foo("foo")])
    d: Optional[str] = None

    class Config(BaseConfig):
        omit_default = True

DataClass(a=42, b=(1, 2, 3), c=[Foo("foo")]).to_dict()  # {}

`namedtuple_as_dict` config option

Dataclasses are a great way to declare and use data models. But it's not the only way. Python has a typed version of namedtuple called NamedTuple which looks similar to dataclasses:

from typing import NamedTuple

class Point(NamedTuple):
    x: int
    y: int

the same with a dataclass will look like this:

from dataclasses import dataclass

@dataclass
class Point:
    x: int
    y: int

At first glance, you can use both options. But imagine that you need to create a bunch of instances of the Point class. Due to how dataclasses work you will have more memory consumption compared to named tuples. In such a case it could be more appropriate to use named tuples.

By default, all named tuples are packed into lists. But with namedtuple_as_dict option you have a drop-in replacement for dataclasses:

from dataclasses import dataclass
from typing import List, NamedTuple
from mashumaro import DataClassDictMixin

class Point(NamedTuple):
    x: int
    y: int

@dataclass
class DataClass(DataClassDictMixin):
    points: List[Point]

    class Config:
        namedtuple_as_dict = True

obj = DataClass.from_dict({"points": [{"x": 0, "y": 0}, {"x": 1, "y": 1}]})
print(obj.to_dict())  # {"points": [{"x": 0, "y": 0}, {"x": 1, "y": 1}]}

If you want to serialize only certain named tuple fields as dictionaries, you can use the corresponding serialization and deserialization engines.

`allow_postponed_evaluation` config option

PEP 563 solved the problem of forward references by postponing the evaluation of annotations, so you can write the following code:

from __future__ import annotations
from dataclasses import dataclass
from mashumaro import DataClassDictMixin

@dataclass
class A(DataClassDictMixin):
    x: B

@dataclass
class B(DataClassDictMixin):
    y: int

obj = A.from_dict({'x': {'y': 1}})

You don't need to write anything special here, forward references work out of the box. If a field of a dataclass has a forward reference in the type annotations, building of from_* and to_* methods of this dataclass will be postponed until they are called once. However, if for some reason you don't want the evaluation to be possibly postponed, you can disable it using allow_postponed_evaluation option:

from __future__ import annotations
from dataclasses import dataclass
from mashumaro import DataClassDictMixin

@dataclass
class A(DataClassDictMixin):
    x: B

    class Config:
        allow_postponed_evaluation = False

# UnresolvedTypeReferenceError: Class A has unresolved type reference B
# in some of its fields

@dataclass
class B(DataClassDictMixin):
    y: int

In this case you will get UnresolvedTypeReferenceError regardless of whether class B is declared below or not.

`dialect` config option

This option is described below in the Dialects section.

`orjson_options` config option

This option changes default options for orjson.dumps encoder which is used in DataClassORJSONMixin. For example, you can tell orjson to handle non-str dict keys as the built-in json.dumps encoder does. See orjson documentation to read more about these options.

import orjson
from dataclasses import dataclass
from typing import Dict
from mashumaro.config import BaseConfig
from mashumaro.mixins.orjson import DataClassORJSONMixin

@dataclass
class MyClass(DataClassORJSONMixin):
    x: Dict[int, int]

    class Config(BaseConfig):
        orjson_options = orjson.OPT_NON_STR_KEYS

assert MyClass({1: 2}).to_json() == {"1": 2}

`discriminator` config option

This option is described in the Class level discriminator section.

`lazy_compilation` config option

By using this option, the compilation of the from_* and to_* methods will be deferred until they are called first time. This will reduce the import time and, in certain instances, may enhance the speed of deserialization by leveraging the data that is accessible after the class has been created.

[!CAUTION]
If you need to save a reference to from_* or to_* method, you should do it after the method is compiled. To be safe, you can always use lambda function:
from_dict = lambda x: MyModel.from_dict(x)
to_dict = lambda x: x.to_dict()

`sort_keys` config option

When set, the keys on serialized dataclasses will be sorted in alphabetical order.

Unlike the sort_keys option in the standard library's json.dumps function, this option acts at class creation time and has no effect on the performance of serialization.

from dataclasses import dataclass
from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig

@dataclass
class SortedDataClass(DataClassDictMixin):
    foo: int
    bar: int

    class Config(BaseConfig):
        sort_keys = True

t = SortedDataClass(1, 2)
assert t.to_dict() == {"bar": 2, "foo": 1}

`forbid_extra_keys` config option

When set, the deserialization of dataclasses will fail if the input dictionary contains keys that are not present in the dataclass.

from dataclasses import dataclass
from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig

@dataclass
class DataClass(DataClassDictMixin):
    a: int

    class Config(BaseConfig):
        forbid_extra_keys = True

DataClass.from_dict({"a": 1, "b": 2})  # ExtraKeysError: Extra keys: {'b'}

It plays well with aliases and allow_deserialization_not_by_alias options.

Passing field values as is

In some cases it's needed to pass a field value as is without any changes during serialization / deserialization. There is a predefined pass_through object that can be used as serialization_strategy or serialize / deserialize options:

from dataclasses import dataclass, field
from mashumaro import DataClassDictMixin, pass_through

class MyClass:
    def __init__(self, some_value):
        self.some_value = some_value

@dataclass
class A1(DataClassDictMixin):
    x: MyClass = field(
        metadata={
            "serialize": pass_through,
            "deserialize": pass_through,
        }
    )

@dataclass
class A2(DataClassDictMixin):
    x: MyClass = field(
        metadata={
            "serialization_strategy": pass_through,
        }
    )

@dataclass
class A3(DataClassDictMixin):
    x: MyClass

    class Config:
        serialization_strategy = {
            MyClass: pass_through,
        }

@dataclass
class A4(DataClassDictMixin):
    x: MyClass

    class Config:
        serialization_strategy = {
            MyClass: {
                "serialize": pass_through,
                "deserialize": pass_through,
            }
        }

my_class_instance = MyClass(42)

assert A1.from_dict({'x': my_class_instance}).x == my_class_instance
assert A2.from_dict({'x': my_class_instance}).x == my_class_instance
assert A3.from_dict({'x': my_class_instance}).x == my_class_instance
assert A4.from_dict({'x': my_class_instance}).x == my_class_instance

a1_dict = A1(my_class_instance).to_dict()
a2_dict = A2(my_class_instance).to_dict()
a3_dict = A3(my_class_instance).to_dict()
a4_dict = A4(my_class_instance).to_dict()

assert a1_dict == a2_dict == a3_dict == a4_dict == {"x": my_class_instance}

Extending existing types

There are situations where you might want some values of the same type to be treated as their own type. You can create new logical types with NewType, Annotated or TypeAliasType and register serialization strategies for them:

from typing import Mapping, NewType, Annotated
from dataclasses import dataclass
from mashumaro import DataClassDictMixin

SessionID = NewType("SessionID", str)
AccountID = Annotated[str, "AccountID"]

type DeviceID = str

@dataclass
class Context(DataClassDictMixin):
    account_sessions: Mapping[AccountID, SessionID]
    account_devices: list[DeviceID]

    class Config:
        serialization_strategy = {
            AccountID: {
                "deserialize": lambda x: ...,
                "serialize": lambda x: ...,
            },
            SessionID: {
                "deserialize": lambda x: ...,
                "serialize": lambda x: ...,
            },
            DeviceID: {
                "deserialize": lambda x: ...,
                "serialize": lambda x: ...,
            }
        }

Although using NewType is usually the most reliable way to avoid logical errors, you have to pay for it with notable overhead. If you are creating dataclass instances manually, then you know that type checkers will enforce you to enclose a value in your "NewType" callable, which leads to performance degradation:

python -m timeit -s "from typing import NewType; MyInt = NewType('MyInt', int)" "MyInt(42)"
10000000 loops, best of 5: 31.1 nsec per loop

python -m timeit -s "from typing import NewType; MyInt = NewType('MyInt', int)" "42"
50000000 loops, best of 5: 4.35 nsec per loop

However, when you create dataclass instances using the from_* method provided by one of the mixins or using one of the decoders, there will be no performance degradation, because the value won't be enclosed in the callable in the generated code. Therefore, if performance is more important to you than catching logical errors by type checkers, and you are actively creating or changing dataclasses manually, then you should take a closer look at using Annotated.

Field aliases

In some cases it's better to have different names for a field in your dataclass and in its serialized view. For example, a third-party legacy API you are working with might operate with camel case style, but you stick to snake case style in your code base. Or you want to load data with keys that are invalid identifiers in Python. Aliases can solve this problem.

There are multiple ways to assign an alias:

Using Alias(...) annotation in a field type
Using alias parameter in field metadata
Using aliases parameter in a dataclass config

By default, aliases only affect deserialization, but it can be extended to serialization as well. If you want to serialize all the fields by aliases you have two options to do so:

serialize_by_alias config option
serialize_by_alias dialect option
by_alias keyword argument in to_* methods

Here is an example with Alias annotation in a field type:

from dataclasses import dataclass
from typing import Annotated
from mashumaro import DataClassDictMixin
from mashumaro.types import Alias

@dataclass
class DataClass(DataClassDictMixin):
    foo_bar: Annotated[int, Alias("fooBar")]

obj = DataClass.from_dict({"fooBar": 42})  # DataClass(foo_bar=42)
obj.to_dict()  # {"foo_bar": 42}  # no aliases on serialization by default

The same with field metadata:

from dataclasses import dataclass, field
from mashumaro import field_options

@dataclass
class DataClass:
    foo_bar: str = field(metadata=field_options(alias="fooBar"))

And with a dataclass config:

from dataclasses import dataclass
from mashumaro.config import BaseConfig

@dataclass
class DataClass:
    foo_bar: str

    class Config(BaseConfig):
        aliases = {"foo_bar": "fooBar"}

[!TIP]
If you want to deserialize all the fields by its names along with aliases, there is a config option for that.

Dialects

Sometimes it's needed to have different serialization and deserialization methods depending on the data source where entities of the dataclass are stored or on the API to which the entities are being sent or received from. There is a special Dialect type that may contain all the differences from the default serialization and deserialization methods. You can create different dialects and use each of them for the same dataclass depending on the situation.

Suppose we have the following dataclass with a field of type date:

@dataclass
class Entity(DataClassDictMixin):
    dt: date

By default, a field of date type serializes to a string in ISO 8601 format, so the serialized entity will look like {'dt': '2021-12-31'}. But what if we have, for example, two sensitive legacy Ethiopian and Japanese APIs that use two different formats for dates — dd/mm/yyyy and yyyy年mm月dd日? Instead of creating two similar dataclasses we can have one dataclass and two dialects:

from dataclasses import dataclass
from datetime import date, datetime
from mashumaro import DataClassDictMixin
from mashumaro.config import ADD_DIALECT_SUPPORT
from mashumaro.dialect import Dialect
from mashumaro.types import SerializationStrategy

class DateTimeSerializationStrategy(SerializationStrategy):
    def __init__(self, fmt: str):
        self.fmt = fmt

    def serialize(self, value: date) -> str:
        return value.strftime(self.fmt)

    def deserialize(self, value: str) -> date:
        return datetime.strptime(value, self.fmt).date()

class EthiopianDialect(Dialect):
    serialization_strategy = {
        date: DateTimeSerializationStrategy("%d/%m/%Y")
    }

class JapaneseDialect(Dialect):
    serialization_strategy = {
        date: DateTimeSerializationStrategy("%Y年%m月%d日")
    }

@dataclass
class Entity(DataClassDictMixin):
    dt: date

    class Config:
        code_generation_options = [ADD_DIALECT_SUPPORT]

entity = Entity(date(2021, 12, 31))
entity.to_dict(dialect=EthiopianDialect)  # {'dt': '31/12/2021'}
entity.to_dict(dialect=JapaneseDialect)   # {'dt': '2021年12月31日'}
Entity.from_dict({'dt': '2021年12月31日'}, dialect=JapaneseDialect)

`serialization_strategy` dialect option

This dialect option has the same meaning as the similar config option but for the dialect scope. You can register custom SerializationStrategy, serialize and deserialize methods for the specific types.

`serialize_by_alias` dialect option

This dialect option has the same meaning as the similar config option but for the dialect scope.

`omit_none` dialect option

This dialect option has the same meaning as the similar config option but for the dialect scope.

`omit_default` dialect option

This dialect option has the same meaning as the similar config option but for the dialect scope.

`namedtuple_as_dict` dialect option

This dialect option has the same meaning as the similar config option but for the dialect scope.

`no_copy_collections` dialect option

By default, all collection data types are serialized as a copy to prevent mutation of the original collection. As an example, if a dataclass contains a field of type list[str], then it will be serialized as a copy of the original list, so you can safely mutate it after. The downside is that copying is always slower than using a reference to the original collection. In some cases we know beforehand that mutation doesn't take place or is even desirable, so we can benefit from avoiding unnecessary copies by setting no_copy_collections to a sequence of origin collection data types. This is applicable only for collections containing elements that do not require conversion.

from dataclasses import dataclass
from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig
from mashumaro.dialect import Dialect

class NoCopyDialect(Dialect):
    no_copy_collections = (list, dict, set)

@dataclass
class DataClass(DataClassDictMixin):
    simple_list: list[str]
    simple_dict: dict[str, str]
    simple_set: set[str]

    class Config(BaseConfig):
        dialect = NoCopyDialect

obj = DataClass(["foo"], {"bar": "baz"}, {"foobar"})
data = obj.to_dict()

assert data["simple_list"] is obj.simple_list
assert data["simple_dict"] is obj.simple_dict
assert data["simple_set"] is obj.simple_set

This option is enabled for list and dict in the default dialects that belong to mixins and codecs for the following formats:

JSON (orjson library)
TOML
MessagePack

Changing the default dialect

You can change the default serialization and deserialization methods not only in the serialization_strategy config option but also using the dialect config option. If you have multiple dataclasses without a common parent class the default dialect can help you to reduce the number of code lines written:

@dataclass
class Entity(DataClassDictMixin):
    dt: date

    class Config:
        dialect = JapaneseDialect

entity = Entity(date(2021, 12, 31))
entity.to_dict()  # {'dt': '2021年12月31日'}
assert Entity.from_dict({'dt': '2021年12月31日'}) == entity

Default dialect can also be set when using codecs:

from mashumaro.codecs import BasicDecoder, BasicEncoder

@dataclass
class Entity:
    dt: date

decoder = BasicDecoder(Entity, default_dialect=JapaneseDialect)
encoder = BasicEncoder(Entity, default_dialect=JapaneseDialect)

entity = Entity(date(2021, 12, 31))
encoder.encode(entity) # {'dt': '2021年12月31日'}
assert decoder.decode({'dt': '2021年12月31日'}) == entity

Discriminator

There is a special Discriminator class that allows you to customize how a union of dataclasses or their hierarchy will be deserialized. It has the following parameters that affects class selection rules:

field — optional name of the input dictionary key (also known as tag) by which all the variants can be distinguished
include_subtypes — allow to deserialize subclasses
include_supertypes — allow to deserialize superclasses
variant_tagger_fn — a custom function used to generate tag values associated with a variant

By default, each variant that you want to discriminate by tags should have a class-level attribute containing an associated tag value. This attribute should have a name defined by field parameter. The tag value coule be in the following forms:

without annotations: type = 42
annotated as ClassVar: type: ClassVar[int] = 42
annotated as Final: type: Final[int] = 42
annotated as Literal: type: Literal[42] = 42
annotated as StrEnum: type: ResponseType = ResponseType.OK

[!NOTE]
Keep in mind that by default only Final, Literal and StrEnum fields are processed during serialization.

However, it is possible to use discriminator without the class-level attribute. You can provide a custom function that generates one or many variant tag values. This function should take a class as the only argument and return either a single value of the basic type like str or int or a list of them to associate multiple tags with a variant. The common practice is to use a class name as a single tag value:

variant_tagger_fn = lambda cls: cls.__name__

Next, we will look at different use cases, as well as their pros and cons.

Subclasses distinguishable by a field

Often you have a base dataclass and multiple subclasses that are easily distinguishable from each other by the value of a particular field. For example, there may be different events, messages or requests with a discriminator field "event_type", "message_type" or just "type". You could've listed all of them within Union type, but it would be too verbose and impractical. Moreover, deserialization of the union would be slow, since we need to iterate over each variant in the list until we find the right one.

We can improve subclass deserialization using Discriminator as annotation within Annotated type. We will use field parameter and set include_subtypes to True.

[!IMPORTANT]
The discriminator field should be accessible from the __dict__ attribute of a specific descendant, i.e. defined at the level of that descendant. A descendant class without a discriminator field will be ignored, but its descendants won't.

Suppose we have a hierarchy of client events distinguishable by a class attribute "type":

from dataclasses import dataclass
from ipaddress import IPv4Address
from mashumaro import DataClassDictMixin

@dataclass
class ClientEvent(DataClassDictMixin):
    pass

@dataclass
class ClientConnectedEvent(ClientEvent):
    type = "connected"
    client_ip: IPv4Address

@dataclass
class ClientDisconnectedEvent(ClientEvent):
    type = "disconnected"
    client_ip: IPv4Address

We use base dataclass ClientEvent for a field of another dataclass:

from typing import Annotated, List
# or from typing_extensions import Annotated
from mashumaro.types import Discriminator


@dataclass
class AggregatedEvents(DataClassDictMixin):
    list: List[
        Annotated[
            ClientEvent, Discriminator(field="type", include_subtypes=True)
        ]
    ]

Now we can deserialize events based on "type" value:

events = AggregatedEvents.from_dict(
    {
        "list": [
            {"type": "connected", "client_ip": "10.0.0.42"},
            {"type": "disconnected", "client_ip": "10.0.0.42"},
        ]
    }
)
assert events == AggregatedEvents(
    list=[
        ClientConnectedEvent(client_ip=IPv4Address("10.0.0.42")),
        ClientDisconnectedEvent(client_ip=IPv4Address("10.0.0.42")),
    ]
)

Subclasses without a common field

In rare cases you have to deal with subclasses that don't have a common field name which they can be distinguished by. Since Discriminator can be initialized without "field" parameter you can use it with only include_subclasses enabled. The drawback is that we will have to go through all the subclasses until we find the suitable one. It's almost like using Union type but with subclasses support.

Suppose we're making a brunch. We have some ingredients:

@dataclass
class Ingredient(DataClassDictMixin):
    name: str

@dataclass
class Hummus(Ingredient):
    made_of: Literal["chickpeas", "beet", "artichoke"]
    grams: int

@dataclass
class Celery(Ingredient):
    pieces: int

Let's create a plate:

@dataclass
class Plate(DataClassDictMixin):
    ingredients: List[
        Annotated[Ingredient, Discriminator(include_subtypes=True)]
    ]

And now we can put our ingredients on the plate:

plate = Plate.from_dict(
    {
        "ingredients": [
            {
                "name": "hummus from the shop",
                "made_of": "chickpeas",
                "grams": 150,
            },
            {"name": "celery from my garden", "pieces": 5},
        ]
    }
)
assert plate == Plate(
    ingredients=[
        Hummus(name="hummus from the shop", made_of="chickpeas", grams=150),
        Celery(name="celery from my garden", pieces=5),
    ]
)

In some cases it's necessary to fall back to the base class if there is no suitable subclass. We can set include_supertypes to True:

@dataclass
class Plate(DataClassDictMixin):
    ingredients: List[
        Annotated[
            Ingredient,
            Discriminator(include_subtypes=True, include_supertypes=True),
        ]
    ]

plate = Plate.from_dict(
    {
        "ingredients": [
            {
                "name": "hummus from the shop",
                "made_of": "chickpeas",
                "grams": 150,
            },
            {"name": "celery from my garden", "pieces": 5},
            {"name": "cumin"}  # <- new unknown ingredient
        ]
    }
)
assert plate == Plate(
    ingredients=[
        Hummus(name="hummus from the shop", made_of="chickpeas", grams=150),
        Celery(name="celery from my garden", pieces=5),
        Ingredient(name="cumin"),  # <- unknown ingredient added
    ]
)

Class level discriminator

It may often be more convenient to specify a Discriminator once at the class level and use that class without Annotated type for subclass deserialization. Depending on the Discriminator parameters, it can be used as a replacement for subclasses distinguishable by a field as well as for subclasses without a common field. The only difference is that you can't use include_supertypes=True because it would lead to a recursion error.

Reworked example will look like this:

from dataclasses import dataclass
from ipaddress import IPv4Address
from typing import List
from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig
from mashumaro.types import Discriminator

@dataclass
class ClientEvent(DataClassDictMixin):
    class Config(BaseConfig):
        discriminator = Discriminator(  # <- add discriminator
            field="type",
            include_subtypes=True,
        )

@dataclass
class ClientConnectedEvent(ClientEvent):
    type = "connected"
    client_ip: IPv4Address

@dataclass
class ClientDisconnectedEvent(ClientEvent):
    type = "disconnected"
    client_ip: IPv4Address

@dataclass
class AggregatedEvents(DataClassDictMixin):
    list: List[ClientEvent]  # <- use base class here

And now we can deserialize events based on "type" value as we did earlier:

events = AggregatedEvents.from_dict(
    {
        "list": [
            {"type": "connected", "client_ip": "10.0.0.42"},
            {"type": "disconnected", "client_ip": "10.0.0.42"},
        ]
    }
)
assert events == AggregatedEvents(
    list=[
        ClientConnectedEvent(client_ip=IPv4Address("10.0.0.42")),
        ClientDisconnectedEvent(client_ip=IPv4Address("10.0.0.42")),
    ]
)

What's more interesting is that you can now deserialize subclasses simply by calling the superclass from_* method, which is very useful:

disconnected_event = ClientEvent.from_dict(
    {"type": "disconnected", "client_ip": "10.0.0.42"}
)
assert disconnected_event == ClientDisconnectedEvent(IPv4Address("10.0.0.42"))

The same is applicable for subclasses without a common field:

@dataclass
class Ingredient(DataClassDictMixin):
    name: str

    class Config:
        discriminator = Discriminator(include_subtypes=True)

...

celery = Ingredient.from_dict({"name": "celery from my garden", "pieces": 5})
assert celery == Celery(name="celery from my garden", pieces=5)

Working with union of classes

Deserialization of union of types distinguishable by a particular field will be much faster using Discriminator because there will be no traversal of all classes and an attempt to deserialize each of them. Usually this approach can be used when you have multiple classes without a common superclass or when you only need to deserialize some of the subclasses. In the following example we will use include_supertypes=True to deserialize two subclasses out of three:

from dataclasses import dataclass
from typing import Annotated, Literal, Union
# or from typing_extensions import Annotated
from mashumaro import DataClassDictMixin
from mashumaro.types import Discriminator

@dataclass
class Event(DataClassDictMixin):
    pass

@dataclass
class Event1(Event):
    code: Literal[1] = 1
    ...

@dataclass
class Event2(Event):
    code: Literal[2] = 2
    ...

@dataclass
class Event3(Event):
    code: Literal[3] = 3
    ...

@dataclass
class Message(DataClassDictMixin):
    event: Annotated[
        Union[Event1, Event2],
        Discriminator(field="code", include_supertypes=True),
    ]

event1_msg = Message.from_dict({"event": {"code": 1, ...}})
event2_msg = Message.from_dict({"event": {"code": 2, ...}})
assert isinstance(event1_msg.event, Event1)
assert isinstance(event2_msg.event, Event2)

# raises InvalidFieldValue:
Message.from_dict({"event": {"code": 3, ...}})

Again, it's not necessary to have a common superclass. If you have a union of dataclasses without a field that they can be distinguishable by, you can still use Discriminator, but deserialization will almost be the same as for Union type without Discriminator except that it could be possible to deserialize subclasses with include_subtypes=True.

[!IMPORTANT]
When both include_subtypes and include_supertypes are enabled, all subclasses will be attempted to be deserialized first, superclasses — at the end.

In the following example you can see how priority works — first we try to deserialize ChickpeaHummus, and if it fails, then we try Hummus:

@dataclass
class Hummus(DataClassDictMixin):
    made_of: Literal["chickpeas", "artichoke"]
    grams: int

@dataclass
class ChickpeaHummus(Hummus):
    made_of: Literal["chickpeas"]

@dataclass
class Celery(DataClassDictMixin):
    pieces: int

@dataclass
class Plate(DataClassDictMixin):
    ingredients: List[
        Annotated[
            Union[Hummus, Celery],
            Discriminator(include_subtypes=True, include_supertypes=True),
        ]
    ]

plate = Plate.from_dict(
    {
        "ingredients": [
            {"made_of": "chickpeas", "grams": 100},
            {"made_of": "artichoke", "grams": 50},
            {"pieces": 4},
        ]
    }
)
assert plate == Plate(
    ingredients=[
        ChickpeaHummus(made_of='chickpeas', grams=100),  # <- subclass
        Hummus(made_of='artichoke', grams=50),  # <- superclass
        Celery(pieces=4),
    ]
)

Using a custom variant tagger function

Sometimes it is impractical to have a class-level attribute with a tag value, especially when you have a lot of classes. We can have a custom tagger function instead. This method is applicable for all scenarios of using the discriminator, but for demonstration purposes, let's focus only on one of them.

Suppose we want to use the middle part of Client*Event as a tag value:

from dataclasses import dataclass
from ipaddress import IPv4Address
from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig
from mashumaro.types import Discriminator


def client_event_tagger(cls):
    # not the best way of doing it, it's just a demo
    return cls.__name__[6:-5].lower()

@dataclass
class ClientEvent(DataClassDictMixin):
    class Config(BaseConfig):
        discriminator = Discriminator(
            field="type",
            include_subtypes=True,
            variant_tagger_fn=client_event_tagger,
        )

@dataclass
class ClientConnectedEvent(ClientEvent):
    client_ip: IPv4Address

@dataclass
class ClientDisconnectedEvent(ClientEvent):
    client_ip: IPv4Address

We can now deserialize subclasses as we did it earlier without variant tagger:

disconnected_event = ClientEvent.from_dict(
    {"type": "disconnected", "client_ip": "10.0.0.42"}
)
assert disconnected_event == ClientDisconnectedEvent(IPv4Address("10.0.0.42"))

If we need to associate multiple tags with a single variant, we can return a list of tags:

def client_event_tagger(cls):
    name = cls.__name__[6:-5]
    return [name.lower(), name.upper()]

Code generation options

Add `omit_none` keyword argument

If you want to have control over whether to skip None values on serialization you can add omit_none parameter to to_* methods using the code_generation_options list. The default value of omit_none parameter depends on whether the omit_none config option or omit_none dialect option is enabled.

from dataclasses import dataclass
from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig, TO_DICT_ADD_OMIT_NONE_FLAG

@dataclass
class Inner(DataClassDictMixin):
    x: int = None
    # "x" won't be omitted since there is no TO_DICT_ADD_OMIT_NONE_FLAG here

@dataclass
class Model(DataClassDictMixin):
    x: Inner
    a: int = None
    b: str = None  # will be omitted

    class Config(BaseConfig):
        code_generation_options = [TO_DICT_ADD_OMIT_NONE_FLAG]

Model(x=Inner(), a=1).to_dict(omit_none=True)  # {'x': {'x': None}, 'a': 1}

Add `by_alias` keyword argument

If you want to have control over whether to serialize fields by their aliases you can add by_alias parameter to to_* methods using the code_generation_options list. The default value of by_alias parameter depends on whether the serialize_by_alias config option is enabled.

from dataclasses import dataclass, field
from mashumaro import DataClassDictMixin, field_options
from mashumaro.config import BaseConfig, TO_DICT_ADD_BY_ALIAS_FLAG

@dataclass
class DataClass(DataClassDictMixin):
    field_a: int = field(metadata=field_options(alias="FieldA"))

    class Config(BaseConfig):
        code_generation_options = [TO_DICT_ADD_BY_ALIAS_FLAG]

DataClass(field_a=1).to_dict()  # {'field_a': 1}
DataClass(field_a=1).to_dict(by_alias=True)  # {'FieldA': 1}

Add `dialect` keyword argument

Support for dialects is disabled by default for performance reasons. You can enable it using a ADD_DIALECT_SUPPORT constant:

from dataclasses import dataclass
from datetime import date
from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig, ADD_DIALECT_SUPPORT

@dataclass
class Entity(DataClassDictMixin):
    dt: date

    class Config(BaseConfig):
        code_generation_options = [ADD_DIALECT_SUPPORT]

Add `context` keyword argument

Sometimes it's needed to pass a "context" object to the serialization hooks that will take it into account. For example, you could want to have an option to remove sensitive data from the serialization result if you need to. You can add context parameter to to_* methods that will be passed to __pre_serialize__ and __post_serialize__ hooks. The type of this context as well as its mutability is up to you.

from dataclasses import dataclass
from typing import Dict, Optional
from uuid import UUID
from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig, ADD_SERIALIZATION_CONTEXT

class BaseModel(DataClassDictMixin):
    class Config(BaseConfig):
        code_generation_options = [ADD_SERIALIZATION_CONTEXT]

@dataclass
class Account(BaseModel):
    id: UUID
    username: str
    name: str

    def __pre_serialize__(self, context: Optional[Dict] = None):
        return self

    def __post_serialize__(self, d: Dict, context: Optional[Dict] = None):
        if context and context.get("remove_sensitive_data"):
            d["username"] = "***"
            d["name"] = "***"
        return d

@dataclass
class Session(BaseModel):
    id: UUID
    key: str
    account: Account

    def __pre_serialize__(self, context: Optional[Dict] = None):
        return self

    def __post_serialize__(self, d: Dict, context: Optional[Dict] = None):
        if context and context.get("remove_sensitive_data"):
            d["key"] = "***"
        return d


foo = Session(
    id=UUID('03321c9f-6a97-421e-9869-918ff2867a71'),
    key="VQ6Q9bX4c8s",
    account=Account(
        id=UUID('4ef2baa7-edef-4d6a-b496-71e6d72c58fb'),
        username="john_doe",
        name="John"
    )
)
assert foo.to_dict() == {
    'id': '03321c9f-6a97-421e-9869-918ff2867a71',
    'key': 'VQ6Q9bX4c8s',
    'account': {
        'id': '4ef2baa7-edef-4d6a-b496-71e6d72c58fb',
        'username': 'john_doe',
        'name': 'John'
    }
}
assert foo.to_dict(context={"remove_sensitive_data": True}) == {
    'id': '03321c9f-6a97-421e-9869-918ff2867a71',
    'key': '***',
    'account': {
        'id': '4ef2baa7-edef-4d6a-b496-71e6d72c58fb',
        'username': '***',
        'name': '***'
    }
}

Generic dataclasses

Along with user-defined generic types implementing SerializableType interface, generic and variadic generic dataclasses can also be used. There are two applicable scenarios for them.

Generic dataclass inheritance

If you have a generic dataclass and want to serialize and deserialize its instances depending on the concrete types, you can use inheritance for that:

from dataclasses import dataclass
from datetime import date
from typing import Generic, Mapping, TypeVar, TypeVarTuple
from mashumaro import DataClassDictMixin

KT = TypeVar("KT")
VT = TypeVar("VT", date, str)
Ts = TypeVarTuple("Ts")

@dataclass
class GenericDataClass(Generic[KT, VT, *Ts]):
    x: Mapping[KT, VT]
    y: Tuple[*Ts, KT]

@dataclass
class ConcreteDataClass(
    GenericDataClass[str, date, *Tuple[float, ...]],
    DataClassDictMixin,
):
    pass

ConcreteDataClass.from_dict({"x": {"a": "2021-01-01"}, "y": [1, 2, "a"]})
# ConcreteDataClass(x={'a': datetime.date(2021, 1, 1)}, y=(1.0, 2.0, 'a'))

You can override TypeVar field with a concrete type or another TypeVar. Partial specification of concrete types is also allowed. If a generic dataclass is inherited without type overriding the types of its fields remain untouched.

Generic dataclass in a field type

Another approach is to specify concrete types in the field type hints. This can help to have different versions of the same generic dataclass:

from dataclasses import dataclass
from datetime import date
from typing import Generic, TypeVar
from mashumaro import DataClassDictMixin

T = TypeVar('T')

@dataclass
class GenericDataClass(Generic[T], DataClassDictMixin):
    x: T

@dataclass
class DataClass(DataClassDictMixin):
    date: GenericDataClass[date]
    str: GenericDataClass[str]

instance = DataClass(
    date=GenericDataClass(x=date(2021, 1, 1)),
    str=GenericDataClass(x='2021-01-01'),
)
dictionary = {'date': {'x': '2021-01-01'}, 'str': {'x': '2021-01-01'}}
assert DataClass.from_dict(dictionary) == instance

GenericSerializableType interface

There is a generic alternative to SerializableType called GenericSerializableType. It makes it possible to decide yourself how to serialize and deserialize input data depending on the types provided:

from dataclasses import dataclass
from datetime import date
from typing import Dict, TypeVar
from mashumaro import DataClassDictMixin
from mashumaro.types import GenericSerializableType

KT = TypeVar("KT")
VT = TypeVar("VT")

class DictWrapper(Dict[KT, VT], GenericSerializableType):
    __packers__ = {date: lambda x: x.isoformat(), str: str}
    __unpackers__ = {date: date.fromisoformat, str: str}

    def _serialize(self, types) -> Dict[KT, VT]:
        k_type, v_type = types
        k_conv = self.__packers__[k_type]
        v_conv = self.__packers__[v_type]
        return {k_conv(k): v_conv(v) for k, v in self.items()}

    @classmethod
    def _deserialize(cls, value, types) -> "DictWrapper[KT, VT]":
        k_type, v_type = types
        k_conv = cls.__unpackers__[k_type]
        v_conv = cls.__unpackers__[v_type]
        return cls({k_conv(k): v_conv(v) for k, v in value.items()})

@dataclass
class DataClass(DataClassDictMixin):
    x: DictWrapper[date, str]
    y: DictWrapper[str, date]

input_data = {
    "x": {"2022-12-07": "2022-12-07"},
    "y": {"2022-12-07": "2022-12-07"},
}
obj = DataClass.from_dict(input_data)
assert obj == DataClass(
    x=DictWrapper({date(2022, 12, 7): "2022-12-07"}),
    y=DictWrapper({"2022-12-07": date(2022, 12, 7)}),
)
assert obj.to_dict() == input_data

As you can see, the code turns out to be massive compared to the alternative but in rare cases such flexibility can be useful. You should think twice about whether it's really worth using it.

Serialization hooks

In some cases you need to prepare input / output data or do some extraordinary actions at different stages of the deserialization / serialization lifecycle. You can do this with different types of hooks.

Before deserialization

For doing something with a dictionary that will be passed to deserialization you can use __pre_deserialize__ class method:

@dataclass
class A(DataClassJSONMixin):
    abc: int

    @classmethod
    def __pre_deserialize__(cls, d: Dict[Any, Any]) -> Dict[Any, Any]:
        return {k.lower(): v for k, v in d.items()}

print(DataClass.from_dict({"ABC": 123}))    # DataClass(abc=123)
print(DataClass.from_json('{"ABC": 123}'))  # DataClass(abc=123)

After deserialization

For doing something with a dataclass instance that was created as a result of deserialization you can use __post_deserialize__ class method:

@dataclass
class A(DataClassJSONMixin):
    abc: int

    @classmethod
    def __post_deserialize__(cls, obj: 'A') -> 'A':
        obj.abc = 456
        return obj

print(DataClass.from_dict({"abc": 123}))    # DataClass(abc=456)
print(DataClass.from_json('{"abc": 123}'))  # DataClass(abc=456)

Before serialization

For doing something before serialization you can use __pre_serialize__ method:

@dataclass
class A(DataClassJSONMixin):
    abc: int
    counter: ClassVar[int] = 0

    def __pre_serialize__(self) -> 'A':
        self.counter += 1
        return self

obj = DataClass(abc=123)
obj.to_dict()
obj.to_json()
print(obj.counter)  # 2

Note that you can add an additional context argument using the corresponding code generation option.

After serialization

For doing something with a dictionary that was created as a result of serialization you can use __post_serialize__ method:

@dataclass
class A(DataClassJSONMixin):
    user: str
    password: str

    def __post_serialize__(self, d: Dict[Any, Any]) -> Dict[Any, Any]:
        d.pop('password')
        return d

obj = DataClass(user="name", password="secret")
print(obj.to_dict())  # {"user": "name"}
print(obj.to_json())  # '{"user": "name"}'

Note that you can add an additional context argument using the corresponding code generation option.

JSON Schema

You can build JSON Schema not only for dataclasses but also for any other supported data types. There is support for the following standards:

Building JSON Schema

For simple one-time cases it's recommended to start from using a configurable build_json_schema function. It returns JSONSchema object that can be serialized to json or to dict:

from dataclasses import dataclass, field
from typing import List
from uuid import UUID

from mashumaro.jsonschema import build_json_schema


@dataclass
class User:
    id: UUID
    name: str = field(metadata={"description": "User name"})


print(build_json_schema(List[User]).to_json())

Click to show the result

{
    "type": "array",
    "items": {
        "type": "object",
        "title": "User",
        "properties": {
            "id": {
                "type": "string",
                "format": "uuid"
            },
            "name": {
                "type": "string",
                "description": "User name"
            }
        },
        "additionalProperties": false,
        "required": [
            "id",
            "name"
        ]
    }
}

Additional validation keywords (see below) can be added using annotations:

from typing import Annotated, List
from mashumaro.jsonschema import build_json_schema
from mashumaro.jsonschema.annotations import Maximum, MaxItems

print(
    build_json_schema(
        Annotated[
            List[Annotated[int, Maximum(42)]],
            MaxItems(4)
        ]
    ).to_json()
)

Click to show the result

{
    "type": "array",
    "items": {
        "type": "integer",
        "maximum": 42
    },
    "maxItems": 4
}

The $schema keyword can be added by setting with_dialect_uri to True:

print(build_json_schema(str, with_dialect_uri=True).to_json())

Click to show the result

{
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "type": "string"
}

By default, Draft 2022-12 dialect is being used, but you can change it to another one by setting dialect parameter:

from mashumaro.jsonschema import OPEN_API_3_1

print(
    build_json_schema(
        str, dialect=OPEN_API_3_1, with_dialect_uri=True
    ).to_json()
)

Click to show the result

{
    "$schema": "https://spec.openapis.org/oas/3.1/dialect/base",
    "type": "string"
}

All dataclass JSON Schemas can or can not be placed in the definitions section, depending on the all_refs parameter, which default value comes from a dialect used (False for Draft 2022-12, True for OpenAPI Specification 3.1.1):

print(build_json_schema(List[User], all_refs=True).to_json())

Click to show the result

{
    "type": "array",
    "$defs": {
        "User": {
            "type": "object",
            "title": "User",
            "properties": {
                "id": {
                    "type": "string",
                    "format": "uuid"
                },
                "name": {
                    "type": "string"
                }
            },
            "additionalProperties": false,
            "required": [
                "id",
                "name"
            ]
        }
    },
    "items": {
        "$ref": "#/$defs/User"
    }
}

The definitions section can be omitted from the final document by setting with_definitions parameter to False:

print(
    build_json_schema(
        List[User], dialect=OPEN_API_3_1, with_definitions=False
    ).to_json()
)

Click to show the result

{
    "type": "array",
    "items": {
        "$ref": "#/components/schemas/User"
    }
}

Reference prefix can be changed by using ref_prefix parameter:

print(
    build_json_schema(
        List[User],
        all_refs=True,
        with_definitions=False,
        ref_prefix="#/components/responses",
    ).to_json()
)

Click to show the result

{
    "type": "array",
    "items": {
        "$ref": "#/components/responses/User"
    }
}

The omitted definitions could be found later in the Context object that you could have created and passed to the function, but it could be easier to use JSONSchemaBuilder for that. For example, you might found it handy to build OpenAPI Specification step by step passing your models to the builder and get all the registered definitions later. This builder has reasonable defaults but can be customized if necessary.

from mashumaro.jsonschema import JSONSchemaBuilder, OPEN_API_3_1

builder = JSONSchemaBuilder(OPEN_API_3_1)

@dataclass
class User:
    id: UUID
    name: str

@dataclass
class Device:
    id: UUID
    model: str

print(builder.build(List[User]).to_json())
print(builder.build(List[Device]).to_json())
print(builder.get_definitions().to_json())

Click to show the result

{
    "type": "array",
    "items": {
        "$ref": "#/components/schemas/User"
    }
}

{
    "type": "array",
    "items": {
        "$ref": "#/components/schemas/Device"
    }
}

{
    "User": {
        "type": "object",
        "title": "User",
        "properties": {
            "id": {
                "type": "string",
                "format": "uuid"
            },
            "name": {
                "type": "string"
            }
        },
        "additionalProperties": false,
        "required": [
            "id",
            "name"
        ]
    },
    "Device": {
        "type": "object",
        "title": "Device",
        "properties": {
            "id": {
                "type": "string",
                "format": "uuid"
            },
            "model": {
                "type": "string"
            }
        },
        "additionalProperties": false,
        "required": [
            "id",
            "model"
        ]
    }
}

JSON Schema constraints

Apart from required keywords, that are added automatically for certain data types, you're free to use additional validation keywords. They're presented by the corresponding classes in mashumaro.jsonschema.annotations:

Number constraints:

String constraints:

Array constraints:

Object constraints:

JSON Schema plugins

If the built-in functionality doesn't meet your needs, you can customize the JSON Schema generation or add support for additional types using plugins. The mashumaro.jsonschema.plugins.BasePlugin class provides a get_schema method that you can override to implement custom behavior.

The plugin system works by iterating through all registered plugins and calling their get_schema methods. If a plugin's get_schema method raises a NotImplementedError or returns None, it indicates that the plugin doesn't provide the required functionality for that particular case.

You can apply multiple plugins sequentially, allowing each to modify the schema in turn. This approach enables a step-by-step transformation of the schema, with each plugin contributing its specific modifications.

Plugins can be registered using the plugins argument in either the build_json_schema function or the JSONSchemaBuilder class.

The mashumaro.jsonschema.plugins module contains several built-in plugins. Currently, one of these plugins adds descriptions to JSON schemas using docstrings from dataclasses:

from dataclasses import dataclass

from mashumaro.jsonschema import build_json_schema
from mashumaro.jsonschema.plugins import DocstringDescriptionPlugin


@dataclass
class MyClass:
    """My class"""

    x: int


schema = build_json_schema(MyClass, plugins=[DocstringDescriptionPlugin()])
print(schema.to_json())

Click to show the result

{
    "type": "object",
    "title": "MyClass",
    "description": "My class",
    "properties": {
        "x": {
            "type": "integer"
        }
    },
    "additionalProperties": false,
    "required": [
        "x"
    ]
}

Creating your own custom plugin is straightforward. For instance, if you want to add support for Pydantic models, you could write a plugin similar to the following:

from dataclasses import dataclass

from pydantic import BaseModel

from mashumaro.jsonschema import build_json_schema
from mashumaro.jsonschema.models import Context, JSONSchema
from mashumaro.jsonschema.plugins import BasePlugin
from mashumaro.jsonschema.schema import Instance


class PydanticSchemaPlugin(BasePlugin):
    def get_schema(
        self,
        instance: Instance,
        ctx: Context,
        schema: JSONSchema | None = None,
    ) -> JSONSchema | None:
        try:
            if issubclass(instance.type, BaseModel):
                pydantic_schema = instance.type.model_json_schema()
                return JSONSchema.from_dict(pydantic_schema)
        except TypeError:
            return None


class MyPydanticClass(BaseModel):
    x: int


@dataclass
class MyDataClass:
    y: MyPydanticClass


schema = build_json_schema(MyDataClass, plugins=[PydanticSchemaPlugin()])
print(schema.to_json())

Click to show the result

{
    "type": "object",
    "title": "MyDataClass",
    "properties": {
        "y": {
            "type": "object",
            "title": "MyPydanticClass",
            "properties": {
                "x": {
                    "type": "integer",
                    "title": "X"
                }
            },
            "required": [
                "x"
            ]
        }
    },
    "additionalProperties": false,
    "required": [
        "y"
    ]
}

Extending JSON Schema

Using a Config class it is possible to override some parts of the schema. Currently, you can do the following:

override some field schemas using the "properties" key
change additionalProperties using the "additionalProperties" key

from dataclasses import dataclass
from mashumaro.jsonschema import build_json_schema

@dataclass
class FooBar:
    foo: str
    bar: int

    class Config:
        json_schema = {
            "properties": {
                "foo": {
                    "type": "string",
                    "description": "bar"
                }
            },
            "additionalProperties": True,
        }

print(build_json_schema(FooBar).to_json())

Click to show the result

{
    "type": "object",
    "title": "FooBar",
    "properties": {
        "foo": {
            "type": "string",
            "description": "bar"
        },
        "bar": {
            "type": "integer"
        }
    },
    "additionalProperties": true,
    "required": [
        "foo",
        "bar"
    ]
}

You can also change the "additionalProperties" key to a specific schema by passing it a JSONSchema instance instead of a bool value.

JSON Schema and custom serialization methods

Mashumaro provides different ways to override default serialization methods for dataclass fields or specific data types. In order for these overrides to be reflected in the schema, you need to make sure that the methods have annotations of the return value type.

from dataclasses import dataclass, field
from mashumaro.config import BaseConfig
from mashumaro.jsonschema import build_json_schema

def str_as_list(s: str) -> list[str]:
    return list(s)

def int_as_str(i: int) -> str:
    return str(i)

@dataclass
class FooBar:
    foo: str = field(metadata={"serialize": str_as_list})
    bar: int

    class Config(BaseConfig):
        serialization_strategy = {
            int: {
                "serialize": int_as_str
            }
        }

print(build_json_schema(FooBar).to_json())

Click to show the result

{
    "type": "object",
    "title": "FooBar",
    "properties": {
        "foo": {
            "type": "array",
            "items": {
                "type": "string"
            }
        },
        "bar": {
            "type": "string"
        }
    },
    "additionalProperties": false,
    "required": [
        "foo",
        "bar"
    ]
}

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mashumaro 3.15

Navigation

Verified details

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Unverified details

Project links

Meta

Classifiers

Project description

Fast and well tested serialization library

Table of contents

Introduction

Installation

Changelog

Supported data types

Usage example

How does it work?

Benchmark

Supported serialization formats

Basic form

JSON

json library

orjson library

YAML

TOML

MessagePack

Customization

SerializableType interface

User-defined types

User-defined generic types

SerializationStrategy

Third-party types

Third-party generic types

Field options

serialize option

deserialize option

serialization_strategy option

alias option

Config options

debug config option

code_generation_options config option

serialization_strategy config option

aliases config option

serialize_by_alias config option

allow_deserialization_not_by_alias config option

omit_none config option

omit_default config option

namedtuple_as_dict config option

allow_postponed_evaluation config option

dialect config option

orjson_options config option

discriminator config option

lazy_compilation config option

sort_keys config option

forbid_extra_keys config option

Passing field values as is

Extending existing types

Field aliases

Dialects

serialization_strategy dialect option

serialize_by_alias dialect option

omit_none dialect option

omit_default dialect option

namedtuple_as_dict dialect option

no_copy_collections dialect option

Changing the default dialect

Discriminator

Subclasses distinguishable by a field

Subclasses without a common field

Class level discriminator

Working with union of classes

Using a custom variant tagger function

Code generation options

Add omit_none keyword argument

Add by_alias keyword argument

Add dialect keyword argument

Add context keyword argument

Generic dataclasses

Generic dataclass inheritance

Generic dataclass in a field type

`serialize` option

`deserialize` option

`serialization_strategy` option

`alias` option

`debug` config option

`code_generation_options` config option

`serialization_strategy` config option

`aliases` config option

`serialize_by_alias` config option

`allow_deserialization_not_by_alias` config option

`omit_none` config option

`omit_default` config option

`namedtuple_as_dict` config option

`allow_postponed_evaluation` config option

`dialect` config option

`orjson_options` config option

`discriminator` config option

`lazy_compilation` config option

`sort_keys` config option

`forbid_extra_keys` config option

`serialization_strategy` dialect option

`serialize_by_alias` dialect option

`omit_none` dialect option

`omit_default` dialect option

`namedtuple_as_dict` dialect option

`no_copy_collections` dialect option

Add `omit_none` keyword argument

Add `by_alias` keyword argument

Add `dialect` keyword argument

Add `context` keyword argument