Fast serialization framework on top of dataclasses

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Project description

mashumaro (マシュマロ)

mashumaro is a fast and well tested serialization framework on top of dataclasses.

When using dataclasses, you often need to dump and load objects according to the described scheme. This framework not only adds this ability to serialize in different formats, but also makes serialization rapidly.

Installation
Supported serialization formats
Supported field types
Usage example
How does it work?
Benchmark
API
Customization

Installation

Use pip to install:

$ pip install mashumaro

Supported serialization formats

This framework adds methods for dumping to and loading from the following formats:

Plain dict can be useful when you need to pass a dict object to a third-party library, such as a client for MongoDB.

Supported field 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 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 specific types like:

NoneType
user-defined classes
user-defined generic types

Usage example

from enum import Enum
from typing import List
from dataclasses import dataclass
from mashumaro import DataClassJSONMixin

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

@dataclass
class CurrencyPosition(DataClassJSONMixin):
    currency: Currency
    balance: float

@dataclass
class StockPosition(DataClassJSONMixin):
    ticker: str
    name: str
    balance: int

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

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

json_string = my_portfolio.to_json()
Portfolio.from_json(json_string)  # same as my_portfolio

How does it work?

This framework works by taking the schema of the data and generating a specific parser and builder for exactly that schema. This is much faster than inspection of field types on every call of parsing or building at runtime.

Benchmark

macOS 11.5.2 Big Sur
Apple M1
16GB RAM
Python 3.9.1

Load and dump sample data 1.000 times in 5 runs. The following figures show the best overall time in each case.

Framework	From dict	To dict
Time	Slowdown factor	Time	Slowdown factor
mashumaro	0.04096	1x	0.02741	1x
cattrs	0.07307	1.78x	0.05062	1.85x
pydantic	0.24847	6.07x	0.12292	4.48x
marshmallow	0.29205	7.13x	0.09310	3.4x
dataclasses	—	—	0.22583	8.24x
dacite	0.91553	22.35x	—	—

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
python benchmark/run.py

API

Mashumaro provides a couple of mixins for each format.

`DataClassDictMixin.to_dict(use_bytes: bool, use_enum: bool, use_datetime: bool)`

Make a dictionary from dataclass object based on the dataclass schema provided. Options include:

use_bytes: False     # False - convert bytes/bytearray objects to base64 encoded string, True - keep untouched
use_enum: False      # False - convert enum objects to enum values, True - keep untouched
use_datetime: False  # False - convert datetime oriented objects to ISO 8601 formatted string, True - keep untouched

`DataClassDictMixin.from_dict(data: Mapping, use_bytes: bool, use_enum: bool, use_datetime: bool)`

Make a new object from dict object based on the dataclass schema provided. Options include:

use_bytes: False     # False - load bytes/bytearray objects from base64 encoded string, True - keep untouched
use_enum: False      # False - load enum objects from enum values, True - keep untouched
use_datetime: False  # False - load datetime oriented objects from ISO 8601 formatted string, True - keep untouched

`DataClassJSONMixin.to_json(encoder: Optional[Encoder], dict_params: Optional[Mapping], **encoder_kwargs)`

Make a JSON formatted string from dataclass object based on the dataclass schema provided. Options include:

encoder        # function called for json encoding, defaults to json.dumps
dict_params    # dictionary of parameter values passed underhood to `to_dict` function
encoder_kwargs # keyword arguments for encoder function

`DataClassJSONMixin.from_json(data: Union[str, bytes, bytearray], decoder: Optional[Decoder], dict_params: Optional[Mapping], **decoder_kwargs)`

Make a new object from JSON formatted string based on the dataclass schema provided. Options include:

decoder        # function called for json decoding, defaults to json.loads
dict_params    # dictionary of parameter values passed underhood to `from_dict` function
decoder_kwargs # keyword arguments for decoder function

`DataClassMessagePackMixin.to_msgpack(encoder: Optional[Encoder], dict_params: Optional[Mapping], **encoder_kwargs)`

Make a MessagePack formatted bytes object from dataclass object based on the dataclass schema provided. Options include:

encoder        # function called for MessagePack encoding, defaults to msgpack.packb
dict_params    # dictionary of parameter values passed underhood to `to_dict` function
encoder_kwargs # keyword arguments for encoder function

`DataClassMessagePackMixin.from_msgpack(data: Union[str, bytes, bytearray], decoder: Optional[Decoder], dict_params: Optional[Mapping], **decoder_kwargs)`

Make a new object from MessagePack formatted data based on the dataclass schema provided. Options include:

decoder        # function called for MessagePack decoding, defaults to msgpack.unpackb
dict_params    # dictionary of parameter values passed underhood to `from_dict` function
decoder_kwargs # keyword arguments for decoder function

`DataClassYAMLMixin.to_yaml(encoder: Optional[Encoder], dict_params: Optional[Mapping], **encoder_kwargs)`

Make an YAML formatted bytes object from dataclass object based on the dataclass schema provided. Options include:

encoder        # function called for YAML encoding, defaults to yaml.dump
dict_params    # dictionary of parameter values passed underhood to `to_dict` function
encoder_kwargs # keyword arguments for encoder function

`DataClassYAMLMixin.from_yaml(data: Union[str, bytes], decoder: Optional[Decoder], dict_params: Optional[Mapping], **decoder_kwargs)`

Make a new object from YAML formatted data based on the dataclass schema provided. Options include:

decoder        # function called for YAML decoding, defaults to yaml.safe_load
dict_params    # dictionary of parameter values passed underhood to `from_dict` function
decoder_kwargs # keyword arguments for decoder function

Customization

SerializableType interface

If you already have a separate custom class, and you want to serialize instances of it with mashumaro, you can achieve this by implementing SerializableType interface:

from typing import Dict
from datetime import datetime
from dataclasses import dataclass
from mashumaro import DataClassDictMixin
from mashumaro.types import SerializableType

class DateTime(datetime, SerializableType):
    def _serialize(self) -> Dict[str, int]:
        return {
            "year": self.year,
            "month": self.month,
            "day": self.day,
            "hour": self.hour,
            "minute": self.minute,
            "second": self.second,
        }

    @classmethod
    def _deserialize(cls, value: Dict[str, int]) -> 'DateTime':
        return DateTime(
            year=value['year'],
            month=value['month'],
            day=value['day'],
            hour=value['hour'],
            minute=value['minute'],
            second=value['second'],
        )


@dataclass
class Holiday(DataClassDictMixin):
    when: DateTime = DateTime.now()


new_year = Holiday(when=DateTime(2019, 1, 1, 12))
dictionary = new_year.to_dict()
# {'x': {'year': 2019, 'month': 1, 'day': 1, 'hour': 0, 'minute': 0, 'second': 0}}
assert Holiday.from_dict(dictionary) == new_year

If you have a custom generic type and are looking for a generic version of such an interface, read this.

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 usedataclasses.field function as a default field value to configure some serialization aspects through its metadata parameter. Next section describes all supported options to use in metadata mapping.

`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 field type that this option is used with. At this moment there are next serialization engines to choose from:

Applicable field 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.

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 field type that this option is used with. At this moment there are next deserialization engines to choose from:

Applicable field 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.

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 behaviour for a class depending on some defined parameters. For this case you can create the special class implementing SerializationStrategy interface:

from dataclasses import dataclass, field
from datetime import datetime
from mashumaro import DataClassDictMixin
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={
            "serialization_strategy": FormattedDateTime(
                fmt="%d%m%Y%H%M%S",
            )
        }
    )
    verbose: datetime = field(
        metadata={
            "serialization_strategy": FormattedDateTime(
                fmt="%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

`alias` option

In some cases it's better to have different names for a field in your class 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 even you want to load data with keys that are invalid identifiers in Python. This problem is easily solved by using 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)
x.to_dict()  # {"a": 1, "b": 2}  # no aliases on serialization by default

If you want to write all the field aliases in one place there is such a config option.

If you want to serialize all the fields by aliases you have two options to do so:

serialize_by_alias config option
by_alias keyword argument in to_dict method

It's hard to imagine when it might be necessary to serialize only specific fields by alias, but such functionality is easily added to the library. Open the issue if you need it.

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 DataClassDictMixin, field_options

@dataclass
class A(DataClassDictMixin):
    x: int = field(
        metadata=field_options(
            serialize=str,
            deserialize=int,
            ...
        )
    )

More options are on the way. If you know which option would be useful for many, please don't hesitate to create an issue or pull request.

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_dict` method.
`TO_DICT_ADD_BY_ALIAS_FLAG`	Adds `by_alias` keyword-only arguments to `to_dict` method.

`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):

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

`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 when this option is enabled. The more flexible but less fast way to do the same is using 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}

`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_dict and to_dict 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.

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_dict method using the code_generation_options list:

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_dict method using the code_generation_options list. On the other hand if serialization by alias is always needed, the best solution is to use the serialize_by_alias config option.

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}

Keep in mind, if you're serializing data in JSON or another format, then you need to pass by_alias argument to dict_params dictionary.

User-defined generic types

There is support for user-defined generic types. You can inherit generic dataclasses along with overwriting types in them, use generic dataclasses as field types, or create your own generic types with serialization under your control.

User-defined generic dataclasses

If you have a generic version of a dataclass and want to serialize and deserialize its instances depending on the concrete types, you can achieve this using inheritance:

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

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

@dataclass
class GenericDataClass(Generic[KT, VT]):
    x: Mapping[KT, VT]

@dataclass
class ConcreteDataClass(GenericDataClass[str, date], DataClassDictMixin):
    pass

ConcreteDataClass.from_dict({"x": {"a": "2021-01-01"}})          # ok
ConcreteDataClass.from_dict({"x": {"a": "not a date but str"}})  # error

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 dataclasses as field types

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 serialize and deserialize instances of generic types depending on the types provided:

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

KT = TypeVar("KT", int, str)
VT = TypeVar("VT", int, str)

class GenericDict(Dict[KT, VT], GenericSerializableType):
    def _serialize(self, types) -> Dict[KT, VT]:
        k_type, v_type = types
        if k_type not in (int, str) or v_type not in (int, str):
            raise TypeError
        return {k_type(k): v_type(v) for k, v in self.items()}

    @classmethod
    def _deserialize(cls, value, types) -> 'GenericDict[KT, VT]':
        k_type, v_type = types
        if k_type not in (int, str) or v_type not in (int, str):
            raise TypeError
        return cls({k_type(k): v_type(v) for k, v in value.items()})

@dataclass
class DataClass(DataClassDictMixin):
    x: GenericDict[int, str]
    y: GenericDict[str, int]

instance = DataClass(GenericDict({1: 'a'}), GenericDict({'b': 2}))
dictionary = instance.to_dict()  # {'x': {1: 'a'}, 'y': {'b': 2}}
assert DataClass.from_dict(dictionary) == instance

The difference between SerializableType and GenericSerializableType is that the methods of GenericSerializableType have a parameter types, to which the concrete types will be passed. If you don't need this information you can still use SerializableType interface even with generic types.

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

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"}'

TODO

add optional validation
write custom useful types such as URL, Email etc

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mashumaro 2.9.1

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Project links

Meta

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Project description

mashumaro (マシュマロ)

Table of contents

Installation

Supported serialization formats

Supported field types

Usage example

How does it work?

Benchmark

API

DataClassDictMixin.to_dict(use_bytes: bool, use_enum: bool, use_datetime: bool)

DataClassDictMixin.from_dict(data: Mapping, use_bytes: bool, use_enum: bool, use_datetime: bool)

DataClassJSONMixin.to_json(encoder: Optional[Encoder], dict_params: Optional[Mapping], **encoder_kwargs)

DataClassJSONMixin.from_json(data: Union[str, bytes, bytearray], decoder: Optional[Decoder], dict_params: Optional[Mapping], **decoder_kwargs)

DataClassMessagePackMixin.to_msgpack(encoder: Optional[Encoder], dict_params: Optional[Mapping], **encoder_kwargs)

DataClassMessagePackMixin.from_msgpack(data: Union[str, bytes, bytearray], decoder: Optional[Decoder], dict_params: Optional[Mapping], **decoder_kwargs)

DataClassYAMLMixin.to_yaml(encoder: Optional[Encoder], dict_params: Optional[Mapping], **encoder_kwargs)

DataClassYAMLMixin.from_yaml(data: Union[str, bytes], decoder: Optional[Decoder], dict_params: Optional[Mapping], **decoder_kwargs)

Customization

SerializableType interface

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

namedtuple_as_dict config option

allow_postponed_evaluation config option

Code generation options

Add omit_none keyword argument

Add by_alias keyword argument

User-defined generic types

User-defined generic dataclasses

Generic dataclasses as field types

GenericSerializableType interface

Serialization hooks

Before deserialization

After deserialization

Before serialization

After serialization

TODO

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`DataClassDictMixin.to_dict(use_bytes: bool, use_enum: bool, use_datetime: bool)`

`DataClassDictMixin.from_dict(data: Mapping, use_bytes: bool, use_enum: bool, use_datetime: bool)`

`DataClassJSONMixin.to_json(encoder: Optional[Encoder], dict_params: Optional[Mapping], **encoder_kwargs)`

`DataClassJSONMixin.from_json(data: Union[str, bytes, bytearray], decoder: Optional[Decoder], dict_params: Optional[Mapping], **decoder_kwargs)`

`DataClassMessagePackMixin.to_msgpack(encoder: Optional[Encoder], dict_params: Optional[Mapping], **encoder_kwargs)`

`DataClassMessagePackMixin.from_msgpack(data: Union[str, bytes, bytearray], decoder: Optional[Decoder], dict_params: Optional[Mapping], **decoder_kwargs)`

`DataClassYAMLMixin.to_yaml(encoder: Optional[Encoder], dict_params: Optional[Mapping], **encoder_kwargs)`

`DataClassYAMLMixin.from_yaml(data: Union[str, bytes], decoder: Optional[Decoder], dict_params: Optional[Mapping], **decoder_kwargs)`

`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

`namedtuple_as_dict` config option

`allow_postponed_evaluation` config option

Add `omit_none` keyword argument

Add `by_alias` keyword argument