mashumaro 1.23

Fast serialization framework on top of dataclasses

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.

Table of contents

• Installation
• Supported serialization formats
• Supported field types
• Usage example
• How does it work?
• API
• Customization
  • User defined classes
    • Serializable Interface
    • Serialization Strategy
  • Field options
    • serialize option
    • deserialize option
  • Serialization hooks
    • Before deserialization
    • After deserialization
    • Before serialization
    • After serialization

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
• json
• yaml
• msgpack

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:

• List
• Tuple
• Set
• FrozenSet
• Deque
• Dict
• Mapping
• MutableMapping
• ChainMap
• Sequence

for special primitives from the typing module:

• Optional
• Any

for enumerations based on classes from the standard enum module:

• Enum
• IntEnum
• Flag
• IntFlag

for common built-in types:

• int
• float
• bool
• str
• bytes
• bytearray

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

• datetime
• date
• time
• timedelta
• timezone

for pathlike types:

• PurePath
• Path
• PurePosixPath
• PosixPath
• PureWindowsPath
• WindowsPath
• os.PathLike

for other less popular built-in types:

• uuid.UUID
• decimal.Decimal
• fractions.Fraction
• ipaddress.IPv4Address
• ipaddress.IPv6Address
• ipaddress.IPv4Network
• ipaddress.IPv6Network
• ipaddress.IPv4Interface
• ipaddress.IPv6Interface

for specific types like NoneType, nested dataclasses itself and even user defined classes.

Usage example

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

class PetType(Enum):
    CAT = 'CAT'
    MOUSE = 'MOUSE'

@dataclass(unsafe_hash=True)
class Pet(DataClassJSONMixin):
    name: str
    age: int
    pet_type: PetType

@dataclass
class Person(DataClassJSONMixin):
    first_name: str
    second_name: str
    age: int
    pets: Set[Pet]


tom = Pet(name='Tom', age=5, pet_type=PetType.CAT)
jerry = Pet(name='Jerry', age=3, pet_type=PetType.MOUSE)
john = Person(first_name='John', second_name='Smith', age=18, pets={tom, jerry})

dump = john.to_json()
person = Person.from_json(dump)
# person == john

Pet.from_json('{"name": "Tom", "age": 5, "pet_type": "CAT"}')
# Pet(name='Tom', age=5, pet_type=<PetType.CAT: 'CAT'>)

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.

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

User defined classes

You can define and use custom classes with mashumaro. There are two options for customization.

Serializable Interface

The first one is useful when you already have the separate custom class and you want to serialize instances of it with mashumaro. All what you need is to implement 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

Serialization Strategy

The second 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 datetime import datetime
from dataclasses import dataclass
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: FormattedDateTime(fmt='%d%m%Y%H%M%S') = datetime.now()
    verbose: FormattedDateTime(fmt='%A %B %d, %Y, %H:%M:%S') = datetime.now()


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

⚠️ Since PEP-563 breaks SerializationStrategy, it will be implemented differently sometime in a future version 2.x.

Field options

In some cases creating a new class just for one little thing could be excessive. You can use dataclasses.field as a field value and configure some serialization aspects through its metadata argument. Next section describes all supported options to use in metadata mapping.

serialize option

This option allows you to change the default serialization method through a value of type Callable[[Any], Any] that could be any callable object like a function, a class method, a class instance method, an instance of a callable class or even a lambda function.

Example:

@dataclass
class A(DataClassDictMixin):
    dt: datetime = field(
        metadata={
            "serialize": lambda v: v.strftime('%Y-%m-%d %H:%M:%S')
        }
    )

deserialize option

This option allows you to change the default 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 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 deserialization.

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.

Example:

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

@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))
        }
    )

If you don't want to remember the names of the options you can use field_params 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.

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 Union support (try to match types on each call)
• write benchmarks
• add optional validation
• write custom useful types such as URL, Email etc
• write documentation