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Fatal1ty
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Fast and well tested serialization framework on top of dataclasses

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mashumaro (マシュマロ)

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

Build Status Coverage Status Latest Version Python Version License

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

Use pip to install:

shell
$ 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: *

List
*
Tuple
*
Set
*
FrozenSet
*
Deque
*
Dict
*
OrderedDict
*
Mapping
*
MutableMapping
*
Counter
*
ChainMap
*
Sequence

for special primitives from the typing module: *

Optional
*
Union
*
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:)

</pettype.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.

Benchmark

  • macOS 11.1 Big Sur
  • Apple M1
  • 16GB RAM

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 0.04114 1x 0.02729 1x
cattrs 0.06471 1.57x 0.04804 1.76x
pydantic 0.23675 5.75x 0.11420 4.18x
marshmallow 0.24702 6.0x 0.09430 3.46x
dataclasses 0.22787 8.35x
dacite 0.91061 22.13x

To run benchmark in your environment:

bash
git clone [email protected]: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:

python
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:

python
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]) -&gt; '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

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 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 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 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)) } )

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) -&gt; str:
    return value.strftime(self.fmt)

def deserialize(self, value: str) -&gt; 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) -&gt; str:
    return value.strftime(self.fmt)

def deserialize(self, value: str) -&gt; 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): field_a: int field_b: int

class Config(BaseConfig):
    aliases = {
        "field_a": "FieldA",
        "field_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}

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.

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]) -&gt; 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') -&gt; '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) -&gt; '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]) -&gt; 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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