Dataclass Wizard vs. Other De/Serialization Libraries!

perf-vs-apischema.py

# pip install apischema
# pip install dataclass-wizard

from dataclasses import dataclass, field
from timeit import timeit
from uuid import UUID, uuid4

from apischema import deserialize, serialize

from dataclass_wizard import JSONWizard


# Define a schema with standard dataclasses
@dataclass
class Resource:
    id: UUID
    name: str
    tags: set[str] = field(default_factory=set)


@dataclass
class Resource2(JSONWizard):
    class _(JSONWizard.Meta):
        # with upcoming `v1`, this will be even faster!
        # Follow for more: https://github.com/rnag/dataclass-wizard/discussions/165
        v1 = False

    id: UUID
    name: str
    tags: set[str] = field(default_factory=set)

# Get some data
uuid = uuid4()
data = {"id": str(uuid), "name": "wyfo", "tags": ["some_tag"]}


n = 100_000

print('apischema:         ', timeit('deserialize(Resource, data)', globals=globals(), number=n))
print('dataclass-wizard:  ', timeit('Resource2.from_dict(data)', globals=globals(), number=n))

# Deserialize data
resource = deserialize(Resource, data)
resource2 = Resource2.from_dict(data)

# Results (current):
# apischema:          0.2984417909756303
# dataclass-wizard:   0.1650990409834776

# Results (with upcoming `v1`):
# apischema:          0.31923512497451156
# dataclass-wizard:   0.11814916698494926

assert resource == Resource(uuid, "wyfo", {"some_tag"})
assert resource2 == Resource2(uuid, "wyfo", {"some_tag"})

# Serialize objects
assert serialize(Resource, resource) == data

perf-vs-dacite.py

from dataclasses import dataclass
from timeit import timeit

from dacite import from_dict
from dataclass_wizard import fromdict, LoadMeta
from pydantic.dataclasses import dataclass as py_dataclass


@dataclass
class User:
    name: str
    age: int
    is_active: bool


@py_dataclass
class UserModel:
    name: str
    age: int
    is_active: bool


data = {
    'name': 'john',
    'age': 30,
    'is_active': True,
}


n = 100_000

# setup: pip install dacite
# dacite:   0.303
# setup: git clone -b feature/performance-improvements https://github.com/konradhalas/dacite.git && cd dacite && pip install -e .
# dacite:   ??
print('dacite:            ', timeit('from_dict(data_class=User, data=data)',
                          globals=globals(), number=n))

# setup: pip install pydantic
# pydantic:  0.100
print('pydantic:          ', timeit('UserModel(**data)',
                           globals=globals(), number=n))

# setup: pip install dataclass-wizard

# With upcoming `v1`, the `dataclass-wizard` will be even faster!
# LoadMeta(v1=True).bind_to(User)

# dataclass-wizard:  0.075
print('dataclass-wizard:  ', timeit('fromdict(User, data)',
                           globals=globals(), number=n))


user = from_dict(data_class=User, data=data)
assert user == User(name='john', age=30, is_active=True)

user = UserModel(**data)
assert user == UserModel(name='john', age=30, is_active=True)

user = fromdict(User, data)
assert user == User(name='john', age=30, is_active=True)