Define a single factory to generate the same data in multiple formats
Project description
multi_factory
Define a single factory to generate the same data in multiple formats:
- Base (original data as defined on the factory)
- JSON (original data converted into a Python dict that is JSON serialisable)
- Domain (JSON data that is passed through a
marshmallowschema that validates it and converts it into a domain object like a@dataclass)
Installation
multi_factory can be installed using pip (requires Python >=3.10):
pip install multi-factory
Quick start
Imagine you have the following code to represent a User in your application:
from typing import Any
from enum import Enum
from uuid import UUID
from datetime import datetime
from dataclasses import dataclass
from marshmallow import Schema, fields, post_load
class Gender(Enum):
MALE = 1
FEMALE = 2
OTHER = 3
class UserSchema(Schema):
id = fields.UUID()
first_name = fields.String()
last_name = fields.String()
age = fields.Integer()
birthday = fields.DateTime()
gender = fields.Enum(Gender)
@post_load
def to_domain(self, incoming_data: dict[str, Any], **kwargs: Any) -> User:
return User(**incoming_data)
@dataclass
class User:
id: UUID
first_name: str
last_name: str
age: int
birthday: datetime
gender: Gender
The above code will be used in a POST /users HTTP API endpoint, where the request body will contain a JSON representation of the User class that will need
to be validated and de-serialized by the UserSchema class. The UserSchema class will also pass the validated data into the User class so it is easier to
use and pass around your application.
To be able to test this POST /users endpoint, you will need to define factories to generate data for this User class in multiple formats for use in tests.
You write the following code to define multiple independent factories to achieve this:
import factory
from uuid import uuid4
class UserDictFactory(factory.Factory):
class Meta:
model = dict
id = uuid4()
first_name = "Bob"
last_name = "Dylan"
age = 21
birthday = datetime(year=2000, month=1, day=1, hour=0)
gender = Gender.MALE
class UserJSONFactory(factory.Factory):
class Meta:
model = dict
id = str(uuid4())
first_name = "Bob"
last_name = "Dylan"
age = 21
birthday = datetime(year=2000, month=1, day=1, hour=0).isoformat()
gender = Gender.MALE.name
class UserDomainFactory(factory.Factory):
class Meta:
model = User
id = uuid4()
first_name = "Bob"
last_name = "Dylan"
age = 21
birthday = datetime(year=2000, month=1, day=1, hour=0)
gender = Gender.MALE
Having to write 3 factories here adds more work that what should be necessary. It also increases the risk of these 3 factories getting out of sync if a change is made to one of them and not the other 2.
With multi-factory, we can do the following instead:
from multi_factory import JSONToDomainFactory
class UserFactory(JSONToDomainFactory[User, UserSchema]):
id = uuid4()
first_name = "Bob"
last_name = "Dylan"
age = 21
birthday = datetime(year=2000, month=1, day=1, hour=0)
gender = Gender.MALE
This UserFactory combines the 3 factories above into a single factory. This means you only need to define the test data once, which requires less maintenance
that having the same data defined in multiple factories.
When you invoke this UserFactory you will have access to the 3 different formats for the same data:
>>> result = UserFactory()
>>> result
JSONToDomainFactoryResult(
base={
'id': UUID('96a43fc4-069a-4882-a388-24033299496f'),
'first_name': 'Bob',
'last_name': 'Dylan',
'age': 21,
'birthday': datetime.datetime(2000, 1, 1, 0, 0),
'gender': <Gender.MALE: 1>
},
json={
'id': '96a43fc4-069a-4882-a388-24033299496f',
'first_name': 'Bob',
'last_name': 'Dylan',
'age': 21,
'birthday': '2000-01-01T00:00:00',
'gender': 'MALE'
},
domain=User(
id=UUID('96a43fc4-069a-4882-a388-24033299496f'),
first_name='Bob',
last_name='Dylan',
age=21,
birthday=datetime.datetime(2000, 1, 1, 0, 0),
gender=<Gender.MALE: 1>
)
)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file multi_factory-0.2.1.tar.gz.
File metadata
- Download URL: multi_factory-0.2.1.tar.gz
- Upload date:
- Size: 9.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.12.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
081cc2e185cd8d6146e848e9c17e8e28ee6787f9c522d20a6ace93f852b66910
|
|
| MD5 |
ab4f726ab31421d4688f795ccba413b1
|
|
| BLAKE2b-256 |
517217db7f17f47d5ce0bef362ba7f2531302e4dc1ab1d3156ad0169a4d4680a
|
File details
Details for the file multi_factory-0.2.1-py3-none-any.whl.
File metadata
- Download URL: multi_factory-0.2.1-py3-none-any.whl
- Upload date:
- Size: 10.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.12.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
80b04b9dd1080056d32a72b9d9b9fb8f26a1f656469dffa6baaa780df07071ab
|
|
| MD5 |
7d1f4c760ba66fe3d28ee2a39415f08b
|
|
| BLAKE2b-256 |
27a20d927c939b76cae79ce0fcf01797f2ae5e16d0d830d972533d11d34cfca1
|