django-dataclassconf 0.1.0

A simple Django package setting loader that utilizes dataclasses for type hinting and type checking

Django DataclassConf

A simple Django package setting loader that utilizes dataclasses for type hinting and type checking.

Bring modern Python typing, robust validation, and full IDE autocomplete to your Django configurations.

django-dataclassconf allows you to bind your Django settings cleanly to structured standard Python dataclasses. Powered by dacite, it automatically catches dynamic setting updates while ensuring your configuration layer stays type-safe and isolated.

But Why?

• Fail-Fast Validation: Catch bad configuration types or missing values instantly during server startup or container deployment rather than hitting silent runtime crashes mid-request.

• Full IDE Autocomplete: Say goodbye to blind getattr(settings, "MY_SETTING") calls. Enjoy full hovering type definitions and autocompletion in VS Code, PyCharm, and MyPy.

• Dual Format Normalization: Merges flat environment styles (PREFIX_TIMEOUT = 30) and structured dictionary blocks (PREFIX = {"TIMEOUT": 30}) into a single unified object seamlessly.

• Test-Safe Isolation: Fully supports Django's test suite cycles. When settings are overridden dynamically in unit tests, your dataclasses mutate cleanly in-place and revert automatically.

Usage

1. Define Your Configuration Dataclass

Create a file named config.py (or any name you prefer tbh) within your application or package. Inherit from BaseConfig and define your variables.

from dataclasses import dataclass, field
from django_dataclassconf.config import BaseConfig, config_loader

@dataclass
class MyPackageConfig(BaseConfig):
    DOCUMENTS_ROOT_PATH: str = '/the/default/path/'
    MAX_FILE_SIZE: int = 10000
    INDEXER_CLASS: str = 'myapp.utils.DocumentIndexer'

    @property
    def _prefix(self) -> str:
        """
        Define the config's prefix here, return blank string if it doesn't 
        have a prefix such as when we write a configuration dataclass that 
        will hold the `DEBUG` setting
        """
        return 'MY_PACKAGE'

package_config = MyPackageConfig()

2. Subscribe to the Configuration Loader

For your dataclass to grab configuration data from Django's settings.py on startup and capture updates during tests, subscribe your instance into config_loader inside your app's initialization hook:

# my_app/apps.py
class MyAppConfig(AppConfig):
    name = 'my_app'
    
    def ready(self):
        from django_dataclassconf.config import config_loader
        from .config import package_config

        config_loader.subscribe(package_config)

3. Access Your Settings Anywhere

Core Practice would be to import your configuration instance directly instead of using the global django.conf.settings object to harness the full type safety and IDE autocomplete.

# my_app/views.py
from django.http import HttpResponse
from my_app.config import package_config

def my_view(request):
    ...
    # Your IDE now natively autocompletes these fields
    if file_size > package_config.MAX_FILE_SIZE:
        return HttpResponse(
            {'detail': 'File size has exceeded the maximum size limit!'}, 
            status = 400
        )

Extras

Your Configuration Dataclass can also be a nested dataclasses, you only need to inherit BaseConfig to the root configuration dataclass.

from dataclasses import dataclass, field
from django_dataclassconf.config import BaseConfig, config_loader

@dataclass
class DocumentPreview:
    preview_page_count: int = 10
    strip_cover_page: bool = False

@dataclass
class MyPackageConfig(BaseConfig):
    DOCUMENTS_ROOT_PATH: str = '/the/default/path/'
    PREVIEW: DocumentPreview = field(default_factory=DocumentPreview)

    @property
    def _prefix(self) -> str:
        return 'MY_PACKAGE'

configuration = MyPackageConfig()
config_loader.subscribe(configuration)

And it will look like this in settings.py

MY_PACKAGE = {
    'DOCUMENTS_ROOT_PATH': '/custom/path/',
    'PREVIEW': {
        'preview_page_count': 12,
        'strip_cover_page': True
    },
}

License

This project is licensed under the MIT License - see the LICENSE file for details.