dependency-injector-fg 4.44.1

Dependency injection framework for Python - FORK by gortibaldik

This is a fork of Dependency Injector library

Everything below is written and belongs to Roman Mogylatov who was maintaining the library until 2022. This fork fixes some issues that we found during the usage of the library.

What is Dependency Injector?

Dependency Injector is a dependency injection framework for Python.

It helps implement the dependency injection principle.

Key features of the Dependency Injector:

• Providers. Provides Factory, Singleton, Callable, Coroutine, Object, List, Dict, Configuration, Resource, Dependency, and Selector providers that help assemble your objects. See Providers.

• Overriding. Can override any provider by another provider on the fly. This helps in testing and configuring dev/stage environment to replace API clients with stubs etc. See Provider overriding.

• Configuration. Reads configuration from yaml, ini, and json files, pydantic settings, environment variables, and dictionaries. See Configuration provider.

• Resources. Helps with initialization and configuring of logging, event loop, thread or process pool, etc. Can be used for per-function execution scope in tandem with wiring. See Resource provider.

• Containers. Provides declarative and dynamic containers. See Containers.

• Wiring. Injects dependencies into functions and methods. Helps integrate with other frameworks: Django, Flask, Aiohttp, Sanic, FastAPI, etc. See Wiring.

• Asynchronous. Supports asynchronous injections. See Asynchronous injections.

• Typing. Provides typing stubs, mypy-friendly. See Typing and mypy.

• Performance. Fast. Written in Cython.

• Maturity. Mature and production-ready. Well-tested, documented, and supported.

from dependency_injector import containers, providers
from dependency_injector.wiring import Provide, inject


class Container(containers.DeclarativeContainer):

    config = providers.Configuration()

    api_client = providers.Singleton(
        ApiClient,
        api_key=config.api_key,
        timeout=config.timeout,
    )

    service = providers.Factory(
        Service,
        api_client=api_client,
    )


@inject
def main(service: Service = Provide[Container.service]) -> None:
    ...


if __name__ == "__main__":
    container = Container()
    container.config.api_key.from_env("API_KEY", required=True)
    container.config.timeout.from_env("TIMEOUT", as_=int, default=5)
    container.wire(modules=[__name__])

    main()  # <-- dependency is injected automatically

    with container.api_client.override(mock.Mock()):
        main()  # <-- overridden dependency is injected automatically

When you call the main() function the Service dependency is assembled and injected automatically.

When you do testing, you call the container.api_client.override() method to replace the real API client with a mock. When you call main(), the mock is injected.

You can override any provider with another provider.

It also helps you in a re-configuring project for different environments: replace an API client with a stub on the dev or stage.

With the Dependency Injector, object assembling is consolidated in a container. Dependency injections are defined explicitly. This makes it easier to understand and change how an application works.

Visit the docs to know more about the Dependency injection and inversion of control in Python.

Installation

The package is available on the PyPi:

pip install dependency-injector

Documentation

The documentation is available here.

Examples

Choose one of the following:

• Application example (single container)

• Application example (multiple containers)

• Decoupled packages example (multiple containers)

• Boto3 example

• Django example

• Flask example

• Aiohttp example

• Sanic example

• FastAPI example

• FastAPI + Redis example

• FastAPI + SQLAlchemy example

Tutorials

Choose one of the following:

• Flask web application tutorial

• Aiohttp REST API tutorial

• Asyncio monitoring daemon tutorial

• CLI application tutorial

Concept

The framework stands on the PEP20 (The Zen of Python) principle:

Explicit is better than implicit

You need to specify how to assemble and where to inject the dependencies explicitly.

The power of the framework is in its simplicity. Dependency Injector is a simple tool for the powerful concept.

Frequently asked questions

What is dependency injection?

• dependency injection is a principle that decreases coupling and increases cohesion

Why should I do the dependency injection?

• your code becomes more flexible, testable, and clear 😎

How do I start applying the dependency injection?

• you start writing the code following the dependency injection principle

• you register all of your application components and their dependencies in the container

• when you need a component, you specify where to inject it or get it from the container

What price do I pay and what do I get?

• you need to explicitly specify the dependencies

• it will be extra work in the beginning

• it will payoff as project grows

Have a question?

• Open a Github Issue

Found a bug?

• Open a Github Issue

Want to help?

• ⭐️ Star the Dependency Injector on the Github

• 🆕 Start a new project with the Dependency Injector

• 💬 Tell your friend about the Dependency Injector

Want to contribute?

• 🔀 Fork the project

• ⬅️ Open a pull request to the develop branch