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Dependency Injector
?
What is Dependency Injector
is a dependency injection framework for Python.
It helps implementing the dependency injection principle.
Key features of the Dependency Injector
:
-
Providers. Provides
Factory
,Singleton
,Callable
,Coroutine
,Object
,List
,Dict
,Configuration
,Resource
,Dependency
andSelector
providers that help assembling your objects. SeeProviders <https://python-dependency-injector.ets-labs.org/providers/index.html>
_. -
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 <https://python-dependency-injector.ets-labs.org/providers/overriding.html>
_. -
Configuration. Reads configuration from
yaml
&ini
files,pydantic
settings, environment variables, and dictionaries. SeeConfiguration provider <https://python-dependency-injector.ets-labs.org/providers/configuration.html>
_. -
Containers. Provides declarative and dynamic containers.
See
Containers <https://python-dependency-injector.ets-labs.org/containers/index.html>
_. -
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 <https://python-dependency-injector.ets-labs.org/providers/resource.html>
_. -
Wiring. Injects dependencies into functions and methods. Helps integrating with
other frameworks: Django, Flask, Aiohttp, Sanic, FastAPI, etc.
See
Wiring <https://python-dependency-injector.ets-labs.org/wiring.html>
_. -
Asynchronous. Supports asynchronous injections.
See
Asynchronous injections <https://python-dependency-injector.ets-labs.org/providers/async.html>
_. -
Typing. Provides typing stubs,
mypy
-friendly. SeeTyping and mypy <https://python-dependency-injector.ets-labs.org/providers/typing_mypy.html>
_. -
Performance. Fast. Written in
Cython
. - Maturity. Mature and production-ready. Well-tested, documented and supported.
.. code-block:: python
from dependency_injector import containers, providers from dependency_injector.wiring import inject, Provide
class Container(containers.DeclarativeContainer):
config = providers.Configuration()
api_client = providers.Singleton(
ApiClient,
api_key=config.api_key,
timeout=config.timeout.as_int(),
)
service = providers.Factory(
Service,
api_client=api_client,
)
@inject def main(service: Service = Provide[Container.service]): ...
if name == 'main': container = Container() container.config.api_key.from_env('API_KEY') container.config.timeout.from_env('TIMEOUT') container.wire(modules=[sys.modules[name]])
main() # <-- dependency is injected automatically
with container.api_client.override(mock.Mock()):
main() # <-- overridden dependency is injected automatically
When you call main()
function the Service
dependency is assembled and injected automatically.
When doing a testing you call the container.api_client.override()
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 configuring project for the different environments: replace an API client with a stub on the dev or stage.
With the Dependency Injector
objects assembling is consolidated in the container.
Dependency injections are defined explicitly.
This makes easier to understand and change how application works.
.. figure:: https://raw.githubusercontent.com/wiki/ets-labs/python-dependency-injector/img/di-readme.svg :target: https://github.com/ets-labs/python-dependency-injector
Visit the docs to know more about the
Dependency injection and inversion of control in Python <https://python-dependency-injector.ets-labs.org/introduction/di_in_python.html>
_.
Installation
The package is available on the PyPi
_::
pip install dependency-injector
Documentation
The documentation is available here <https://python-dependency-injector.ets-labs.org/>
_.
Examples
Choose one of the following:
-
Application example (single container) <https://python-dependency-injector.ets-labs.org/examples/application-single-container.html>
_ -
Application example (multiple containers) <https://python-dependency-injector.ets-labs.org/examples/application-multiple-containers.html>
_ -
Decoupled packages example (multiple containers) <https://python-dependency-injector.ets-labs.org/examples/decoupled-packages.html>
_ -
Boto3 example <https://python-dependency-injector.ets-labs.org/examples/boto3.html>
_ -
Django example <https://python-dependency-injector.ets-labs.org/examples/django.html>
_ -
Flask example <https://python-dependency-injector.ets-labs.org/examples/flask.html>
_ -
Aiohttp example <https://python-dependency-injector.ets-labs.org/examples/aiohttp.html>
_ -
Sanic example <https://python-dependency-injector.ets-labs.org/examples/sanic.html>
_ -
FastAPI example <https://python-dependency-injector.ets-labs.org/examples/fastapi.html>
_ -
FastAPI + Redis example <https://python-dependency-injector.ets-labs.org/examples/fastapi-redis.html>
_ -
FastAPI + SQLAlchemy example <https://python-dependency-injector.ets-labs.org/examples/fastapi-sqlalchemy.html>
_
Tutorials
Choose one of the following:
-
Flask web application tutorial <https://python-dependency-injector.ets-labs.org/tutorials/flask.html>
_ -
Aiohttp REST API tutorial <https://python-dependency-injector.ets-labs.org/tutorials/aiohttp.html>
_ -
Asyncio monitoring daemon tutorial <https://python-dependency-injector.ets-labs.org/tutorials/asyncio-daemon.html>
_ -
CLI application tutorial <https://python-dependency-injector.ets-labs.org/tutorials/cli.html>
_
Concept
The framework stands on the PEP20 (The Zen of Python) <https://www.python.org/dev/peps/pep-0020/>
_ principle:
.. code-block:: bash
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 a simplicity.
Dependency Injector
is a simple tool for the powerful concept.
Frequently asked questions
What is the 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 doing 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 the project grows
Have a question?
- Open a
Github Issue <https://github.com/ets-labs/python-dependency-injector/issues>
_
Found a bug?
- Open a
Github Issue <https://github.com/ets-labs/python-dependency-injector/issues>
_
Want to help?
- |star| Star the
Dependency Injector
on theGithub <https://github.com/ets-labs/python-dependency-injector/>
_ - |new| Start a new project with the
Dependency Injector
- |tell| Tell your friend about the
Dependency Injector
Want to contribute?
- |fork| Fork the project
- |pull| Open a pull request to the
develop
branch
.. _PyPi: https://pypi.org/project/dependency-injector/
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