Python dependency injection framework
Dependency Injector is a Python dependency injection framework. It was designed to be unified, developer-friendly tool that helps to implement dependency injection pattern in formal, pretty, Pythonic way.
Dependency Injector framework key features are:
- Easy, smart, pythonic style.
- Obvious, clear structure.
- Extensibility and flexibility.
- Memory efficiency.
- Thread safety.
- Semantic versioning.
Dependency injection is a software design pattern that implements Inversion of control for resolving dependencies. Formally, if object A depends on object B, object A must not create or import object B directly. Instead of this object A must provide a way for injecting object B. The responsibilities of objects creation and dependencies injection are delegated to external code - the dependency injector.
Popular terminology of dependency injection pattern:
- Object A, that is dependant on object B, is often called - the client.
- Object B, that is a dependency, is often called - the service.
- External code that is responsible for creation of objects and injection of dependencies is often called - the dependency injector.
There are several ways of how service can be injected into the client:
- by passing it as __init__ argument (constructor / initializer injection)
- by setting it as attribute’s value (attribute injection)
- by passing it as method’s argument (method injection)
Dependency injection pattern has few strict rules that should be followed:
- The client delegates to the dependency injector the responsibility of injecting its dependencies - the service(s).
- The client doesn’t know how to create the service, it knows only interface of the service. The service doesn’t know that it is used by The client.
- The dependency injector knows how to create the client and the service, it also knows that the client depends on the service, and knows how to inject the service into the client.
- The client and the service know nothing about the dependency injector.
Dependency injection pattern provides next advantages:
- Control on application structure.
- Decreased coupling between application components.
- Increased code reusability.
- Increased testability.
- Increased maintainability.
- Reconfiguration of system without rebuilding.
Example of dependency injection
Brief example below demonstrates usage of Dependency Injector for creating several IoC containers for some example application:
"""Example of dependency injection in Python.""" import logging import sqlite3 import boto.s3.connection import example.main import example.services import dependency_injector.containers as containers import dependency_injector.providers as providers class Platform(containers.DeclarativeContainer): """IoC container of platform service providers.""" logger = providers.Singleton(logging.Logger, name='example') database = providers.Singleton(sqlite3.connect, ':memory:') s3 = providers.Singleton(boto.s3.connection.S3Connection, aws_access_key_id='KEY', aws_secret_access_key='SECRET') class Services(containers.DeclarativeContainer): """IoC container of business service providers.""" users = providers.Factory(example.services.Users, logger=Platform.logger, db=Platform.database) auth = providers.Factory(example.services.Auth, logger=Platform.logger, db=Platform.database, token_ttl=3600) photos = providers.Factory(example.services.Photos, logger=Platform.logger, db=Platform.database, s3=Platform.s3) class Application(containers.DeclarativeContainer): """IoC container of application component providers.""" main = providers.Callable(example.main.main, users_service=Services.users, auth_service=Services.auth, photos_service=Services.photos)
Next example demonstrates run of example application defined above:
"""Run example application.""" import sys import logging from containers import Platform, Application if __name__ == '__main__': # Configure platform logger: Platform.logger().addHandler(logging.StreamHandler(sys.stdout)) # Run application: Application.main(uid=sys.argv, password=sys.argv, photo=sys.argv) # Previous call is an equivalent of next operations: # # logger = logging.Logger(name='example') # database = sqlite3.connect(':memory:') # s3 = boto.s3.connection.S3Connection(aws_access_key_id='KEY', # aws_secret_access_key='SECRET') # # example.main.main(uid=sys.argv, # password=sys.argv, # photo=sys.argv, # users_service=example.services.Users(logger=logger, # db=database), # auth_service=example.services.Auth(logger=logger, # db=database, # token_ttl=3600), # photos_service=example.services.Photos(logger=logger, # db=database, # s3=s3))
Alternative definition styles of providers
Dependecy Injector supports few other styles of dependency injections definition.
IoC containers from previous example could look like these:
class Platform(containers.DeclarativeContainer): """IoC container of platform service providers.""" logger = providers.Singleton(logging.Logger) \ .add_kwargs(name='example') database = providers.Singleton(sqlite3.connect) \ .add_args(':memory:') s3 = providers.Singleton(boto.s3.connection.S3Connection) \ .add_kwargs(aws_access_key_id='KEY', aws_secret_access_key='SECRET')
or like this these:
class Platform(containers.DeclarativeContainer): """IoC container of platform service providers.""" logger = providers.Singleton(logging.Logger) logger.add_kwargs(name='example') database = providers.Singleton(sqlite3.connect) database.add_args(':memory:') s3 = providers.Singleton(boto.s3.connection.S3Connection) s3.add_kwargs(aws_access_key_id='KEY', aws_secret_access_key='SECRET')
You can get more Dependency Injector examples in /examples directory on GitHub:
Dependency Injector library is available on PyPi:
pip install dependency_injector
Feedback & Support
Feel free to post questions, bugs, feature requests, proposals etc. on Dependency Injector GitHub Issues:
Your feedback is quite important!
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