dependency-injector 3.14.4

Dependency injection microframework for Python

Dependency Injector is a dependency injection microframework for Python. It was designed to be unified, developer-friendly tool that helps to implement dependency injection design pattern in formal, pretty, Pythonic way.

Dependency Injector framework key features are:

• Easy, smart, pythonic style.
• Obvious, clear structure.
• Extensibility and flexibility.
• High performance.
• Memory efficiency.
• Thread safety.
• Documentation.
• Semantic versioning.

Dependency Injector containers and providers are implemented as C extension types using Cython.

Status

PyPi
Python versions and implementations
Builds and tests coverage
Github

Dependency injection

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 the following 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

Let’s go through next example:

Listing of example.engines module:

"""Dependency injection example, engines module."""


class Engine(object):
    """Example engine base class.

    Engine is a heart of every car. Engine is a very common term and could be
    implemented in very different ways.
    """


class GasolineEngine(Engine):
    """Gasoline engine."""


class DieselEngine(Engine):
    """Diesel engine."""


class ElectroEngine(Engine):
    """Electro engine."""

Listing of example.cars module:

"""Dependency injection example, cars module."""


class Car(object):
    """Example car."""

    def __init__(self, engine):
        """Initializer."""
        self._engine = engine  # Engine is injected

Next example demonstrates creation of several cars with different engines:

"""Dependency injection example, Cars & Engines."""

import example.cars
import example.engines


if __name__ == '__main__':
    gasoline_car = example.cars.Car(example.engines.GasolineEngine())
    diesel_car = example.cars.Car(example.engines.DieselEngine())
    electro_car = example.cars.Car(example.engines.ElectroEngine())

While previous example demonstrates advantages of dependency injection, there is a disadvantage demonstration as well - creation of car requires additional code for specification of dependencies. Nevertheless, this disadvantage could be easily avoided by using a dependency injection framework for creation of inversion of control container (IoC container).

Example of creation of several inversion of control containers (IoC containers) using Dependency Injector:

"""Dependency injection example, Cars & Engines IoC containers."""

import example.cars
import example.engines

import dependency_injector.containers as containers
import dependency_injector.providers as providers


class Engines(containers.DeclarativeContainer):
    """IoC container of engine providers."""

    gasoline = providers.Factory(example.engines.GasolineEngine)

    diesel = providers.Factory(example.engines.DieselEngine)

    electro = providers.Factory(example.engines.ElectroEngine)


class Cars(containers.DeclarativeContainer):
    """IoC container of car providers."""

    gasoline = providers.Factory(example.cars.Car,
                                 engine=Engines.gasoline)

    diesel = providers.Factory(example.cars.Car,
                               engine=Engines.diesel)

    electro = providers.Factory(example.cars.Car,
                                engine=Engines.electro)


if __name__ == '__main__':
    gasoline_car = Cars.gasoline()
    diesel_car = Cars.diesel()
    electro_car = Cars.electro()

Dependency Injector structure

Dependency Injector is a microframework and has a very simple structure.

There are 2 main entities: providers & containers.

Providers

Providers are strategies of accessing objects. They define how particular objects are provided.

• Provider - base provider class.
• Callable - provider that calls wrapped callable on every call. Supports positional & keyword argument injections.
• Factory - provider that creates new instance of specified class on every call. Supports positional & keyword argument injections, as well as attribute injections.
• Singleton - provider that creates new instance of specified class on first call and returns same instance on every next call. Supports positional & keyword argument injections, as well as attribute injections.
• Object - provider that returns provided instance “as is”.
• ExternalDependency - provider that can be useful for development of self-sufficient libraries / modules / applications that has required external dependencies.
• Configuration - provider that helps with implementing late static binding of configuration options - use first, define later.

Containers

Containers are collections of providers. Main purpose of containers is to group providers.

• DeclarativeContainer - is inversion of control container that could be defined in declarative manner. It should cover most of the cases when list of providers that would be included in container is deterministic (container will not change its structure in runtime).
• DynamicContainer - is an inversion of control container with dynamic structure. It should cover most of the cases when list of providers that would be included in container is non-deterministic and depends on application’s flow or its configuration (container’s structure could be determined just after application will be started and will do some initial work, like parsing list of container providers from the configuration).

Dependency Injector in action

Brief example below is a simplified version of inversion of control container from one of the real-life applications. This example demonstrates usage of Dependency Injector inversion of control container & providers for specifying all application components and their dependencies between each other in one module. Besides other listed above advantages, it gives a great opportunity to control & manage application’s structure in one place.

"""Example of dependency injection in Python."""

import logging
import sqlite3

import boto3

from dependency_injector import containers, providers
from example import services, main


class IocContainer(containers.DeclarativeContainer):
    """Application IoC container."""

    config = providers.Configuration('config')
    logger = providers.Singleton(logging.Logger, name='example')

    # Gateways

    database_client = providers.Singleton(sqlite3.connect, config.database.dsn)

    s3_client = providers.Singleton(
        boto3.client, 's3',
        aws_access_key_id=config.aws.access_key_id,
        aws_secret_access_key=config.aws.secret_access_key,
    )

    # Services

    users_service = providers.Factory(
        services.UsersService,
        db=database_client,
        logger=logger,
    )

    auth_service = providers.Factory(
        services.AuthService,
        token_ttl=config.auth.token_ttl,
        db=database_client,
        logger=logger,
    )

    photos_service = providers.Factory(
        services.PhotosService,
        db=database_client,
        s3=s3_client,
        logger=logger,
    )

    # Misc

    main = providers.Callable(
        main.main,
        users_service=users_service,
        auth_service=auth_service,
        photos_service=photos_service,
    )

Next example demonstrates run of example application defined above:

"""Run example of dependency injection in Python."""

import sys
import logging

from container import IocContainer


if __name__ == '__main__':
    # Configure container:
    container = IocContainer(
        config={
            'database': {
                'dsn': ':memory:',
            },
            'aws': {
                'access_key_id': 'KEY',
                'secret_access_key': 'SECRET',
            },
            'auth': {
                'token_ttl': 3600,
            },
        }
    )
    container.logger().addHandler(logging.StreamHandler(sys.stdout))

    # Run application:
    container.main(*sys.argv[1:])

You can get more Dependency Injector examples in /examples directory on GitHub:

https://github.com/ets-labs/python-dependency-injector

Installation

Dependency Injector library is available on PyPi:

pip install dependency_injector

Documentation

Dependency Injector documentation is hosted on ReadTheDocs:

• User’s guide
• API docs

Feedback & Support

Feel free to post questions, bugs, feature requests, proposals etc. on Dependency Injector GitHub Issues:

https://github.com/ets-labs/python-dependency-injector/issues

Your feedback is quite important!