dependency-injector 3.14.12

Dependency injection microframework for Python

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

The key features of the Dependency Injector framework are:

• Easy, smart, and pythonic style.
• Obvious and clear structure.
• Extensibility and flexibility.
• High performance.
• Memory efficiency.
• Thread safety.
• Documented.
• Semantically versioned.

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 to resolve 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 to inject object B. The responsibilities of objects creation and dependency injection are delegated to external code - the dependency injector.

Popular terminology of the dependency injection pattern:

• Object A, which depends on object B, is often called - the client.
• Object B, which is depended on, 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 to inject a service into a client:

• by passing it as an __init__ argument (constructor / initializer injection)
• by setting it as an attribute’s value (attribute injection)
• by passing it as a method’s argument (method injection)

The 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 the 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.

The dependency injection pattern provides the following advantages:

• Control of application structure.
• Decreased coupling of application components.
• Increased code reusability.
• Increased testability.
• Increased maintainability.
• Reconfiguration of a 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

The next example demonstrates the 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 the previous example demonstrates the advantages of dependency injection, there is a disadvantage demonstrated as well - the creation of a car requires additional code to specificaty its dependencies. However, this disadvantage could be avoided by using a dependency injection framework for the creation of an inversion of control container (IoC container).

Here’s an example of the 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 simple structure.

There are two main entities: providers and containers.

Providers

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

• Provider - base provider class.
• Callable - provider that calls a wrapped callable on every call. Supports positional and keyword argument injections.
• Factory - provider that creates new instance of specified class on every call. Supports positional and keyword argument injections, as well as attribute injections.
• Singleton - provider that creates new instance of specified class on its first call and returns the same instance on every next call. Supports position and 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, and applications that require external dependencies.
• Configuration - provider that helps with implementing late static binding of configuration options - use first, define later.

Containers

Containers are collections of providers. The main purpose of containers is to group providers.

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

Dependency Injector in action

The brief example below is a simplified version of inversion of control containers from a real-life application. The example demonstrates the usage of Dependency Injector inversion of control container and providers for specifying application components and their dependencies on each other in one module. Besides other previously mentioned advantages, it shows a great opportunity to control and 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,
    )

The next example demonstrates a run of the 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 find more Dependency Injector examples in the /examples directory on our GitHub:

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

Installation

The Dependency Injector library is available on PyPi:

pip install dependency_injector

Documentation

The 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 the Dependency Injector GitHub issues page:

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

Your feedback is quite important!