python-inject

Dependency injection the python way, the good way.

Key features

• Fast.
• Thread-safe.
• Simple to use.
• Does not steal class constructors.
• Does not try to manage your application object graph.
• Transparently integrates into tests.
• Autoparams leveraging type annotations.
• Supports type hinting in Python 3.5+.
• Supports Python 3.9+ (v5.*), 3.5-3.8 (v4.*) and Python 2.7–3.5 (v3.*).

Python Support

Python	Inject Version
3.9+	5.0+
3.6-3.8	4.1+, < 5.0
3.5	4.0
< 3.5	3.*

Installation

Use pip to install the lastest version:

pip install inject

Autoparams example

@inject.autoparams returns a decorator which automatically injects arguments into a function that uses type annotations. This is supported only in Python >= 3.5.

@inject.autoparams()
def refresh_cache(cache: RedisCache, db: DbInterface):
    pass

There is an option to specify which arguments we want to inject without attempts of injecting everything:

@inject.autoparams('cache', 'db')
def sign_up(name, email, cache: RedisCache, db: DbInterface):
    pass

Step-by-step example

# Import the inject module.
import inject


# `inject.instance` requests dependencies from the injector.
def foo(bar):
    cache = inject.instance(Cache)
    cache.save('bar', bar)


# `inject.params` injects dependencies as keyword arguments or positional argument. 
# Also you can use @inject.autoparams in Python 3.5, see the example above.
@inject.params(cache=Cache, user=CurrentUser)
def baz(foo, cache=None, user=None):
    cache.save('foo', foo, user)

# this can be called in different ways:
# with injected arguments
baz('foo')

# with positional arguments
baz('foo', my_cache)

# with keyword arguments
baz('foo', my_cache, user=current_user)


# `inject.param` is deprecated, use `inject.params` instead.
@inject.param('cache', Cache)
def bar(foo, cache=None):
    cache.save('foo', foo)


# `inject.attr` creates properties (descriptors) which request dependencies on access.
class User(object):
    cache = inject.attr(Cache)
            
    def __init__(self, id):
        self.id = id

    def save(self):
        self.cache.save('users', self)
    
    @classmethod
    def load(cls, id):
        return cls.cache.load('users', id)


# Create an optional configuration.
def my_config(binder):
    binder.bind(Cache, RedisCache('localhost:1234'))

# Configure a shared injector.
inject.configure(my_config)


# Instantiate User as a normal class. Its `cache` dependency is injected when accessed.
user = User(10)
user.save()

# Call the functions, the dependencies are automatically injected.
foo('Hello')
bar('world')

Usage with Django

Django can load some modules multiple times which can lead to InjectorException: Injector is already configured. You can use configure(once=True) which is guaranteed to run only once when the injector is absent:

import inject
inject.configure(my_config, once=True)

Testing

In tests use inject.configure(callable, clear=True) to create a new injector on setup, and optionally inject.clear() to clean up on tear down:

class MyTest(unittest.TestCase):
    def setUp(self):
        inject.configure(lambda binder: binder
            .bind(Cache, MockCache()) \
            .bind(Validator, TestValidator()),
            clear=True)
    
    def tearDown(self):
        inject.clear()

Composable configurations

You can reuse configurations and override already registered dependencies to fit the needs in different environments or specific tests.

    def base_config(binder):
        # ... more dependencies registered here
        binder.bind(Validator, RealValidator())
        binder.bind(Cache, RedisCache('localhost:1234'))

    def tests_config(binder):
        # reuse existing configuration
        binder.install(base_config)

        # override only certain dependencies
        binder.bind(Validator, TestValidator())
        binder.bind(Cache, MockCache())
    
    inject.configure(tests_config, allow_override=True, clear=True)
        

Thread-safety

After configuration the injector is thread-safe and can be safely reused by multiple threads.

Binding types

Instance bindings always return the same instance:

redis = RedisCache(address='localhost:1234')
def config(binder):
    binder.bind(Cache, redis)

Constructor bindings create a singleton on injection:

def config(binder):
    # Creates a redis cache singleton on first injection.
    binder.bind_to_constructor(Cache, lambda: RedisCache(address='localhost:1234'))

Provider bindings call the provider on injection:

def get_my_thread_local_cache():
    pass

def config(binder):
    # Executes the provider on each injection.
    binder.bind_to_provider(Cache, get_my_thread_local_cache) 

Runtime bindings automatically create singletons on injection, require no configuration. For example, only the Config class binding is present, other bindings are runtime:

class Config(object):
    pass

class Cache(object):
    config = inject.attr(Config)

class Db(object):
    config = inject.attr(Config)

class User(object):
    cache = inject.attr(Cache)
    db = inject.attr(Db)
    
    @classmethod
    def load(cls, user_id):
        return cls.cache.load('users', user_id) or cls.db.load('users', user_id)
    
inject.configure(lambda binder: binder.bind(Config, load_config_file()))
user = User.load(10)

Disabling runtime binding

Sometimes runtime binding leads to unexpected behaviour. Say if you forget to bind an instance to a class, inject will try to implicitly instantiate it.

If an instance is unintentionally created with default arguments it may lead to hard-to-debug bugs. To disable runtime binding and make sure that only explicitly bound instances are injected, pass bind_in_runtime=False to inject.configure.

In this case inject immediately raises InjectorException when the code tries to get an unbound instance.

Keys

It is possible to use any hashable object as a binding key. For example:

import inject

inject.configure(lambda binder: \
    binder.bind('host', 'localhost') \
    binder.bind('port', 1234))

Why no scopes?

I've used Guice and Spring in Java for a lot of years, and I don't like their scopes. python-inject by default creates objects as singletons. It does not need a prototype scope as in Spring or NO_SCOPE as in Guice because python-inject does not steal your class constructors. Create instances the way you like and then inject dependencies into them.

Other scopes such as a request scope or a session scope are fragile, introduce high coupling, and are difficult to test. In python-inject write custom providers which can be thread-local, request-local, etc.

For example, a thread-local current user provider:

import inject
import threading

# Given a user class.
class User(object):
    pass

# Create a thread-local current user storage.
_LOCAL = threading.local()

def get_current_user():
    return getattr(_LOCAL, 'user', None)

def set_current_user(user):
    _LOCAL.user = user

# Bind User to a custom provider.
inject.configure(lambda binder: binder.bind_to_provider(User, get_current_user))

# Inject the current user.
@inject.params(user=User)
def foo(user):
    pass

Links

• Project: https://github.com/ivankorobkov/python-inject

License

Apache License 2.0

Contributors

• Ivan Korobkov @ivankorobkov
• Jaime Wyant @jaimewyant
• Sebastian Buczyński @Enforcer
• Oleksandr Fedorov @Fedorof
• cselvaraj @cselvaraj
• 陆雨晴 @SixExtreme
• Andrew William Borba @andrewborba10
• jdmeyer3 @jdmeyer3
• Alex Grover @ajgrover
• Harro van der Kroft @wisepotato
• Samiur Rahman @samiur
• 45deg @45deg
• Alexander Nicholas Costas @ancostas
• Dmitry Balabka @dbalabka