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Injector - Python dependency injection framework, inspired by Guice

Project Description

Injector - Python dependency injection framework, inspired by Guice

.. image::


Dependency injection as a formal pattern is less useful in Python than in other
languages, primarily due to its support for keyword arguments, the ease with
which objects can be mocked, and its dynamic nature.

That said, a framework for assisting in this process can remove a lot of
boiler-plate from larger applications. That's where Injector can help. It
automatically and transitively provides keyword arguments with their values. As
an added benefit, Injector encourages nicely compartmentalised code through the
use of ``Module`` s.

While being inspired by Guice, it does not slavishly replicate its API.
Providing a Pythonic API trumps faithfulness.

Supported Python versions

Injector work with the following Python interpreters:

* CPython 2.6, 2.7, 3.2, 3.3
* PyPy 1.9

A Full Example
Here's a full example to give you a taste of how Injector works::

>>> from injector import Module, Key, provides, Injector, inject, singleton

We'll use an in-memory SQLite database for our example::

>>> import sqlite3

And make up an imaginary RequestHandler class that uses the SQLite connection::

>>> class RequestHandler(object):
... @inject(db=sqlite3.Connection)
... def __init__(self, db):
... self._db = db
... def get(self):
... cursor = self._db.cursor()
... cursor.execute('SELECT key, value FROM data ORDER by key')
... return cursor.fetchall()

Next, for the sake of the example, we'll create a "configuration" annotated

>>> Configuration = Key('configuration')

`Key` is used to uniquely identifies the configuration dictionary. Next, we
bind the configuration to the injector, using a module::

>>> def configure_for_testing(binder):
... configuration = {'db_connection_string': ':memory:'}
... binder.bind(Configuration, to=configuration, scope=singleton)

Next we create a module that initialises the DB. It depends on the
configuration provided by the above module to create a new DB connection, then
populates it with some dummy data, and provides a Connection object::

>>> class DatabaseModule(Module):
... @singleton
... @provides(sqlite3.Connection)
... @inject(configuration=Configuration)
... def provide_sqlite_connection(self, configuration):
... conn = sqlite3.connect(configuration['db_connection_string'])
... cursor = conn.cursor()
... cursor.execute('CREATE TABLE IF NOT EXISTS data (key PRIMARY KEY, value)')
... cursor.execute('INSERT OR REPLACE INTO data VALUES ("hello", "world")')
... return conn

(Note how we have decoupled configuration from our database initialisation

Finally, we initialise an Injector and use it to instantiate a RequestHandler
instance. This first transitively constructs a `sqlite3.Connection` object,
and the `Configuration` dictionary that it in turn requires, then instantiates
our `RequestHandler`::

>>> injector = Injector([configure_for_testing, DatabaseModule()])
>>> handler = injector.get(RequestHandler)
>>> tuple(map(str, handler.get()[0])) # py3/py2 compatibility hack
('hello', 'world')

We can also veryify that our Configuration and SQLite connections are indeed
singletons within the Injector::

>>> injector.get(Configuration) is injector.get(Configuration)
>>> injector.get(sqlite3.Connection) is injector.get(sqlite3.Connection)

You're probably thinking something like: "this is a large amount of work just
to give me a database connection", and you are correct; dependency injection is
typically not that useful for smaller projects. It comes into its own on large
projects where the up-front effort pays for itself in two ways:

1. Forces decoupling. In our example, this is illustrated by decoupling
our configuration and database configuration.
2. After a type is configured, it can be injected anywhere with no
additional effort. Simply @inject and it appears. We don't really
illustrate that here, but you can imagine adding an arbitrary number of
RequestHandler subclasses, all of which will automatically have a DB
connection provided.

At its heart, Injector is simply a dictionary for mapping types to things that
create instances of those types. This could be as simple as::

{str: 'an instance of a string'}

For those new to dependency-injection and/or Guice, though, some of the
terminology used may not be obvious.

A means of providing an instance of a type. Built-in providers include
``ClassProvider`` (creates a new instance from a class),
``InstanceProvider`` (returns an existing instance directly),
``CallableProvider`` (provides an instance by calling a function).

By default, providers are executed each time an instance is required. Scopes
allow this behaviour to be customised. For example, ``SingletonScope``
(typically used through the class decorator ``singleton``), can be used to
always provide the same instance of a class.

Other examples of where scopes might be a threading scope, where instances are
provided per-thread, or a request scope, where instances are provided

The default scope is ``NoScope``.

Binding Key
A binding key uniquely identifies a provider of a type. It is effectively a
tuple of ``(type, annotation)`` where ``type`` is the type to be provided and
``annotation`` is additional, optional, uniquely identifying information for
the type.

For example, the following are all unique binding keys for ``str``::

(str, 'name')
(str, 'description')

For a generic type such as ``str``, annotations are very useful for unique

As an *alternative* convenience to using annotations, ``Key`` may be used
to create unique types as necessary::

>>> from injector import Key
>>> Name = Key('name')
>>> Description = Key('description')

Which may then be used as binding keys, without annotations, as they already
uniquely identify a particular provider::

(Name, None)
(Description, None)

Though of course, annotations may still be used with these types, like any
other type.

An annotation is additional unique information about a type to avoid binding
key collisions. It creates a new unique binding key for an existing type.

A binding is the mapping of a unique binding key to a corresponding provider.
For example::

>>> from injector import InstanceProvider
>>> bindings = {
... (Name, None): InstanceProvider('Sherlock'),
... (Description, None): InstanceProvider('A man of astounding insight'),
... }

The ``Binder`` is simply a convenient wrapper around the dictionary
that maps types to providers. It provides methods that make declaring bindings

A ``Module`` configures bindings. It provides methods that simplify the
process of binding a key to a provider. For example the above bindings would be
created with::

>>> from injector import Module
>>> class MyModule(Module):
... def configure(self, binder):
... binder.bind(Name, to='Sherlock')
... binder.bind(Description, to='A man of astounding insight')

For more complex instance construction, methods decorated with
``@provides`` will be called to resolve binding keys::

>>> from injector import provides
>>> class MyModule(Module):
... def configure(self, binder):
... binder.bind(Name, to='Sherlock')
... @provides(Description)
... def describe(self):
... return 'A man of astounding insight (at %s)' % time.time()

Injection is the process of providing an instance of a type, to a method that
uses that instance. It is achieved with the ``inject`` decorator. Keyword
arguments to inject define which arguments in its decorated method should be
injected, and with what.

Here is an example of injection on a module provider method, and on the
constructor of a normal class::

>>> from injector import inject
>>> class User(object):
... @inject(name=Name, description=Description)
... def __init__(self, name, description):
... = name
... self.description = description

>>> class UserModule(Module):
... def configure(self, binder):
... binder.bind(User)

>>> class UserAttributeModule(Module):
... def configure(self, binder):
... binder.bind(Name, to='Sherlock')
... @provides(Description)
... @inject(name=Name)
... def describe(self, name):
... return '%s is a man of astounding insight' % name

You can also ``inject``-decorate class itself. This code::

>>> @inject(name=Name)
... class Item(object):
... pass

is equivalent to::

>>> class Item(object):
... @inject(name=Name)
... def __init__(self, name):
... = name

**Note**: You can also begin the name of injected member with an underscore(s)
(to indicate the member being private for example). In such case the member
will be injected using the name you specified, but corresponding parameter in
a constructor (let's say you instantiate the class manually) will have the
underscore prefix stripped (it makes it consistent with most of the usual
parameter names)::

>>> @inject(_y=int)
... class X(object):
... pass

>>> x1 = injector.get(X)
>>> x1.y
Traceback (most recent call last):
AttributeError: 'X' object has no attribute 'y'
>>> x1._y

>>> x2 = X(y=2)
>>> x2.y
Traceback (most recent call last):
AttributeError: 'X' object has no attribute 'y'
>>> x2._y

The ``Injector`` brings everything together. It takes a list of
``Module`` s, and configures them with a binder, effectively creating a
dependency graph::

>>> from injector import Injector
>>> injector = Injector([UserModule(), UserAttributeModule()])

You can also pass classes instead of instances to ``Injector``, it will
instantiate them for you::

>>> injector = Injector([UserModule, UserAttributeModule])

The injector can then be used to acquire instances of a type, either directly::

>>> injector.get(Name)
>>> injector.get(Description)
'Sherlock is a man of astounding insight'

Or transitively::

>>> user = injector.get(User)
>>> isinstance(user, User)
>>> user.description
'Sherlock is a man of astounding insight'

Assisted injection
Sometimes there are classes that have injectable and non-injectable parameters in their
constructors. Let's have for example::

>>> class Database(object): pass

>>> class User(object):
... def __init__(self, name):
... = name

>>> @inject(db=Database)
... class UserUpdater(object):
... def __init__(self, user):
... pass

You may want to have database connection ``db`` injected into ``UserUpdater`` constructor,
but in the same time provide ``user`` object by yourself, and assuming that ``user`` object
is a value object and there's many users in your application it doesn't make much sense
to inject objects of class ``User``.

In this situation there's technique called Assisted injection::

>>> from injector import AssistedBuilder
>>> injector = Injector()
>>> builder = injector.get(AssistedBuilder(UserUpdater))
>>> user = User('John')
>>> user_updater =

This way we don't get ``UserUpdater`` directly but rather a builder object. Such builder
has ``build(**kwargs)`` method which takes non-injectable parameters, combines
them with injectable dependencies of ``UserUpdater`` and calls ``UserUpdater`` initializer
using all of them.

``AssistedBuilder(X)`` is injectable just as anything else, if you need instance of it you
just ask for it like that::

>>> @inject(updater_builder=AssistedBuilder(UserUpdater))
... class NeedsUserUpdater(object):
... def method(self):
... updater =

More information on this topic:

* `"How to use Google Guice to create objects that require parameters?" on Stack Overflow <>`_
* `Google Guice assisted injection <>`_
Child injectors

Concept similar to Guice's child injectors is supported by ``Injector``. This way you can
have one injector that inherits bindings from other injector (parent) but these bindings
can be overriden in it and it doesn't affect parent injector bindings::

>>> def configure_parent(binder):
... binder.bind(str, to='asd')
... binder.bind(int, to=42)
>>> def configure_child(binder):
... binder.bind(str, to='qwe')
>>> parent = Injector(configure_parent)
>>> child = parent.create_child_injector(configure_child)
>>> parent.get(str), parent.get(int)
('asd', 42)
>>> child.get(str), child.get(int)
('qwe', 42)

**Note**: Default scopes are bound only to root injector. Binding them manually to child
injectors will result in unexpected behaviour.
**Note 2**: Once a binding key is present in parent injector scope (like ``singleton``
scope), provider saved there takes predecence when binding is overridden in child injector in
the same scope. This behaviour is subject to change::

>>> def configure_parent(binder):
... binder.bind(str, to='asd', scope=singleton)
>>> def configure_child(binder):
... binder.bind(str, to='qwe', scope=singleton)
>>> parent = Injector(configure_parent)
>>> child = parent.create_child_injector(configure_child)
>>> child.get(str) # this behaves as expected
>>> parent.get(str) # wat


Singletons are declared by binding them in the SingletonScope. This can be done
in three ways:

1. Decorating the class with ``@singleton``.
2. Decorating a ``@provides(X)`` decorated Module method with ``@singleton``.
3. Explicitly calling ``binder.bind(X, scope=singleton)``.

A (redunant) example showing all three methods::

>>> @singleton
... class Thing(object): pass
>>> class ThingModule(Module):
... def configure(self, binder):
... binder.bind(Thing, scope=singleton)
... @singleton
... @provides(Thing)
... def provide_thing(self):
... return Thing()

Implementing new Scopes
In the above description of scopes, we glossed over a lot of detail. In
particular, how one would go about implementing our own scopes.

Basically, there are two steps. First, subclass ``Scope`` and implement

>>> from injector import Scope
>>> class CustomScope(Scope):
... def get(self, key, provider):
... return provider

Then create a global instance of ``ScopeDecorator`` to allow classes to be
easily annotated with your scope::

>>> from injector import ScopeDecorator
>>> customscope = ScopeDecorator(CustomScope)

This can be used like so:

>>> @customscope
... class MyClass(object):
... pass

Scopes are bound in modules with the ``Binder.bind_scope`` method::

>>> class MyModule(Module):
... def configure(self, binder):
... binder.bind_scope(CustomScope)

Scopes can be retrieved from the injector, as with any other instance. They are
singletons across the life of the injector::

>>> injector = Injector([MyModule()])
>>> injector.get(CustomScope) is injector.get(CustomScope)

For scopes with a transient lifetime, such as those tied to HTTP requests, the
usual solution is to use a thread or greenlet-local cache inside the scope. The
scope is "entered" in some low-level code by calling a method on the scope
instance that creates this cache. Once the request is complete, the scope is
"left" and the cache cleared.


When you use unit test framework such as ``unittest2`` or ``nose`` you can also
profit from ``injector``. However, manually creating injectors and test classes
can be quite annoying. There is, however, ``with_injector`` method decorator which
has parameters just as ``Injector`` construtor and installes configured injector into
class instance on the time of method call::

>>> from injector import Module, with_injector
>>> class UsernameModule(Module):
... def configure(self, binder):
... binder.bind(str, 'Maria')
>>> class TestSomethingClass(object):
... @with_injector(UsernameModule())
... def setup(self):
... pass
... @inject(username=str)
... def test_username(self, username):
... assert (username == 'Maria')

*Each* method call re-initializes ``Injector`` - if you want to you can also put
``with_injector`` decorator on class constructor.

After such call all ``inject``-decorated methods will work just as you'd expect
them to work.

Thread safety

The following functions are thread safe:

* ``Injector.get``
* injection provided by ``inject`` decorator (please note, however, that it doesn't say anything about decorated function thread safety)

This framework is similar to snake-guice, but aims for simplification.

:copyright: (c) 2010 by Alec Thomas
:license: BSD

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