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Pinject is a dependency injection library for python.
The primary goal of Pinject is to help you assemble objects into graphs in an easy, maintainable way.
If you are already familiar with other dependency injection libraries, you may want to read the condensed summary section at the end, so that you get an idea of what Pinject is like and how it might differ from libraries you're used to.
There is a changelog of differences between released versions near the end of this README.
If you're wondering why to use a dependency injection library at all: if you're writing a lot of object-oriented code in python, then it will make your life easier. See, for instance:
If you're wondering why to use Pinject instead of another python dependency injection library, a few of reasons are:
@inject_this and @annotate_that just to get started. With Pinject, you call new_object_graph(), one line, and you're good to go.Look at the simplest getting-started examples for Pinject and for other similar libraries. Pinject should be uniformly easier to use, clearer to read, and less boilerplate that you need to add. If you don't find this to be the case, email!
The easiest way to install Pinject is to get the latest released version from PyPI:
.. code-block:: shell
sudo pip install pinject
If you are interested in the developing version, you can install the next version from Test PyPI:
.. code-block:: shell
sudo pip install \
--no-deps \
--no-cache \
--upgrade \
--index-url https://test.pypi.org/simple/ \
pinject
You can also check out all the source code, including tests, designs, and TODOs:
.. code-block:: shell
git clone https://github.com/google/pinject
The most important function in the pinject module is
new_object_graph(). This creates an ObjectGraph, which you can use to
instantiate objects using dependency injection. If you pass no args to
new_object_graph(), it will return a reasonably configured default
ObjectGraph.
.. code-block:: python
>>> class OuterClass(object):
... def __init__(self, inner_class):
... self.inner_class = inner_class
...
>>> class InnerClass(object):
... def __init__(self):
... self.forty_two = 42
...
>>> obj_graph = pinject.new_object_graph()
>>> outer_class = obj_graph.provide(OuterClass)
>>> print outer_class.inner_class.forty_two
42
>>>
As you can see, you don't need to tell Pinject how to construct its
ObjectGraph, and you don't need to put decorators in your code. Pinject has
reasonable defaults that allow it to work out of the box.
A Pinject binding is an association between an arg name and a provider.
In the example above, Pinject created a binding between the arg name
inner_class and an implicitly created provider for the class
InnerClass. The binding it had created was how Pinject knew that it
should pass an instance of InnerClass as the value of the inner_class
arg when instantiating OuterClass.
Pinject creates implicit bindings for classes. The implicit bindings assume
your code follows PEP8 conventions: your classes are named in CamelCase,
and your args are named in lower_with_underscores. Pinject transforms
class names to injectable arg names by lowercasing words and connecting them
with underscores. It will also ignore any leading underscore on the class
name.
+-------------+-------------+
| Class name | Arg name |
+=============+=============+
| Foo | foo |
+-------------+-------------+
| FooBar | foo_bar |
+-------------+-------------+
| _Foo | foo |
+-------------+-------------+
| _FooBar | foo_bar |
+-------------+-------------+
If two classes map to the same arg name, whether those classes are in the same module or different modules, Pinject will not create an implicit binding for that arg name (though it will not raise an error).
So far, the examples have not told new_object_graph() the classes for
which it should create implicit bindings. new_object_graph() by default
looks in all imported modules, but you may occasionally want to restrict the
classes for which new_object_graph() creates implicit bindings. If so,
new_object_graph() has two args for this purpose.
modules arg specifies in which (python) modules to look for classes; this defaults to ALL_IMPORTED_MODULES.classes arg specifies a exact list of classes; this defaults to None... code-block:: python
>>> class SomeClass(object):
... def __init__(self, foo):
... self.foo = foo
...
>>> class Foo(object):
... pass
...
>>> obj_graph = pinject.new_object_graph(modules=None, classes=[SomeClass])
>>> # obj_graph.provide(SomeClass) # would raise a NothingInjectableForArgError
>>> obj_graph = pinject.new_object_graph(modules=None, classes=[SomeClass, Foo])
>>> some_class = obj_graph.provide(SomeClass)
>>>
One thing that can get tedious about dependency injection via initializers is
that you need to write __init__() methods that copy args to fields. These
__init__() methods can get repetitive, especially when you have several
initializer args.
.. code-block:: python
>>> class ClassWithTediousInitializer(object):
... def __init__(self, foo, bar, baz, quux):
... self._foo = foo
... self._bar = bar
... self._baz = baz
... self._quux = quux
...
>>>
Pinject provides decorators that you can use to avoid repetitive initializer bodies.
@copy_args_to_internal_fields prepends an underscore, i.e., it copies an arg named foo to a field named _foo. It's useful for normal classes.@copy_args_to_public_fields copies the arg named as-is, i.e., it copies an arg named foo to a field named foo. It's useful for data objects... code-block:: python
>>> class ClassWithTediousInitializer(object):
... @pinject.copy_args_to_internal_fields
... def __init__(self, foo, bar, baz, quux):
... pass
...
>>> cwti = ClassWithTediousInitializer('a-foo', 'a-bar', 'a-baz', 'a-quux')
>>> print cwti._foo
'a-foo'
>>>
When using these decorators, you'll normally pass in the body of the
initializer, but you can put other statements there if you need to. The args
will be copied to fields before the initializer body is executed.
These decorators can be applied to initializers that take **kwargs but not
initializers that take *pargs (since it would be unclear what field name
to use).
To create any bindings more complex than the implicit class bindings described
above, you use a binding spec. A binding spec is any python class that
inherits from BindingSpec. A binding spec can do three things:
configure() method can create explicit bindings to classes or instances, as well as requiring bindings without creating them.dependencies() method can return depended-on binding specs.The new_object_graph() function takes a sequence of binding spec instances
as its binding_specs arg. new_object_graph() takes binding spec
instances, rather than binding spec classes, so that you can manually inject
any initial dependencies into the binding specs as needed.
Binding specs should generally live in files named binding_specs.py, where
each file is named in the plural even if there is exactly one binding spec in
it. Ideally, a directory's worth of functionality should be coverable with a
single binding spec. If not, you can create multiple binding specs in the
same binding_specs.py file. If you have so many binding specs that you
need to split them into multiple files, you should name them each with a
_binding_specs.py suffix.
configure() methodsPinject creates implicit bindings for classes, but sometimes the implicit
bindings aren't what you want. For instance, if you have
SomeReallyLongClassName, you may not want to name your initializer args
some_really_long_class_name but instead use something shorter like
long_name, just for this class.
For such situations, you can create explicit bindings using the
configure() method of a binding spec. The configure() method takes a
function bind() as an arg and calls that function to create explicit
bindings.
.. code-block:: python
>>> class SomeClass(object):
... def __init__(self, long_name):
... self.long_name = long_name
...
>>> class SomeReallyLongClassName(object):
... def __init__(self):
... self.foo = 'foo'
...
>>> class MyBindingSpec(pinject.BindingSpec):
... def configure(self, bind):
... bind('long_name', to_class=SomeReallyLongClassName)
...
>>> obj_graph = pinject.new_object_graph(binding_specs=[MyBindingSpec()])
>>> some_class = obj_graph.provide(SomeClass)
>>> print some_class.long_name.foo
'foo'
>>>
The bind() function passed to a binding function binds its first arg,
which must be an arg name (as a str), to exactly one of two kinds of
things.
to_class binds to a class. When the binding is used, Pinject injects an instance of the class.to_instance binds to an instance of some object. Every time the binding is used, Pinject uses that instance... code-block:: python
>>> class SomeClass(object):
... def __init__(self, foo):
... self.foo = foo
...
>>> class MyBindingSpec(pinject.BindingSpec):
... def configure(self, bind):
... bind('foo', to_instance='a-foo')
...
>>> obj_graph = pinject.new_object_graph(binding_specs=[MyBindingSpec()])
>>> some_class = obj_graph.provide(SomeClass)
>>> print some_class.foo
'a-foo'
>>>
The configure() method of a binding spec also may take a function
require() as an arg and use that function to require that a binding be
present without actually defining that binding. require() takes as args
the name of the arg for which to require a binding.
.. code-block:: python
>>> class MainBindingSpec(pinject.BindingSpec):
... def configure(self, require):
... require('foo')
...
>>> class RealFooBindingSpec(pinject.BindingSpec):
... def configure(self, bind):
... bind('foo', to_instance='a-real-foo')
...
>>> class StubFooBindingSpec(pinject.BindingSpec):
... def configure(self, bind):
... bind('foo', to_instance='a-stub-foo')
...
>>> class SomeClass(object):
... def __init__(self, foo):
... self.foo = foo
...
>>> obj_graph = pinject.new_object_graph(
... binding_specs=[MainBindingSpec(), RealFooBindingSpec()])
>>> some_class = obj_graph.provide(SomeClass)
>>> print some_class.foo
'a-real-foo'
>>> # pinject.new_object_graph(
... # binding_specs=[MainBindingSpec()]) # would raise a MissingRequiredBindingError
...
>>>
Being able to require a binding without defining the binding is useful when
you know the code will need some dependency satisfied, but there is more than
one implementation that satisfies that dependency, e.g., there may be a real
RPC client and a fake RPC client. Declaring the dependency means that any
expected but missing bindings will be detected early, when
new_object_graph() is called, rather than in the middle of running your
program.
You'll notice that the configure() methods above have different
signatures, sometimes taking the arg bind and sometimes taking the arg
require. configure() methods must take at least one arg that is
either bind or require, and they may have both args. Pinject will
pass whichever arg or args your configure() method needs.
Binding specs can declare dependencies. A binding spec declares its
dependencies by returning a sequence of instances of the dependent binding
specs from its dependencies() method.
.. code-block:: python
>>> class ClassOne(object):
... def __init__(self, foo):
... self.foo = foo
....
>>> class BindingSpecOne(pinject.BindingSpec):
... def configure(self, bind):
... bind('foo', to_instance='foo-')
...
>>> class ClassTwo(object):
... def __init__(self, class_one, bar):
... self.foobar = class_one.foo + bar
...
>>> class BindingSpecTwo(pinject.BindingSpec):
... def configure(self, bind):
... bind('bar', to_instance='-bar')
... def dependencies(self):
... return [BindingSpecOne()]
...
>>> obj_graph = pinject.new_object_graph(binding_specs=[BindingSpecTwo()])
>>> class_two = obj_graph.provide(ClassTwo)
>>> print class_two.foobar
'foo--bar'
>>>
If classes from module A are injected as collaborators into classes from
module B, then you should consider having the binding spec for module B depend
on the binding spec for module A. In the example above, ClassOne is
injected as a collaborator into ClassTwo, and so BindingSpecTwo (the
binding spec for ClassTwo) depends on BindingSpecOne (the binding spec
for ClassOne).
In this way, you can build a graph of binding spec dependencies that mirrors the graph of collaborator dependencies.
Since explicit bindings cannot conflict (see the section below on binding
precedence), a binding spec should only have dependencies that there will
never be a choice about using. If there may be a choice, then it is better to
list the binding specs separately and explicitly when calling
new_object_graph().
The binding spec dependencies can be a directed acyclic graph (DAG); that is, binding spec A can be a dependency of B and of C, and binding spec D can have dependencies on B and C. Even though there are multiple dependency paths from D to A, the bindings in binding spec A will only be evaluated once.
The binding spec instance of A that is a dependency of B is considered the
same as the instance that is a dependency of C if the two instances are equal
(via __eq__()). The default implementation of __eq__() in
BindingSpec says that two binding specs are equal if they are of exactly
the same python type. You will need to override __eq__() (as well as
__hash__()) if your binding spec is parameterized, i.e., if it takes one
or more initializer args so that two instances of the binding spec may behave
differently.
.. code-block:: python
>>> class SomeBindingSpec(pinject.BindingSpec):
... def __init__(self, the_instance):
... self._the_instance = the_instance
... def configure(self, bind):
... bind('foo', to_instance=self._the_instance)
... def __eq__(self, other):
... return (type(self) == type(other) and
... self._the_instance == other._the_instance)
... def __hash__(self):
... return hash(type(self)) ^ hash(self._the_instance)
...
>>>
If it takes more to instantiate a class than calling its initializer and injecting initializer args, then you can write a provider method for it. Pinject can use provider methods to instantiate objects used to inject as the values of other args.
Pinject looks on binding specs for methods named like provider methods and then creates explicit bindings for them.
.. code-block:: python
>>> class SomeClass(object):
... def __init__(self, foo):
... self.foo = foo
...
>>> class SomeBindingSpec(pinject.BindingSpec):
... def provide_foo(self):
... return 'some-complex-foo'
...
>>> obj_graph = pinject.new_object_graph(binding_specs=[SomeBindingSpec()])
>>> some_class = obj_graph.provide(SomeClass)
>>> print some_class.foo
'some-complex-foo'
>>>
Pinject looks on binding specs for methods whose names start with
provide_, and it assumes that the methods are providers for whatever the
rest of their method names are. For instance, Pinject assumes that the method
provide_foo_bar() is a provider method for the arg name foo_bar.
Pinject injects all args of provider methods that have no default when it calls the provider method.
.. code-block:: python
>>> class SomeClass(object):
... def __init__(self, foobar):
... self.foobar = foobar
...
>>> class SomeBindingSpec(pinject.BindingSpec):
... def provide_foobar(self, bar, hyphen='-'):
... return 'foo' + hyphen + bar
... def provide_bar(self):
... return 'bar'
...
>>> obj_graph = pinject.new_object_graph(binding_specs=[SomeBindingSpec()])
>>> some_class = obj_graph.provide(SomeClass)
>>> print some_class.foobar
'foo-bar'
>>>
Bindings have precedence: explicit bindings take precedence over implicit bindings.
modules and classes args passed to new_object_graph().Pinject will prefer an explicit to an implicit binding.
.. code-block:: python
>>> class SomeClass(object):
... def __init__(self, foo):
... self.foo = foo
...
>>> class Foo(object):
... pass
...
>>> class SomeBindingSpec(pinject.BindingSpec):
... def configure(self, bind):
... bind('foo', to_instance='foo-instance')
...
>>> obj_graph = pinject.new_object_graph(binding_specs=[SomeBindingSpec()])
>>> some_class = obj_graph.provide(SomeClass)
>>> print some_class.foo
'foo-instance'
>>>
If you have two classes named the same thing, Pinject will have two different (and thus conflicting) implicit bindings. But Pinject will not complain unless you try to use those bindings. Pinject will complain if you try to create different (and thus conflicting) explicit bindings.
Pinject tries to strike a balance between being helpful and being safe. Sometimes, you may want or need to change this balance.
new_object_graph() uses implicit bindings by default. If you worry that
you may accidentally inject a class or use a provider function
unintentionally, then you can make new_object_graph() ignore implicit
bindings, by setting only_use_explicit_bindings=True. If you do so, then
Pinject will only use explicit bindings.
If you want to promote an implicit binding to be an explicit binding, you can
annotate the corresponding class with @inject(). The @inject()
decorator lets you create explicit bindings without needing to create binding
specs, as long as you can live with the binding defaults (e.g., no annotations
on args, see below).
.. code-block:: python
>>> class ExplicitlyBoundClass(object):
... @pinject.inject()
... def __init__(self, foo):
... self.foo = foo
...
>>> class ImplicitlyBoundClass(object):
... def __init__(self, foo):
... self.foo = foo
...
>>> class SomeBindingSpec(pinject.BindingSpec):
... def configure(self, bind):
... bind('foo', to_instance='explicit-foo')
...
>>> obj_graph = pinject.new_object_graph(binding_specs=[SomeBindingSpec()],
... only_use_explicit_bindings=True)
>>> # obj_graph.provide(ImplicitlyBoundClass) # would raise a NonExplicitlyBoundClassError
>>> some_class = obj_graph.provide(ExplicitlyBoundClass)
>>> print some_class.foo
'explicit-foo'
>>>
You can also promote an implicit binding to explicit by using
@annotated_arg() (see below), with or without @inject() as well.
(Previous versions of Pinject included an @injectable decorator. That is
deprecated in favor of @inject(). Note that @inject() needs parens,
whereas @injectable didn't.)
On the opposite side of permissiveness, Pinject by default will complain if a
provider method returns None. If you really want to turn off this default
behavior, you can pass allow_injecting_none=True to
new_object_graph().
Pinject annotations let you have different objects injected for the same arg name. For instance, you may have two classes in different parts of your codebase named the same thing, and you want to use the same arg name in different parts of your codebase.
On the arg side, an annotation tells Pinject only to inject using a binding
whose binding key includes the annotation object. You can use
@annotate_arg() on an initializer, or on a provider method, to specify the
annotation object.
On the binding side, an annotation changes the binding so that the key of the
binding includes the annotation object. When using bind() in a binding
spec's configure() method, you can pass an annotated_with arg to
specify the annotation object.
.. code-block:: python
>>> class SomeClass(object):
... @pinject.annotate_arg('foo', 'annot')
... def __init__(self, foo):
... self.foo = foo
...
>>> class SomeBindingSpec(pinject.BindingSpec):
... def configure(self, bind):
... bind('foo', annotated_with='annot', to_instance='foo-with-annot')
... bind('foo', annotated_with=12345, to_instance='12345-foo')
...
>>> obj_graph = pinject.new_object_graph(binding_specs=[SomeBindingSpec()])
>>> some_class = obj_graph.provide(SomeClass)
>>> print some_class.foo
'foo-with-annot'
>>>
Also on the binding side, when defining a provider method, you can use the
@provides() decorator. The decorator lets you pass an annotated_with
arg to specify the annotation object. The decorator's first param,
arg_name also lets you override what arg name you want the provider to be
for. This is optional but useful if you want the same binding spec to have
two provider methods for the same arg name but annotated differently.
(Otherwise, the methods would need to be named the same, since they're for the
same arg name.)
.. code-block:: python
>>> class SomeClass(object):
... @pinject.annotate_arg('foo', 'annot')
... def __init__(self, foo):
... self.foo = foo
...
>>> class SomeBindingSpec(pinject.BindingSpec):
... @pinject.provides('foo', annotated_with='annot')
... def provide_annot_foo(self):
... return 'foo-with-annot'
... @pinject.provides('foo', annotated_with=12345)
... def provide_12345_foo(self):
... return '12345-foo'
...
>>> obj_graph = pinject.new_object_graph(binding_specs=[SomeBindingSpec()])
>>> some_class = obj_graph.provide(SomeClass)
>>> print some_class.foo
'foo-with-annot'
>>>
When requiring a binding, via the require arg passed into the
configure() method of a binding spec, you can pass the arg
annotated_with to require an annotated binding.
.. code-block:: python
>>> class MainBindingSpec(pinject.BindingSpec):
... def configure(self, require):
... require('foo', annotated_with='annot')
...
>>> class NonSatisfyingBindingSpec(pinject.BindingSpec):
... def configure(self, bind):
... bind('foo', to_instance='an-unannotated-foo')
...
>>> class SatisfyingBindingSpec(pinject.BindingSpec):
... def configure(self, bind):
... bind('foo', annotated_with='annot', to_instance='an-annotated-foo')
...
>>> obj_graph = pinject.new_object_graph(
... binding_specs=[MainBindingSpec(), SatisfyingBindingSpec()]) # works
>>> # obj_graph = pinject.new_object_graph(
... # binding_specs=[MainBindingSpec(),
... # NonSatisfyingBindingSpec()]) # would raise a MissingRequiredBindingError
>>>
You can use any kind of object as an annotation object as long as it
implements __eq__() and __hash__().
By default, Pinject remembers the object it injected into a (possibly annotated) arg, so that it can inject the same object into other args with the same name. This means that, for each arg name, a single instance of the bound-to class, or a single instance returned by a provider method, is created by default.
.. code-block:: python
>>> class SomeClass(object):
... def __init__(self, foo):
... self.foo = foo
...
>>> class SomeBindingSpec(pinject.BindingSpec):
... def provide_foo(self):
... return object()
...
>>> obj_graph = pinject.new_object_graph(binding_specs=[SomeBindingSpec()])
>>> some_class_1 = obj_graph.provide(SomeClass)
>>> some_class_2 = obj_graph.provide(SomeClass)
>>> print some_class_1.foo is some_class_2.foo
True
>>>
In some cases, you may want to create new instances, always or sometimes, instead of reusing them each time they're injected. If so, you want to use scopes.
A scope controls memoization (i.e., caching). A scope can choose to cache never, sometimes, or always.
Pinject has two built-in scopes. Singleton scope (SINGLETON) is the
default and always caches. Prototype scope (PROTOTYPE) is the other
built-in option and does no caching whatsoever.
Every binding is associated with a scope. You can specify a scope for a
binding by decorating a provider method with @in_scope(), or by passing an
in_scope arg to bind() in a binding spec's configure() method.
.. code-block:: python
>>> class SomeClass(object):
... def __init__(self, foo):
... self.foo = foo
...
>>> class SomeBindingSpec(pinject.BindingSpec):
... @pinject.provides(in_scope=pinject.PROTOTYPE)
... def provide_foo(self):
... return object()
...
>>> obj_graph = pinject.new_object_graph(binding_specs=[SomeBindingSpec()])
>>> some_class_1 = obj_graph.provide(SomeClass)
>>> some_class_2 = obj_graph.provide(SomeClass)
>>> print some_class_1.foo is some_class_2.foo
False
>>>
If a binding specifies no scope explicitly, then it is in singleton scope. Implicit class bindings are always in singleton scope.
Memoization of class bindings works at the class level, not at the binding key level. This means that, if you bind two arg names (or the same arg name with two different annotations) to the same class, and the class is in a memoizing scope, then the same class instance will be provided when you inject the different arg names.
.. code-block:: python
>>> class InjectedClass(object):
... pass
...
>>> class SomeObject(object):
... def __init__(self, foo, bar):
... self.foo = foo
... self.bar = bar
...
>>> class SomeBindingSpec(pinject.BindingSpec):
... def configure(self, bind):
... bind('foo', to_class=InjectedClass)
... bind('bar', to_class=InjectedClass)
...
>>> obj_graph = pinject.new_object_graph(
... binding_specs=[SomeBindingSpec()])
>>> some_object = obj_graph.provide(SomeObject)
>>> print some_object.foo is some_object.bar
True
>>>
Pinject memoizes class bindings this way because this is more likely to be what you mean if you bind two different arg names to the same class in singleton scope: you want only one instance of the class, even though it may be injected in multiple places.
Sometimes, you need to inject not just a single instance of some class, but rather you need to inject the ability to create instances on demand. (Clearly, this is most useful when the binding you're using is not in the singleton scope, otherwise you'll always get the same instance, and you may as well just inject that..)
You could inject the Pinject object graph, but you'd have to do that dependency injection manually (Pinject doesn't inject itself!), and you'd be injecting a huge set of capabilities when your class really only needs to instantiate objects of one type.
To solve this, Pinject creates provider bindings for each bound arg name.
It will look at the arg name for the prefix provide_, and if it finds that
prefix, it assumes you want to inject a provider function for whatever the
rest of the arg name is. For instance, if you have an arg named
provide_foo_bar, then Pinject will inject a zero-arg function that, when
called, provides whatever the arg name foo_bar is bound to.
.. code-block:: python
>>> class Foo(object):
... def __init__(self):
... self.forty_two = 42
...
>>> class SomeBindingSpec(pinject.BindingSpec):
... def configure(self, bind):
... bind('foo', to_class=Foo, in_scope=pinject.PROTOTYPE)
...
>>> class NeedsProvider(object):
... def __init__(self, provide_foo):
... self.provide_foo = provide_foo
...
>>> obj_graph = pinject.new_object_graph(binding_specs=[SomeBindingSpec()])
>>> needs_provider = obj_graph.provide(NeedsProvider)
>>> print needs_provider.provide_foo() is needs_provider.provide_foo()
False
>>> print needs_provider.provide_foo().forty_two
42
>>>
Pinject will always look for the provide_ prefix as a signal to inject a
provider function, anywhere it injects dependencies (initializer args, binding
spec provider methods, etc.). This does mean that it's quite difficult, say,
to inject an instance of a class named ProvideFooBar into an arg named
provide_foo_bar, but assuming you're naming your classes as noun phrases
instead of verb phrases, this shouldn't be a problem.
Watch out: don't confuse
provide_something with provider functions; andProvider bindings are useful when you want to create instances of a class on demand. But a zero arg provider function will always return an instance configured the same way (within a given scope). Sometimes, you want the ability to parameterize the provided instances, e.g., based on run-time user configuration. You want the ability to create instances where part of the initialization data is provided per-instance at run-time and part of the initialization data is injected as dependencies.
To do this, other dependency injection libraries have you define factory classes. You inject dependencies into the factory class's initializer function, and then you call the factory class's creation method with the per-instance data.
.. code-block:: python
>>> class WidgetFactory(object):
... def __init__(self, widget_polisher):
... self._widget_polisher = widget_polisher
... def new(self, color): # normally would contain some non-trivial code...
... return some_function_of(self._widget_polisher, color)
...
>>> class SomeBindingSpec(pinject.BindingSpec):
... def provide_something_with_colored_widgets(self, colors, widget_factory):
... return SomethingWithColoredWidgets(
... [widget_factory.new(color) for color in colors])
...
>>>
You can follow this pattern in Pinject, but it involves boring boilerplate for
the factory class, saving away the initializer-injected dependencies to be
used in the creation method. Plus, you have to create yet another
...Factory class, which makes you feel like you're programming in java,
not python.
As a less repetitive alternative, Pinject lets you use partial injection on
the provider functions returned by provider bindings. You use the
@inject() decorator to tell Pinject ahead of time which args you expect to
pass directly (vs. automatic injection), and then you pass those args directly
when calling the provider function.
.. code-block:: python
>>> class SomeBindingSpec(pinject.BindingSpec):
... @pinject.inject(['widget_polisher'])
... def provide_widget(self, color, widget_polisher):
... return some_function_of(widget_polisher, color)
... def provide_something_needing_widgets(self, colors, provide_widget):
... return SomethingNeedingWidgets(
... [provide_widget(color) for color in colors])
...
>>>
The first arg to @inject(), arg_names, specifies which args of the
decorated method should be injected as dependencies. If specified, it must be
a non-empty sequence of names of the decorated method's args. The remaining
decorated method args will be passed directly.
In the example above, note that, although there is a method called
provide_widget() and an arg of provide_something_needing_widgets()
called provide_widget, these are not exactly the same! The latter is a
dependency-injected wrapper around the former. The wrapper ensures that the
color arg is passed directly and then injects the widget_polisher
dependency.
The @inject() decorator works to specify args passed directly both for
provider bindings to provider methods (as in the example above) and for
provider bindings to classes (where you can pass args directly to the
initializer, as in the example below).
.. code-block:: python
>>> class Widget(object):
... @pinject.inject(['widget_polisher'])
... def __init__(self, color, widget_polisher):
... pass # normally something involving color and widget_polisher
...
>>> class SomeBindingSpec(pinject.BindingSpec):
... def provide_something_needing_widgets(self, colors, provide_widget):
... return SomethingNeedingWidgets(
... [provide_widget(color) for color in colors])
...
>>>
The @inject() decorator also takes an all_except arg. You can use
this, instead of the (first positional) arg_names arg, if it's clearer and
more concise to say which args are not injected (i.e., which args are passed
directly).
.. code-block:: python
>>> class Widget(object):
... # equivalent to @pinject.inject(['widget_polisher']):
... @pinject.inject(all_except=['color'])
... def __init__(self, color, widget_polisher):
... pass # normally something involving color and widget_polisher
...
>>>
If both arg_names and all_except are omitted, then all args are
injected by Pinject, and none are passed directly. (Both arg_names and
all_except may not be specified at the same time.) Wildcard positional
and keyword args (i.e., *pargs and **kwargs) are always passed
directly, not injected.
If you use @inject() to mark at least one arg of a provider method (or
initializer) as passed directly, then you may no longer directly inject that
provider method's corresponding arg name. You must instead use a provider
binding to inject a provider function, and then pass the required direct
arg(s), as in the examples above.
If you want to, you can create your own custom scope. A custom scope is useful when you have some objects that need to be reused (i.e., cached) but whose lifetime is shorter than the entire lifetime of your program.
A custom scope is any class that implements the Scope interface.
.. code-block:: python
class Scope(object):
def provide(self, binding_key, default_provider_fn):
raise NotImplementedError()
The binding_key passed to provide() will be an object implementing
__eq__() and __hash__() but otherwise opaque (you shouldn't need to
introspect it). You can think of the binding key roughly as encapsulating the
arg name and annotation (if any). The default_provider_fn passed to
provide() is a zero-arg function that, when called, provides an instance
of whatever should be provided.
The job of a scope's provide() function is to return a cached object if
available and appropriate, otherwise to return (and possibly cache) the result
of calling the default provider function.
Scopes almost always have other methods that control clearing the scope's cache. For instance, a scope may have "enter scope" and "exit scope" methods, or a single direct "clear cache" method. When passing a custom scope to Pinject, your code should keep a handle to the custom scope and use that handle to clear the scope's cache at the appropriate time.
You can use one or more custom scopes by passing a map from scope identifier
to scope as the id_to_scope arg of new_object_graph().
.. code-block:: python
>>> class MyScope(pinject.Scope):
... def __init__(self):
... self._cache = {}
... def provide(self, binding_key, default_provider_fn):
... if binding_key not in self._cache:
... self._cache[binding_key] = default_provider_fn()
... return self._cache[binding_key]
... def clear(self):
... self._cache = {}
...
>>> class SomeClass(object):
... def __init__(self, foo):
... self.foo = foo
...
>>> class SomeBindingSpec(pinject.BindingSpec):
... @pinject.provides(in_scope='my custom scope')
... def provide_foo(self):
... return object()
...
>>> my_scope = MyScope()
>>> obj_graph = pinject.new_object_graph(
... binding_specs=[SomeBindingSpec()],
... id_to_scope={'my custom scope': my_scope})
>>> some_class_1 = obj_graph.provide(SomeClass)
>>> some_class_2 = obj_graph.provide(SomeClass)
>>> my_scope.clear()
>>> some_class_3 = obj_graph.provide(SomeClass)
>>> print some_class_1.foo is some_class_2.foo
True
>>> print some_class_2.foo is some_class_3.foo
False
>>>
A scope identifier can be any object implementing __eq__() and
__hash__().
If you plan to use Pinject in a multi-threaded environment (and even if you
don't plan to now but may some day), you should make your custom scope
thread-safe. The example custom scope above could be trivially (but more
verbosely) rewritten to be thread-safe, as in the example below. The lock is
reentrant so that something in MyScope can be injected into something else
in MyScope.
.. code-block:: python
>>> class MyScope(pinject.Scope):
... def __init__(self):
... self._cache = {}
... self._rlock = threading.RLock()
... def provide(self, binding_key, default_provider_fn):
... with self._rlock:
... if binding_key not in self._cache:
... self._cache[binding_key] = default_provider_fn()
... return self._cache[binding_key]
... def clear(self):
... with self._rlock:
... self._cache = {}
>>>
To prevent yourself from injecting objects where they don't belong, you may want to validate one object being injected into another w.r.t. scope.
For instance, you may have created a custom scope for HTTP requests handled by your program. Objects in request scope would be cached for the duration of a single HTTP request. You may want to verify that objects in request scope never get injected into objects in singleton scope. Such an injection is likely not to make semantic sense, since it would make something tied to one HTTP request be used for the duration of your program.
Pinject lets you pass a validation function as the
is_scope_usable_from_scope arg to new_object_graph(). This function
takes two scope identifiers and returns True iff an object in the first
scope can be injected into an object of the second scope.
.. code-block:: python
>>> class RequestScope(pinject.Scope):
... def start_request(self):
... self._cache = {}
... def provide(self, binding_key, default_provider_fn):
... if binding_key not in self._cache:
... self._cache[binding_key] = default_provider_fn()
... return self._cache[binding_key]
...
>>> class SomeClass(object):
... def __init__(self, foo):
... self.foo = foo
...
>>> class SomeBindingSpec(pinject.BindingSpec):
... @pinject.provides(in_scope=pinject.SINGLETON)
... def provide_foo(bar):
... return 'foo-' + bar
... @pinject.provides(in_scope='request scope')
... def provide_bar():
... return '-bar'
...
>>> def is_usable(scope_id_inner, scope_id_outer):
... return not (scope_id_inner == 'request scope' and
... scope_id_outer == scoping.SINGLETON)
...
>>> my_request_scope = RequestScope()
>>> obj_graph = pinject.new_object_graph(
... binding_specs=[SomeBindingSpec()],
... id_to_scope={'request scope': my_request_scope},
... is_scope_usable_from_scope=is_usable)
>>> my_request_scope.start_request()
>>> # obj_graph.provide(SomeClass) # would raise a BadDependencyScopeError
>>>
The default scope accessibility validator allows objects from any scope to be injected into objects from any other scope.
If your code follows PEP8 naming coventions, then you're likely happy with the
default implicit bindings (where the class FooBar gets bound to the arg
name foo_bar) and where provide_foo_bar() is a binding spec's provider
method for the arg name foo_bar.
But if not, read on!
new_object_graph() takes a get_arg_names_from_class_name arg. This is
the function that is used to determine implicit class bindings. This function
takes in a class name (e.g., FooBar) and returns the arg names to which
that class should be implicitly bound (e.g., ['foo_bar']). Its default
behavior is described in the "implicit class bindings" section above, but that
default behavior can be overridden.
For instance, suppose that your code uses a library that names many classes
with the leading letter X (e.g., XFooBar), and you'd like to be able to
bind that to a corresponding arg name without the leading X (e.g.,
foo_bar).
.. code-block:: python
>>> import re
>>> def custom_get_arg_names(class_name):
... stripped_class_name = re.sub('^_?X?', '', class_name)
... return [re.sub('(?!^)([A-Z]+)', r'_\1', stripped_class_name).lower()]
...
>>> print custom_get_arg_names('XFooBar')
['foo_bar']
>>> print custom_get_arg_names('XLibraryClass')
['library_class']
>>> class OuterClass(object):
... def __init__(self, library_class):
... self.library_class = library_class
...
>>> class XLibraryClass(object):
... def __init__(self):
... self.forty_two = 42
...
>>> obj_graph = pinject.new_object_graph(
... get_arg_names_from_class_name=custom_get_arg_names)
>>> outer_class = obj_graph.provide(OuterClass)
>>> print outer_class.library_class.forty_two
42
>>>
The function passed as the get_arg_names_from_class_name arg to
new_object_graph() can return as many or as few arg names as it wants. If
it always returns the empty list (i.e., if it is lambda _: []), then that
disables implicit class bindings.
The standard binding spec methods to configure bindings and declare
dependencies are named configure and dependencies, by default. If you
need to, you can change their names by passing configure_method_name
and/or dependencies_method_name as args to new_object_graph().
.. code-block:: python
>>> class NonStandardBindingSpec(pinject.BindingSpec):
... def Configure(self, bind):
... bind('forty_two', to_instance=42)
...
>>> class SomeClass(object):
... def __init__(self, forty_two):
... self.forty_two = forty_two
...
>>> obj_graph = pinject.new_object_graph(
... binding_specs=[NonStandardBindingSpec()],
... configure_method_name='Configure')
>>> some_class = obj_graph.provide(SomeClass)
>>> print some_class.forty_two
42
>>>
new_object_graph() takes a get_arg_names_from_provider_fn_name arg.
This is the function that is used to identify provider methods on binding
specs. This function takes in the name of a potential provider method (e.g.,
provide_foo_bar) and returns the arg names for which the provider method
is a provider, if any (e.g., ['foo_bar']). Its default behavior is
described in the "provider methods" section above, but that default behavior
can be overridden.
For instance, suppose that you work for a certain large corporation whose
python style guide makes you name functions in CamelCase, and so you need
to name the provider method for the arg name foo_bar more like
ProvideFooBar than provide_foo_bar.
.. code-block:: python
>>> import re
>>> def CustomGetArgNames(provider_fn_name):
... if provider_fn_name.startswith('Provide'):
... provided_camelcase = provider_fn_name[len('Provide'):]
... return [re.sub('(?!^)([A-Z]+)', r'_\1', provided_camelcase).lower()]
... else:
... return []
...
>>> print CustomGetArgNames('ProvideFooBar')
['foo_bar']
>>> print CustomGetArgNames('ProvideFoo')
['foo']
>>> class SomeClass(object):
... def __init__(self, foo):
... self.foo = foo
...
>>> class SomeBindingSpec(pinject.BindingSpec):
... def ProvideFoo(self):
... return 'some-foo'
...
>>> obj_graph = pinject.new_object_graph(
... binding_specs=[SomeBindingSpec()],
... get_arg_names_from_provider_fn_name=CustomGetArgNames)
>>> some_class = obj_graph.provide(SomeClass)
>>> print some_class.foo
'some-foo'
>>>
The function passed as the get_arg_names_from_provider_fn_name arg to
new_object_graph() can return as many or as few arg names as it wants. If
it returns an empty list, then that potential provider method is assumed not
actually to be a provider method.
Pinject raises helpful exceptions whose messages include the file and line number of errors. So, Pinject by default will shorten the stack trace of exceptions that it raises, so that you don't see the many levels of function calls within the Pinject library.
In some situations, though, the complete stack trace is helpful, e.g., when
debugging Pinject, or when your code calls Pinject, which calls back into your
code, which calls back into Pinject. In such cases, to disable exception
stack shortening, you can pass use_short_stack_traces=False to
new_object_graph().
Pinject has a few things to watch out for.
Pinject's default scope is SINGLETON. If you have a multi-threaded
program, it's likely that some or all of the things that Pinject provides from
singleton scope will be used in multiple threads. So, it's important that you
ensure that such classes are thread-safe.
Similarly, it's important that your custom scope classes are thread-safe. Even if the objects they provide are only used in a single thread, it may be that the object graph (and therefore the scope itself) will be used simultaneously in multiple threads.
Remember to make locks re-entrant on your custom scope classes, or otherwise deal with one object in your custom scope trying to inject another object in your custom scope.
That's it for gotchas, for now.
If you are already familiar with dependency injection libraries such as Guice, this section gives you a condensed high level summary of Pinject and how it might be similar to or different than other dependency injection libraries. (If you don't understand it, no problem. The rest of the documentation covers everything listed here.)
some_class arg names for SomeClass classes.__init__() is marked with @inject().foo to provider methods provide_foo().None by default, but you can turn off that check.v0.13: master
v0.12: 28 Nov, 2018
v0.10.2:
v0.10.1:
configure() binding spec method.v0.10:
__eq__() to BindingSpec, so that DAG binding spec dependencies can have equal but not identical dependencies.configure() and dependencies() binding spec method names.@injectable in favor of @inject.require arg to allow binding spec configure methods to declare but not define bindings.@copy_args_to_internal_fields and @copy_args_to_public_fields.InjectableDecoratorAppliedToNonInitError to DecoratorAppliedToNonInitError.v0.9:
use_short_stack_traces arg to new_object_graph().@provides on single provider method.v0.8:
Apache-2.0
Though Google owns this project's copyright, this project is not an official Google product.
FAQs
A pythonic dependency injection library
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