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Description

serum is a fresh take on Dependency Injection in Python 3.

serum is pure python and has no dependencies.

Installation

> pip install serum

Quickstart

from serum import inject, dependency, Context


# Classes decorated with 'dependency' are injectable types.
@dependency 
class Log:
    def info(self, message: str):
        raise NotImplementedError()


class SimpleLog(Log):
    def info(self, message: str):
        print(message)


class StubLog(SimpleLog):
    def info(self, message: str):
        pass


@inject  # Dependencies are injected using a class decorator...
class NeedsLog:
    log: Log  # ...and class level annotations...


class NeedsSimpleLog:
    @inject  # ...or using a function decorator
    def __init__(self, log: SimpleLog):
        self.log = log 


@inject
class NeedsNamedDependency:
    named_dependency: str  # class level annotations annotated with a type that is not
                           # decorated with 'dependency' will be treated as a named
                           # dependency
                           

# Contexts provide dependencies
with Context(SimpleLog, named_dependency='this name is injected!'):
    assert isinstance(NeedsLog().log, SimpleLog)
    assert NeedsNamedDependency().named_dependency == 'this name is injected!'
    

# Contexts will always provide the most specific 
# subtype of the requested type. This allows you to change which
# dependencies are injected.
with Context(StubLog):
    NeedsLog().log.info('Hello serum!')  # doesn't output anything
    NeedsSimpleLog().log.info('Hello serum!')  # doesn't output anything

Documentation

• inject
• dependency
• Context
• singleton
• mock
• match
• IPython Integration

inject

inject is used to decorate functions and classes in which you want to inject dependencies.

from serum import inject, dependency

@dependency
class MyDependency:
    pass

@inject
def f(dependency: MyDependency):
    assert isinstance(dependency, MyDependency)

f()

Functions decorated with inject can be called as normal functions. serum will not attempt to inject arguments given at call time.

@inject
def f(dependency: MyDependency):
    print(dependency)

f('Overridden dependency')  #  outputs: Overridden dependency 

inject will instantiate classes decorated with dependency. In this way, your entire dependency graph can be specified using just inject and dependency.

Instances of simple types and objects you want to instantiate yourself can be injected using keyword arguments to Context.

@inject
def f(dependency: str):
    assert dependency == 'a named dependency'

with Context(dependency='a named dependency'):
    f()

inject can also be used to decorate classes.

@inject
class SomeClass:
    dependency: MyDependency 

This is roughly equivalent to:

class SomeClass:
    @inject
    def __init__(self, dependency: MyDependency):
        self.__dependency = dependency
    
    @property
    def dependency(self) -> MyDependency:
        return self.__dependency

Dependencies that are specified as class level annotations can be overridden using key-word arguments to __init__

assert SomeClass(dependency='Overridden!').dependency == 'Overridden!'

dependency

Classes decorated with dependency can be instantiated and injected by serum.

from serum import dependency, inject

@dependency
class Log:
    def info(self, message):
        print(message)


@inject
class NeedsLog:
    log: Log


assert isinstance(NeedsLog().log, Log)

serum relies on being able to inject all dependencies for dependency decorated classes recursively. To achieve this, serum assumes that the __init__ method of dependency decorated classes can be called without any arguments. This means that all arguments to __init__ of dependency decorated classes must be injected using inject.

@dependency
class SomeDependency:
    def method(self):
        pass


@inject
@dependency
class ValidDependency:  # OK!
    some_dependency: SomeDependency

    def __init__(self):
        ...


@dependency
class AlsoValidDependency:  # Also OK!
    @inject
    def __init__(self, some_dependency: SomeDependency):
        ...


@dependency
class InvalidDependency:
    def __init__(self, a):
        ...

@inject
def f(dependency: InvalidDependency):
    ...

f()  
# raises:
# TypeError: __init__() missing 1 required positional argument: 'a'

# The above exception was the direct cause of the following exception:

# InjectionError                            Traceback (most recent call last)
# ...
# InjectionError: Could not instantiate dependency <class 'InvalidDependency'> 
# when injecting argument "dependency" in <function f at 0x10a074ea0>.

Note that circular dependencies preventing instantiation of dependency decorated classes leads to an error.

@dependency
class AbstractA:
    pass

@dependency
class AbstractB:
    pass


class A(AbstractA):

    @inject
    def __init__(self, b: AbstractB):
        self.b = b

class B(AbstractB):
    @inject
    def __init__(self, a: AbstractA):
        self.a = a

@inject
class Dependent:
    a: AbstractA


with Context(A, B):
    Dependent().a  # raises: CircularDependency: Circular dependency encountered while injecting <class 'AbstractA'> in <B object at 0x1061e3898>

Context

Contexts provide implementations of dependencies. A Context will always provide the most specific subtype of the requested type (in Method Resolution Order).

@dependency
class Super:
    pass


class Sub(Super):
    pass

@inject
class NeedsSuper:
    instance: Super


with Context(Sub):
    assert isinstance(NeedsSuper().instance, Sub)

It is an error to inject a type in an Context that provides two or more equally specific subtypes of that type:

class AlsoSub(Super):
    pass


with Context(Sub, AlsoSub):
    NeedsSuper() # raises: AmbiguousDependencies: Attempt to inject type <class 'Log'> with equally specific provided subtypes: <class 'MockLog'>, <class 'FileLog'>

Contexts can also be used as decorators:

context = Context(Sub)

@context
def f():
    assert isinstance(NeedsSuper().instance, Sub)

You can provide named dependencies of any type using keyword arguments.

@inject
class Database:
    connection_string: str
    

connection_string = 'mysql+pymysql://root:[email protected]:3333/my_db'
context = Context(
    connection_string=connection_string
)
with context:
    assert Database().connection_string == connection_string

Contexts are local to each thread. This means that when using multi-threading each thread runs in its own context

import threading


@singleton
class SomeSingleton:
    pass


@inject
def worker(instance: SomeSingleton):
    print(instance)

with Context():
    worker() # outputs: <SomeSingleton object at 0x101f75470>
    threading.Thread(target=worker).start() # outputs: <SomeSingleton object at 0x1035fb320>

singleton

To always inject the same instance of a dependency in the same Context, annotate your type with singleton.

from serum import singleton


@singleton
class ExpensiveObject:
    pass


@inject
class NeedsExpensiveObject:
    expensive_instance: ExpensiveObject


instance1 = NeedsExpensiveObject()
instance2 = NeedsExpensiveObject()
assert instance1.expensive_instance is instance2.expensive_instance

Note that Singleton dependencies injected in different environments will not refer to the same instance.

with Context():
    instance1 = NeedsExpensiveObject()

with Context():
    assert instance1.expensive_instance is not NeedsExpensiveObject().expensive_instance

mock

serum has support for injecting MagicMocks from the builtin unittest.mock library in unittests using the mock utility function. Mocks are reset when the environment context is closed.

from serum import mock

@dependency
class SomeDependency:
    def method(self):
        return 'some value' 

@inject
class Dependent:
    dependency: SomeDependency


context = Context()
with context:
    mock_dependency = mock(SomeDependency)
    mock_dependency.method.return_value = 'some mocked value'
    instance = Dependent()
    assert instance.dependency is mock_dependency
    assert instance.dependency.method() == 'some mocked value'

with context:
    instance = Dependent()
    assert instance.dependency is not mock_dependency
    assert isinstance(instance.dependency, SomeDependency)

mock uses its argument to spec the injected instance of MagicMock. This means that attempting to call methods that are not defined by the mocked Component leads to an error

with context:
    mock_dependency = mock(SomeDependency)
    mock_dependency.no_method()  # raises: AttributeError: Mock object has no attribute 'no method'

Note that mock will only mock requests of the exact type supplied as its argument, but not requests of more or less specific types

from unittest.mock import MagicMock

@dependency
class Super:
    pass


class Sub(Super):
    pass


class SubSub(Sub):
    pass


@inject
class NeedsSuper:
    injected: Super


@inject
class NeedsSub:
    injected: Sub


@inject
class NeedsSubSub:
    injected: SubSub


with Context():
    mock(Sub)
    needs_super = NeedsSuper()
    needs_sub = NeedsSub()
    needs_subsub = NeedsSubSub()
    assert isinstance(needs_super.injected, Super)
    assert isinstance(needs_sub.injected, MagicMock)
    assert isinstance(needs_subsub.injected, SubSub)

match

match is small utility function for matching Context instances with values of an environment variable.

# my_script.py
from serum import match, dependency, Context, inject

@dependency
class BaseDependency:
    def method(self):
        raise NotImplementedError()


class ProductionDependency(BaseDependency):
    def method(self):
        print('Production!')


class TestDependency(BaseDependency):
    def method(self):
        print('Test!')


@inject
def f(dependency: BaseDependency):
    dependency.method()


context = match(
    environment_variable='MY_SCRIPT_ENV', 
    default=Context(ProductionDependency),
    PROD=Context(ProductionDependency),
    TEST=Context(TestDependency)
)

with context:
    f()

> python my_script.py
Production!

> MY_SCRIPT_ENV=PROD python my_script.py
Production!

> MY_SCRIPT_ENV=TEST python my_script.py
Test!

IPython Integration

It can be slightly annoying to import some Context and start it as a context manager in the beginning of every IPython session. Moreover, you quite often want to run an IPython REPL in a special context, e.g to provide configuration that is normally supplied through command line arguments in some other way.

To this end serum can act as an IPython extension. To activate it, add the following lines to your ipython_config.py:

c.InteractiveShellApp.extensions = ['serum']

Finally, create a file named ipython_context.py in the root of your project. In it, assign the Context instance you would like automatically started to a global variable named context:

# ipython_context.py
from serum import Context


context = Context()

IPython will now enter this context automatically in the beginning of every REPL session started in the root of your project.

Why?

If you've been researching Dependency Injection frameworks for python, you've no doubt come across this opinion:

You dont need Dependency Injection in python. You can just use duck typing and monkey patching!

The position behind this statement is often that you only need Dependency Injection in statically typed languages.

In truth, you don't really need Dependency Injection in any language, statically typed or otherwise. When building large applications that need to run in multiple environments however, Dependency Injection can make your life a lot easier. In my experience, excessive use of monkey patching for managing environments leads to a jumbled mess of implicit initialisation steps and if value is None type code.

In addition to being a framework, I've attempted to design serum to encourage designing classes that follow the Dependency Inversion Principle:

one should “depend upon abstractions, not concretions."

This is achieved by letting inheritance being the principle way of providing dependencies and allowing dependencies to be abstract.