aspyx 0.1.0

A DI and AOP library for Python

aspyx

Table of Contents

• Introduction
• Registration
  • Class
  • Class Factory
  • Method
• Environment
  • Definition
  • Retrieval
• Lifecycle methods
• Post Processors
• Custom scopes
• AOP
• Configuration

Introduction

Aspyx is a small python libary, that adds support for both dependency injection and aop.

The following features are supported

• constructor injection
• method injection
• post processors
• factory classes and methods
• support for eager construction
• support for singleton and reuqest scopes
• possibilty to add custom scopes
• lifecycle events methods
• bundling of injectable object sets by environment classes including recursive imports and inheritance
• container instances that relate to environment classes and manage the lifecylce of related objects
• hierarchical environments

Let's look at a simple example

from aspyx.di import injectable, on_init, on_destroy, environment, Environment


@injectable()
class Foo:
    def __init__(self):
        pass

    def hello(msg: str):
        print(f"hello {msg}")


@injectable()  # eager and singleton by default
class Bar:
    def __init__(self, foo: Foo): # will inject the Foo dependency
        self.foo = foo

    @on_init() # a lifecycle callback called  after the constructor
    def init(self):
        ...


# this class will register all - specifically decorated - classes and factories in the own module
# In this case Foo and Bar

@environment()
class SampleEnvironment:
    # constructor

    def __init__(self):
        pass


# go, forrest

environment = SampleEnvironment(Configuration)

bar = env.get(Bar)
bar.foo.hello("Andi")

The concepts should be pretty familiar , as well as the names which are a combination of Spring and Angular names :-)

Let's add some aspects...

@advice
class SampleAdvice:
    def __init__(self):
        pass

    @before(methods().named("hello").of_type(Foo))
    def callBefore(self, invocation: Invocation):
        print("before Foo.hello(...)")

    @error(methods().named("hello").of_type(Foo))
    def callError(self, invocation: Invocation):
        print("error Foo.hello(...)")
        print(invocation.exception)

    @around(methods().named("hello"))
    def callAround(self, invocation: Invocation):
        print("around Foo.hello()")

        return invocation.proceed()

The invocation parameter stores the complete context of the current execution, which are

• the method
• args
• kwargs
• the result
• the possible caught error

Let's look at the details

Registration

Different mechanisms are available that make classes eligible for injection

Class

Any class annotated with @injectable is eligible for injection

Example:

@injectable()
class Foo:
    def __init__(self):
        pass

Please make sure, that the class defines a constructor, as this is required to determine injected instances.

The constructor can only define parameter types that are known as well to the container!

The decorator accepts the keyword arguments

• eager=True if True, the container will create the instances automatically while booting the environment
• scope="singleton" defines how often instances will be created. singleton will create it only once - per environment -, while request will recreate it on every injection request

Other scopes can be defined. Please check the corresponding chapter.

Class Factory

Classes that implement the Factory base class and are annotated with @factory will register the appropriate classes returned by the create method.

Example:

@factory()
class TestFactory(Factory[Foo]):
    def __init__(self):
        pass

    def create(self) -> Foo:
        return Foo()

As in @injectable, the same arguments are possible.

Method

Any injectable can define methods decorated with @create(), that will create appropriate instances.

Example:

@injectable()
class Foo:
    def __init__(self):
        pass

    @create(scope="request")
    def create(self) -> Baz:
        return Baz()

The same arguments as in @injectable are possible.

Environment

Definition

An Environment is the container that manages the lifecycle of objects. The set of classes and instances is determined by a constructor argument that controls the class registry.

Example:

@environment()
class SampleEnvironment:
    def __init__(self):
        pass

environment = Environment(SampleEnvironment)

The default is that all eligible classes, that are implemented in the containing module or in any submodule will be managed.

By adding an imports: list[Type] parameter, specifying other environment types, it will register the appropriate classes recursively.

Example:

@environment()
class SampleEnvironmen(imports=[OtherEnvironment])):
    def __init__(self):
        pass

Another possibility is to add a parent environment as an Environment constructor parameter

Example:

rootEnvironment = Environment(RootEnvironment)
environment = Environment(SampleEnvironment, parent=rootEnvironment)

The difference is, that in the first case, class instances of imported modules will be created in the scope of the own environment, while in the second case, it will return instances managed by the parent.

The method

shutdown()

is used when a container is not needed anymore. It will call any on_destroy() of all created instances.

Retrieval

def get(type: Type[T]) -> T

is used to retrieve object instances. Depending on the respective scope it will return either cached instances or newly instantiated objects.

The container knows about class hierarchies and is able to get base classes, as long as there is only one implementation.

In case of ambiguities, it will throw an exception.

Please be aware, that a base class are not required to be annotated with @injectable, as this would mean, that it could be created on its own as well. ( Which is possible as well, btw. )

Lifecycle methods

It is possible to declare methods that will be called from the container

• @on_init() called after the constructor and all other injections.
• @on_destroy() called after the container has been shut down

Post Processors

As part of the instantiation logic it is possible to define post processors that execute any side effect on newly created instances.

Example:

@injectable()
class SamplePostProcessor(PostProcessor):
    def process(self, instance: object, environment: Environment):
        print(f"created a {instance}")

Any implementing class of PostProcessor that is eligible for injection will be called by passing the new instance.

Please be aware, that a post processor will only handle instances after its own registration.

As injectables within a single file will be handled in the order as they are declared, a post processor will only take effect for all classes after its declaration!

Custom scopes

As explained, available scopes are "singleton" and "request".

It is easily possible to add custom scopes by inheriting the base-class Scope, decorating the class with @scope(<name>) and overriding the method get

def get(self, provider: AbstractInstanceProvider, environment: Environment, argProvider: Callable[[],list]):

Arguments are:

• provider the actual provider that will create an instance
• environmentthe requesting environment
• argPovider a function that can be called to compute the required arguments recursively

Example: The simplified code of the singleton provider ( disregarding locking logic )

@scope("singleton")
class SingletonScope(Scope):
    # constructor

    def __init__(self):
        super().__init__()

        self.value = None

    # override

    def get(self, provider: AbstractInstanceProvider, environment: Environment, argProvider: Callable[[],list]):
        if self.value is None:
            self.value = provider.create(environment, *argProvider())

        return self.value

AOP

It is possible to define different Aspects, that will be part of method calling flow. This logic fits nicely in the library, since the DI framework controls the instantiation logic and can handle aspects within a regular post processor.

Advice classes need to be part of classes that add a @advice() decorator and can define methods that add aspects.

@advice()
class SampleAdvice:
    def __init__(self):  # could inject dependencies
        pass

    @before(methods().named("hello").of_type(Foo))
    def callBefore(self, invocation: Invocation):
        # arguments: invocation.args
        print("before Foo.hello(...)")

    @error(methods().named("hello").of_type(Foo))
    def callError(self, invocation: Invocation):
        print("error Foo.hello(...)")
        print(invocation.exception)

    @around(methods().named("hello"))
    def callAround(self, invocation: Invocation):
        print("around Foo.hello()")

        return invocation.proceed()  # will leave a result in invocation.result or invocation.exception in case of an exception

Different aspects - with the appropriate decorator - are possible:

• before
  methods that will be executed prior to the original method
• around
  methods that will be executed around to the original method giving it the possibility add side effects or even change the parameters.
• after
  methods that will be executed after to the original method
• error
  methods that will be executed in case of a caught exception, which can be retrieved by invocation.exception

All methods are expected to hava single Invocation parameter, that stores, the function, args and kwargs, the return value and possible exceptions.

It is essential for around methods to call proceed() on the invocation, which will call the next around method in the chain and finally the original method. If the proceed is called with parameters, they will replace the original parameters!

The arguments to the corresponding decorators control, how aspects are associated with which methods. A fluent interface is used describe the mapping. The parameters restrict either methods or classes and are constructed by a call to either methods() or classes().

Both add the fluent methods:

• of_type(type: Type)
  defines the matching classes
• named(name: str)
  defines method or class names
• matches(re: str)
  defines regular expressions for methods or classes
• decorated_with(type: Type)
  defines decorators on methods or classes

The fluent methods named, matches and of_type can be called multiple timess!

Configuration

It is possible to inject configuration values, by decorating methods with @value(<name>) given a configuration key.

@injectable()
class Foo:
    def __init__(self):
        pass

    @value("OS")
    def inject_value(self, os: str):
        ...

This concept relies on a central object ConfigurationManager that stores the overall configuration values as provided by so called configuration sources that are defined as follows.

class ConfigurationSource(ABC):
    def __init__(self, manager: ConfigurationManager):
        manager._register(self)
        pass

    @abstractmethod
    def load(self) -> dict:
        pass

The load method is able to return a tree-like structure by returning a dict.

As a default environment variables are already supported.

Other sources can be added dynamically by just registering them.

@injectable()
class SampleConfigurationSource(ConfigurationSource):
    # constructor

    def __init__(self, manager: ConfigurationManager):
        super().__init__(manager)


    def load(self) -> dict:
        return {
            "a": 1, 
            "b": {
                "d": "2", 
                "e": 3, 
                "f": 4
                }
            }