OO Design Principles: SOLID

ISP

The "Interface Segregration" principle is -- perhaps -- the most important.

In a C++ or Java context, the more that's exposed to a collaborator, the wider the ripples from a change.

In a Python context, a too-rich interface is just annoying to understand, maintain, extend, and test.

One metric for paring down an interface is the number of mock objects needed for an isolated unit test.

(Some Facade and Adapter designs will have a lot of mocks because they're wrapping complicated things in a simple interface. Don't apply the "minimize mocks" metric blindly.)

LSP

Remember, Python has duck typing. LSP is something that applies if -- and only if -- inheritance is being used. A bunch of classes can all implement a common protocol without being suclasses of each other.

An Abstract Base Class has the abstract methods marked so that an object cannot be created. When all of the methods are defined by some subclass, it's now concrete and an object can be created.

A concrete base class can be extended as needed, and any class in the hierarchy is usable. There won't be any "Can't instantiate abstract class" exception.

In order to make LSP work, it's very helpful for subclasses to add features to base classes. Base classes should be a minimal foundation; think of them as a generalization. The extensions should add features; they will be specializations. (It's not mandatory, it's just kind of tedious to write "do nothing" methods for a subclass to take away a base class feature.)

OCP

This requires some consideration of the sources of change and the kinds of extensions that might be necessary.

It's rather difficult to do well when working on a problem domain that's not well understood. After creating some code -- and living with it -- it's easier to see what's likely to change and what's likely essential. Once the change vectors are more clear, it becomes easier to see what parts of a class are likely to be extended.

DIP

Dependency "Inversion" really means don't have simple names of classes everywhere.

In Java and C++ collaborators with a class would have a reference to that class compiled into them. Change some class, and the collaborators all need to be recompiled. No one likes this.

In Python, the name resolution happens at run-time, and there's no additional overhead from making a change. (The name lookups are an overhead that's inherent in Python.)

In Python, the dependency injection means assigning a target class to a variable instead of simply using it.

class SomeThing:
    def __init__(self, this: str, that: int) -> None:
        self.this = this
        self.that = that

class Collaborator:
    what_to_build: type[SomeThing] = SomeThing
    def __init__(self, arg: str) -> None:
        self.something: SomeThing = self.what_to_build("this", arg)

The name of the thing to build is a class variable in the Collaborator class. Making a change to the class used internally by the Collaborator class is isolated to this variable.

We can make a subclass of Collaborator with a new value for what_to_build and change it's behavior. We can go further, of course, and have some centralized configuration that names the classes to use. That can be handy in very complicated applications where a lot of things are likely to change.

As a practical matter, very few things change. A small configuration object with a few critical class references is all that's really required.

SRP

The most difficult of of the SOLID design principles is identifying a "single" responsibility. The question of responsibility often requires some qualifiers. It's important to consider responsibilities from which collaborator's perspective.

A class may do a bunch of things internally. But -- viewed from outside -- it's a single, atomic behavior.

This is where the nine GRASP patterns can come in handy, to implement a class with a single responsibility.