Python Design Patterns

Description

• This repository contains an overview of the Gang of Four (GoF) design patterns with modern-day variations, adjustments, and discussions in Python.

Learning Objectives

• SOLID Design Principles:
  • Single Responsibility Principle
  • Open-Closed Principle
  • Liskov Substitution Principle
  • Interface Segregation Principle
  • Dependency Inversion Principle
• Creational Design Patterns:
  • Builder
    • A builder is a separate component for building an object
    • Can either give builder an initializer or return it via a static function
    • To make builder fluent, return self
    • Different facets of an object can be built with different builders working in tandem via a base class
  • Factories (Factory Method and Abstract Factory)
    • A factory method is a static method that creates object
    • A factory is an entity that can take care of object creation
    • A factory can be external or reside inside the object as an inner class
    • Hierarchies of factories can be used to create related objects
  • Prototype
    • To implement a prototype, partially construct an object and store it somewhere
    • Deep copy the prototype
    • Customize the resulting instance
    • A factory provides a convenient API for using prototypes
  • Singleton
    • Different realizations of Singleton: custom allocator, decorator, metaclass
    • Laziness is easy, just init on first request
    • Monostate variation
    • Testability issues
• Structural Design Patterns:
  • Adapter
    • Implementing an Adapter is easy
    • Determine the API you have and the API you need
    • Create a component which aggregates (has a reference to, ...) the adaptee
    • Intermediate representations can pile up: use caching and other optimizations
  • Bridge
    • Decouple abstraction from implementation
    • Both can exist as hierarchies
    • A stronger form of encapsulation
  • Composite
    • Objects can use other objects via inheritance/composition
    • Some composed and singular objects need similar/identical behaviors
    • Composite design pattern lets us treat both types of objects uniformly
    • Python supports iteration with __iter__ and Iterable ABC
    • A single object can itself iterable by yielding self from __iter__
  • Decorator
    • A decorator keeps the reference to the decorated object(s)
    • Adds utility attributes and methods to augment the object's features
    • May or may not forward calls to the underlying object
    • Proxying of underlying calls can be done dynamically
    • Python's functional decorators wrap functions; no direct relation to the GoF Decorator Pattern
  • Façade
    • Build a Facade to provide a simplified API over a set of classes
    • May wish to (optionally) expose internals through the facade
    • May allow users to 'escalate' to use more complex API
  • Flyweight
    • Store common data externally
    • Specify an index or a reference into the external data store
    • Define the idea of 'ranges' on homogeneuous collections and store data related to those ranges
  • Proxy
    • A proxy has the same interface as underlying object
    • To create a proxy, simple replicate the existing interface of an object
    • Add relevant functionality to the redefined member functions
    • Different proxies (communications, logging, caching, etc.) have completely different behaviors
• Behavioral Design Patterns
  • Chain of Responsibility
    • Chain of responsibility can be implemented as a chain of references or a centralized construct
    • Enlist objects in the chain, possibly controlling their order
    • Object removal from chain (e.g., __exit__)
  • Command
    • Encapsulate all details of an operation in a separate object
    • Define instruction for applything the command (either in the command itself, or elsewhere)
    • Optionally define instructions for undoing the command
    • Can create composite commands (a.k.a. macros)
  • Interpreter
    • Barring simple cases, an interpreter acts in two stages
    • Lexing turns text into a set of tokens
    • Parsing tokens into meaningful construct
  • Iterator
    • An iterator specified how you can traverse an object
    • Stateful iterators cannot be recursive
    • yield allows for much more succinct iteration
  • Mediator
    • Create the mediator and have each object in the system refer to it
      • E.g., in a property
    • Mediator engages in bidirectional communication with its connected components
    • Mediator has functions the components can call
    • Components have functions the mediator can call
    • Event processing (e.g., Rx) libraries make communication easier to implement
  • Memento
    • Mementos are used to roll back states arbitrarily
    • A memento is simply a token/handle class with typically no function of its own
    • A memento is not required to expose directly the state(s) to which it reverts the system
    • Can be used to implement undo/redo
  • Observer
    • Observer is an intrusive approach: an observable must provide an event to subscribe to
    • Subsciption and unsubscription with additional/removal of items in list
    • Property notifications are easy: dependent property notifications are tricky
  • State
    • Given sufficient complexity, it pays to formally define possible states and events/triggers
    • Can define
      • State entry/exit behaviors
      • Action when a particular event causes a transition
      • Guard conditions enabling/disabling a transition
      • Default action when no transitions are found for an event
  • Strategy
    • Define an algorithm at a high level
    • Define the interface you expect each strategy to follow
    • Provide for dynamic composition of strategies in the resulting object
  • Template
    • Define an algorithm at a high level in parent class
    • Define constituent parts as abstract methods/properties
    • Inherit the algorthm class, providing the necessary ovverides
  • Visitor
    • OOP double-dispatch approach is not necessary in Python
    • Make a visitor, decorating each 'overload' with @visitor
    • Call visit() and the entire structure gets traversed

Usage

• All files are created and executed on Mac OS X 10.11 with Python3 (version 3.7)

Awknowledgements

• Dmitri Nesteruk's course "Design Patterns in Python" on Udemy.com

Author

• Tu Vo - tuvo1106