🎉 Ultra-simplified explanation to design patterns! 🎉
...demonstrated with PEP8 compliant python.
A topic that can easily make anyone's mind wobble. Here I try to make them stick in to our minds by explaining them in the simplest way possible, while showing best practices.
This version has been updated with the assumption that the reader has a strong understanding of python, all examples use python 3.5+
Design patterns are solutions to recurring problems; guidelines on how to tackle certain problems. They are not classes, packages or libraries that you can plug into your application and wait for the magic to happen. These are, rather, guidelines on how to tackle certain problems in certain situations.
Design patterns are solutions to recurring problems; guidelines on how to tackle certain problems
Wikipedia describes them as
In software engineering, a software design pattern is a general reusable solution to a commonly occurring problem within a given context in software design. It is not a finished design that can be transformed directly into source or machine code. It is a description or template for how to solve a problem that can be used in many different situations.
- Design patterns are not a silver bullet to all your problems.
- Do not try to force them; bad things are supposed to happen, if done so. Keep in mind that design patterns are solutions to problems, not solutions finding problems; so don't overthink.
- If used in a correct place in a correct manner, they can prove to be a savior; or else they can result in a horrible mess of a code.
In plain words
Creational patterns are focused towards how to instantiate an object or group of related objects.
Wikipedia says
In software engineering, creational design patterns are design patterns that deal with object creation mechanisms, trying to create objects in a manner suitable to the situation. The basic form of object creation could result in design problems or added complexity to the design. Creational design patterns solve this problem by somehow controlling this object creation.
In plain words
Simple factory simply generates an instance for client without exposing any instantiation logic to the client
Real world example
Consider, you are building a house and you need doors. It would be a mess if every time you need a door, you put on your carpenter clothes and start making a door in your house. Instead you get it made from a factory.
Wikipedia says
In object-oriented programming (OOP), a factory is an object for creating other objects – formally a factory is a function or method that returns objects of a varying prototype or class from some method call, which is assumed to be "new".
Programmatic Example
First off, we create an interface Door and it's implementation WoodenDoor
import abc
class Door(abc.ABC):
@abc.abstractmethod
def get_width(self) -> int:
pass
@abc.abstractmethod
def get_height(self) -> int:
pass
def __str__(self):
return f'{self.get_width()} x {self.get_height()}'
class WoodenDoor(Door):
def __init__(self, width, height):
self._width = width
self._height = height
def get_width(self):
return self._width
def get_height(self):
return self._heightThen we have our door factory that makes a door instance (with default size) and returns it
class DoorFactory:
@staticmethod
def make_door(width=3, height=7) -> Door:
return WoodenDoor(width, height)And then it can be used like
if __name__ == '__main__':
# The factory will produce a basic sized door by default
basic_door = DoorFactory.make_door()
print('basic door:', str(basic_door))
print('width:', basic_door.get_width())
print('height:', basic_door.get_height())
# We need a large door, so we can define a custom width and height
large_door = DoorFactory.make_door(width=6, height=14)
print('large door:', str(large_door))
print('width:', large_door.get_width())
print('height:', large_door.get_height())basic door: 3 x 7
width: 3
height: 7
large door: 6 x 14
width: 6
height: 14When to Use?
When creating an object is not just a few assignments and involves some logic, it makes sense to put it in a dedicated factory instead of repeating the same code everywhere.
In plain words
It provides a way to delegate the instantiation logic to child classes.
Real world example
Consider the case of a hiring manager. It is impossible for one person to interview for each of the positions. Based on the job opening, she has to decide and delegate the interview steps to different people.
Wikipedia says
In class-based programming, the factory method pattern is a creational pattern that uses factory methods to deal with the problem of creating objects without having to specify the exact class of the object that will be created. This is done by creating objects by calling a factory method—either specified in an interface and implemented by child classes, or implemented in a base class and optionally overridden by derived classes—rather than by calling a constructor.
Programmatic Example
Taking our hiring manager example above. First of all we have an interviewer interface and some implementations for it
import abc
class Interviewer(abc.ABC):
@abc.abstractmethod
def ask_questions(self):
pass
class Developer(Interviewer):
def ask_questions(self):
print('Developer: asking about design patterns')
class CommunityExecutive(Interviewer):
def ask_questions(self):
print('Community Executive: asking about community building')Now let us create our HiringManager
class HiringManager(abc.ABC):
@abc.abstractmethod
def make_interviewer(self) -> Interviewer:
pass
def take_interview(self):
interviewer = self.make_interviewer()
interviewer.ask_questions()Now any child can extend it and provide the required interviewer
class DevelopmentManager(HiringManager):
def make_interviewer(self):
return Developer()
class MarketingManager(HiringManager):
def make_interviewer(self):
return CommunityExecutive()and then it can be used as
if __name__ == '__main__':
devManager = DevelopmentManager()
devManager.take_interview()
marketingManager = MarketingManager()
marketingManager.take_interview()Developer: asking about design patterns
Community Executive: asking about community buildingWhen to use?
Useful when there is some generic processing in a class but the required sub-class is dynamically decided at runtime. Or putting it in other words, when the client doesn't know what exact sub-class it might need.
In plain words
A factory of factories; a factory that groups the individual but related/dependent factories together without specifying their concrete classes.
Real world example
Extending our door example from Simple Factory. Based on your needs you might get a wooden door from a wooden door shop, iron door from an iron shop or a PVC door from the relevant shop. Plus you might need a guy with different kind of specialities to fit the door, for example a carpenter for wooden door, welder for iron door etc. As you can see there is a dependency between the doors now, wooden door needs carpenter, iron door needs a welder etc.
Wikipedia says
The abstract factory pattern provides a way to encapsulate a group of individual factories that have a common theme without specifying their concrete classes
Programmatic Example
Translating the door example above. First of all we have our Door interface and some implementation for it
import abc
class Door(abc.ABC):
@abc.abstractmethod
def get_description(self) -> str:
pass
class WoodenDoor(Door):
def get_description(self):
return 'I am a wooden door'
class IronDoor(Door):
def get_description(self):
return 'I am an iron door'Then we have some fitting experts for each door type
class DoorFittingExpert(abc.ABC):
@abc.abstractmethod
def get_description(self) -> str:
pass
class Welder(DoorFittingExpert):
def get_description(self):
return 'I can only fit iron doors'
class Carpenter(DoorFittingExpert):
def get_description(self):
return 'I can only fit wooden doors'Now we have our abstract factory that would let us make family of related objects i.e. wooden door factory would create a wooden door and wooden door fitting expert and iron door factory would create an iron door and iron door fitting expert
class DoorFactory(abc.ABC):
@abc.abstractmethod
def make_door(self) -> Door:
pass
@abc.abstractmethod
def make_fitting_expert(self) -> DoorFittingExpert:
pass
class WoodenDoorFactory(DoorFactory):
def make_door(self):
return WoodenDoor()
def make_fitting_expert(self):
return Carpenter()
class IronDoorFactory(DoorFactory):
def make_door(self):
return IronDoor()
def make_fitting_expert(self):
return Welder()And then it can be used as
if __name__ == '__main__':
# Wood
woodenFactory = WoodenDoorFactory()
wood_door = woodenFactory.make_door()
wood_fitting_expert = woodenFactory.make_fitting_expert()
print(wood_door.get_description())
print(wood_fitting_expert.get_description())
# Iron
ironFactory = IronDoorFactory()
iron_door = ironFactory.make_door()
iron_fitting_expert = ironFactory.make_fitting_expert()
print(iron_door.get_description())
print(iron_fitting_expert.get_description())I am a wooden door
I can only fit wooden doors
I am an iron door
I can only fit iron doorsAs you can see the wooden door factory has encapsulated the carpenter and the wooden door also iron door factory has encapsulated the iron door and welder. And thus it had helped us make sure that for each of the created door, we do not get a wrong fitting expert.
When to use?
When there are interrelated dependencies with not-that-simple creation logic involved
In plain words
Allows you to create different flavors of an object while avoiding constructor pollution. Useful when there could be several flavors of an object. Or when there are a lot of steps involved in creation of an object.
Real world example
Imagine you are at Hardee's and you order a specific deal, lets say, "Big Hardee" and they hand it over to you without any questions; this is the example of simple factory. But there are cases when the creation logic might involve more steps. For example you want a customized Subway deal, you have several options in how your burger is made e.g what bread do you want? what types of sauces would you like? What cheese would you want? etc. In such cases builder pattern comes to the rescue.
Wikipedia says
The builder pattern is an object creation software design pattern with the intentions of finding a solution to the telescoping constructor anti-pattern.
Having said that let me add a bit about what telescoping constructor anti-pattern is. At one point or the other we have all seen a constructor like below:
def __init__(self, size, cheese=True, pepperoni=True, tomato=False, lettuce=True):
passAs you can see; the number of constructor parameters can quickly get out of hand and it might become difficult to understand the arrangement of parameters. Plus this parameter list could keep on growing if you would want to add more options in future. This is called telescoping constructor anti-pattern.
Programmatic Example
The sane alternative is to use the builder pattern. First of all we have our burger that we want to make
class Burger:
_size = None
_meat = False
_cheese = False
_tomato = False
_lettuce = False
def __init__(self, builder: BurgerBuilder):
self._size = builder.size
self._meat = builder.meat
self._cheese = builder.cheese
self._tomato = builder.tomato
self._lettuce = builder.lettuce
def __str__(self):
order = (f'Order: Burger',
f'Size: {self._size}',
f'Meat: {self._meat}',
f'Cheese: {self._cheese}',
f'Tomato: {self._tomato}',
f'Lettuce: {self._lettuce}')
return '\n '.join(order) + '\n'And then we have the builder
class BurgerBuilder:
size = None
meat = False
cheese = False
tomato = False
lettuce = False
def __init__(self, size):
self.size = size
def add_meat(self):
self.meat = True
return self
def add_cheese(self):
self.cheese = True
return self
def add_tomato(self):
self.tomato = True
return self
def add_lettuce(self):
self.lettuce = True
return self
def build(self):
return Burger(self)And then it can be used as:
if __name__ == '__main__':
burger = BurgerBuilder(size=10).add_meat().add_lettuce().add_tomato().build()
print(burger)
burger2 = BurgerBuilder(size=15).add_meat().add_cheese().add_lettuce().build()
print(burger2)Order: Burger
Size: 10
Meat: True
Cheese: False
Tomato: True
Lettuce: True
Order: Burger
Size: 15
Meat: True
Cheese: True
Tomato: False
Lettuce: TrueWhen to use?
When there could be several flavors of an object and to avoid the constructor telescoping. The key difference from the factory pattern is that; factory pattern is to be used when the creation is a one step process while builder pattern is to be used when the creation is a multi step process.
In plain words
Create object based on an existing object through cloning.
Real world example
Remember dolly? The sheep that was cloned! Lets not get into the details but the key point here is that it is all about cloning
Wikipedia says
The prototype pattern is a creational design pattern in software development. It is used when the type of objects to create is determined by a prototypical instance, which is cloned to produce new objects.
In short, it allows you to create a copy of an existing object and modify it to your needs, instead of going through the trouble of creating an object from scratch and setting it up.
Programmatic Example
class Sheep:
def __init__(self, name: str, category: str = 'Mountain Sheep'):
self._name = name
self._category = category
@property
def name(self):
return self._name
@name.setter
def name(self, name):
self._name = name
@property
def category(self):
return self._category
@category.setter
def category(self, category):
self._category = categoryThen it can be cloned like below
import copy
if __name__ == '__main__':
original = Sheep('Jolly')
print(original.name)
print(original.category)
cloned = copy.copy(original)
cloned.name = 'Dolly'
print(cloned.name)
print(cloned.category)
print(original.name)Jolly
Mountain Sheep
Dolly
Mountain Sheep
JollyNOTE: copy.copy() returns a shallow copy of the original object, and changes to data in a shallow copy may cause changes in the original. You can use the magic method __copy__ to modify the cloning behavior of the prototype object.
When to use?
When an object is required that is similar to existing object or when the creation would be expensive as compared to cloning.
In plain words
Ensures that only one object of a particular class is ever created.
Real world example
There can only be one president of a country at a time. The same president has to be brought to action, whenever duty calls. President here is singleton.
Wikipedia says
In software engineering, the singleton pattern is a software design pattern that restricts the instantiation of a class to one object. This is useful when exactly one object is needed to coordinate actions across the system.
Singleton pattern is actually considered an anti-pattern and overuse of it should be avoided. It is not necessarily bad and could have some valid use-cases but should be used with caution because it introduces a global state in your application and change to it in one place could affect in the other areas and it could become pretty difficult to debug. The other bad thing about them is it makes your code tightly coupled plus mocking the singleton could be difficult.
Programmatic Example
To create a singleton, we will define a metaclass to house all the Singleton instances and override their __call__ methods to always return only one instance of the Singleton classes.
class Singleton(type):
__instances = {}
def __call__(cls, *args, **kwargs):
if cls not in cls.__instances:
cls.__instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
else:
cls.__instances[cls].__init__(*args, **kwargs)
return cls.__instances[cls]Now in our President class, we can implement it as a Singleton by using metaclass=Singleton. This allows for an easy reusable metaclass for making Singleton objects.
class President(metaclass=Singleton):
def __init__(self, name: str = None):
if name is not None:
self._name = name
@property
def name(self):
return self._name
@name.setter
def name(self, name: str):
self._name = nameThen in order to use
if __name__ == '__main__':
president1 = President(name='George Washington')
print('President 1:', president1.name)
president2 = President(name='John Adams')
print('President 2:', president2.name)
# Even without passing in a name parameter
# We still have access to the singleton's attributes
president3 = President()
print('President 3:', president3.name)
# Compare all the president instances against one another
print('President 1 is President 2:', president1 is president2)
print('President 1 is President 3:', president1 is president3)
print('President 2 is President 3:', president2 is president3)
# Attempt to set only the first president's name
president1.name = 'George Washington'
print('President 1:', president1.name)
print('President 2:', president2.name)
# Attempt to set only the second president's name
president2.name = 'John Adams'
print('President 1:', president1.name)
print('President 2:', president2.name)President 1: George Washington
President 2: John Adams
President 3: John Adams
President 1 is President 2: True
President 1 is President 3: True
President 2 is President 3: True
President 1: George Washington
President 2: George Washington
President 1: John Adams
President 2: John AdamsIn plain words
Structural patterns are mostly concerned with object composition or in other words how the entities can use each other. Or yet another explanation would be, they help in answering "How to build a software component?"
Wikipedia says
In software engineering, structural design patterns are design patterns that ease the design by identifying a simple way to realize relationships between entities.
In plain words
Adapter pattern lets you wrap an otherwise incompatible object in an adapter to make it compatible with another class.
Real world example
Consider that you have some pictures in your memory card and you need to transfer them to your computer. In order to transfer them you need some kind of adapter that is compatible with your computer ports so that you can attach memory card to your computer. In this case card reader is an adapter. Another example would be the famous power adapter; a three legged plug can't be connected to a two pronged outlet, it needs to use a power adapter that makes it compatible with the two pronged outlet. Yet another example would be a translator translating words spoken by one person to another
Wikipedia says
In software engineering, the adapter pattern is a software design pattern that allows the interface of an existing class to be used as another interface. It is often used to make existing classes work with others without modifying their source code.
Programmatic Example
Consider a game where there is a hunter and he hunts lions.
First we have an interface Lion that all types of lions have to implement
import abc
class Lion(abc.ABC):
@abc.abstractmethod
def roar(self):
pass
class AfricanLion(Lion):
def roar(self):
print('African Lion roars...')
class AsianLion(Lion):
def roar(self):
print('Asian Lion roars...')And hunter expects any implementation of Lion interface to hunt.
class Hunter:
@staticmethod
def attack(lion: Lion):
lion.roar()Now let's say we have to add a WildDog in our game so that hunter can hunt that also. But we can't do that directly because dog has a different interface. To make it compatible for our hunter, we will have to create an adapter that is compatible
class WildDog:
@staticmethod
def bark():
print('Wild Dog barks...')
class WildDogAdapter(Lion):
def __init__(self, dog: WildDog):
self._dog = dog
def roar(self):
self._dog.bark()And now the WildDog can be used in our game using WildDogAdapter
if __name__ == '__main__':
hunter = Hunter()
african_lion = AfricanLion()
hunter.attack(african_lion)
asian_lion = AsianLion()
hunter.attack(asian_lion)
wildDog = WildDog()
wildDogAdapter = WildDogAdapter(wildDog)
hunter.attack(wildDogAdapter)African Lion roars...
Asian Lion roars...
Wild Dog barks...In Plain Words
Bridge pattern is about preferring composition over inheritance. Implementation details are pushed from a hierarchy to another object with a separate hierarchy.
Real world example
Consider you have a website with different pages and you are supposed to allow the user to change the theme. What would you do? Create multiple copies of each of the pages for each of the themes or would you just create separate theme and load them based on the user's preferences? Bridge pattern allows you to do the second i.e.
Wikipedia says
The bridge pattern is a design pattern used in software engineering that is meant to "decouple an abstraction from its implementation so that the two can vary independently"
Programmatic Example
Translating our WebPage example from above. Here we have the Theme hierarchy
import abc
class Theme(abc.ABC):
@abc.abstractmethod
def get_color(self) -> str:
pass
class DarkTheme(Theme):
def get_color(self):
return 'Dark Black'
class LightTheme(Theme):
def get_color(self):
return 'Off White'
class AquaTheme(Theme):
def get_color(self):
return 'Light Blue'And the WebPage hierarchy
class WebPage:
def __init__(self, name: str, theme: Theme):
self._name = name
self._theme = theme
def get_content(self):
return f'{self._name} page in {self._theme.get_color()}'
class AboutPage(WebPage):
def __init__(self, theme: Theme):
super().__init__('About', theme)
class CareersPage(WebPage):
def __init__(self, theme: Theme):
super().__init__('Careers', theme)And their uses
if __name__ == '__main__':
# Create two lists of all theme and page classes
themes = [DarkTheme, LightTheme, AquaTheme]
pages = [AboutPage, CareersPage]
# Loop through all theme classes
for theme_class in themes:
# Create a new instance of the theme
theme = theme_class()
# Loop through all pages classes
for page_class in pages:
# Create a new instance of the page
page = page_class(theme)
# Output the page content to console
print(page.get_content())About page in Dark Black
Careers page in Dark Black
About page in Off White
Careers page in Off White
About page in Light Blue
Careers page in Light BlueIn plain words
Composite pattern lets clients treat the individual objects in a uniform manner.
Real world example
Every organization is composed of employees. Each of the employees has the same features i.e. has a salary, has some responsibilities, may or may not report to someone, may or may not have some subordinates etc.
Wikipedia says
In software engineering, the composite pattern is a partitioning design pattern. The composite pattern describes that a group of objects is to be treated in the same way as a single instance of an object. The intent of a composite is to "compose" objects into tree structures to represent part-whole hierarchies. Implementing the composite pattern lets clients treat individual objects and compositions uniformly.
Programmatic Example
Taking our employees example from above. Here we have different employee types
from abc import ABC, abstractmethod
class Employee(ABC):
def __init__(self, name: str, salary: int):
self._name = name
self._salary = salary
@property
@abstractmethod
def name(self) -> str:
pass
@property
@abstractmethod
def salary(self) -> int:
pass
@salary.setter
@abstractmethod
def salary(self, salary: int):
pass
class Developer(Employee):
@property
def name(self):
return self._name
@property
def salary(self):
return self._salary
@salary.setter
def salary(self, salary):
self._salary = salary
class Designer(Employee):
@property
def name(self):
return self._name
@property
def salary(self):
return self._salary
@salary.setter
def salary(self, salary):
self._salary = salaryThen we have an organization which consists of several different types of employees
class Organization:
def __init__(self):
self._employees = []
def add_employee(self, employee):
self._employees.append(employee)
def get_net_salaries(self):
net_salary = 0
for employee in self._employees:
net_salary += employee.salary
return net_salaryAnd then it can be used as
if __name__ == '__main__':
# Create two new employees
john = Developer('John Doe', 12000)
jane = Designer('Jane Doe', 15000)
# Create an org and add the new employees
organization = Organization()
organization.add_employee(john)
organization.add_employee(jane)
# Display net salaries for all employees
print('Net Salaries ' + str(organization.get_net_salaries()))
# Give a raise to john
john.salary = 15000
# Display updated net salaries for all employees
print('Net Salaries ' + str(organization.get_net_salaries()))Net Salaries 27000
Net Salaries 30000In plain words
Decorator pattern lets you dynamically change the behavior of an object at run time by wrapping them in an object of a decorator class.
Real world example
Imagine you run a car service shop offering multiple services. Now how do you calculate the bill to be charged? You pick one service and dynamically keep adding to it the prices for the provided services till you get the final cost. Here each type of service is a decorator.
Wikipedia says
In object-oriented programming, the decorator pattern is a design pattern that allows behavior to be added to an individual object, either statically or dynamically, without affecting the behavior of other objects from the same class. The decorator pattern is often useful for adhering to the Single Responsibility Principle, as it allows functionality to be divided between classes with unique areas of concern.
Programmatic Example
Lets take coffee for example. First of all we have a simple coffee implementing the coffee interface
from abc import ABC, abstractmethod
class Coffee(ABC):
@property
@abstractmethod
def cost(self) -> int:
pass
@property
@abstractmethod
def description(self) -> str:
pass
def __str__(self):
return f'{self.description}: ${self.cost}'
def __repr__(self):
return self.__str__()
class SimpleCoffee(Coffee):
@property
def cost(self):
return 2
@property
def description(self):
return 'Simple Coffee'We want to make the code extensible to allow options to modify it if required. Lets make some mixins (decorators)
class CoffeeMixin(Coffee):
def __init__(self, coffee: Coffee, cost: int, description: str):
self._coffee = coffee
self._cost = cost
self._description = description
@property
def cost(self):
return self._coffee.cost + self._cost
@property
def description(self):
return f'{self._coffee.description}, {self._description}'
class MilkMixin(CoffeeMixin):
def __init__(self, coffee):
super().__init__(coffee, cost=2, description='milk')
class WhipMixin(CoffeeMixin):
def __init__(self, coffee):
super().__init__(coffee, cost=5, description='whip')
class VanillaMixin(CoffeeMixin):
def __init__(self, coffee):
super().__init__(coffee, cost=3, description='vanilla')Lets make a coffee now
if __name__ == '__main__':
order = SimpleCoffee()
print(order)
order = MilkMixin(order)
print(order)
order = WhipMixin(order)
print(order)
order = VanillaMixin(order)
print(order)Simple Coffee: $2
Simple Coffee, milk: $4
Simple Coffee, milk, whip: $9
Simple Coffee, milk, whip, vanilla: $12In plain words
Facade pattern provides a simplified interface to a complex subsystem.
Real world example
How do you turn on the computer? "Hit the power button" you say! That is what you believe because you are using a simple interface that computer provides on the outside, internally it has to do a lot of stuff to make it happen. This simple interface to the complex subsystem is a facade.
Wikipedia says
A facade is an object that provides a simplified interface to a larger body of code, such as a class library.
Programmatic Example
Taking our computer example from above. Here we have the computer class
class Computer:
def getElectricShock(self):
print "Ouch!"
def makeSound(self):
print "Beep Beep!"
def showLoadingScreen(self):
print "Loading..."
def bam(self):
print "Ready to be used..."
def closeEverything(self):
print "Bup bup bup buzzz!"
def sooth(self):
print "Zzzzz"
def pullCurrent(self):
print "Haaah!"Here we have the facade
class ComputerFacade:
_computer = None
def __init__(self, computer):
self.computer = computer
def turnOn(self):
self.computer.getElectricShock()
self.computer.makeSound()
self.computer.showLoadingScreen()
self.computer.bam()
def turnOff(self):
self.computer.closeEverything()
self.computer.pullCurrent()
self.computer.sooth()Now to use the facade
computer = ComputerFacade(Computer())
computer.turnOn()
computer.turnOff()In plain words
It is used to minimize memory usage or computational expenses by sharing as much as possible with similar objects.
Real world example
Did you ever have fresh tea from some stall? They often make more than one cup that you demanded and save the rest for any other customer so to save the resources e.g. gas etc. Flyweight pattern is all about that i.e. sharing.
Wikipedia says
In computer programming, flyweight is a software design pattern. A flyweight is an object that minimizes memory use by sharing as much data as possible with other similar objects; it is a way to use objects in large numbers when a simple repeated representation would use an unacceptable amount of memory.
Programmatic example
Translating our tea example from above. First of all we have tea types and tea maker
class KarakTea:
pass
class TeaMaker:
_availableTea = {}
def make(self, preference):
if not preference in self._availableTea:
self._availableTea[preference] = KarakTea()
return self._availableTea[preference]Then we have the TeaShop which takes orders and serves them
class TeaShop:
_orders = {}
_teaMaker = None
def __init__(self, teaMaker):
self._teaMaker = teaMaker
def takeOrder(self, teaType, table):
self._orders[table] = self._teaMaker.make(teaType)
def serve(self):
for table, tea in self._orders.iteritems():
print "Serving tea to table #" + str(table)And it can be used as below
teaMaker = TeaMaker()
shop = TeaShop(teaMaker)
shop.takeOrder('less sugar', 1)
shop.takeOrder('more milk', 2)
shop.takeOrder('without sugar', 5)
shop.serve()
# Serving tea to table# 1
# Serving tea to table# 2
# Serving tea to table# 5In plain words
Using the proxy pattern, a class represents the functionality of another class.
Real world example
Have you ever used an access card to go through a door? There are multiple options to open that door i.e. it can be opened either using access card or by pressing a button that bypasses the security. The door's main functionality is to open but there is a proxy added on top of it to add some functionality. Let me better explain it using the code example below.
Wikipedia says
A proxy, in its most general form, is a class functioning as an interface to something else. A proxy is a wrapper or agent object that is being called by the client to access the real serving object behind the scenes. Use of the proxy can simply be forwarding to the real object, or can provide additional logic. In the proxy extra functionality can be provided, for example caching when operations on the real object are resource intensive, or checking preconditions before operations on the real object are invoked.
Programmatic Example
Taking our security door example from above. Firstly we have the door interface and an implementation of door
class Door:
def open(self):
pass
def close(self):
pass
class LabDoor(Door):
def open(self):
print "Opening lab door"
def close(self):
print "Closing the lab door"Then we have a proxy to secure any doors that we want
class SecuredDoor():
_door = None
def __init__(self, door):
self.door = door
def open(self, password):
if self.authenticate(password):
self.door.open()
else:
print "Big no! It ain't possible."
def authenticate(self, password):
return password == '$ecr@t'
def close(self):
self.door.close()And here is how it can be used
door = SecuredDoor(LabDoor())
door.open('invalid') # Big no! It ain't possible
door.open('$ecr@t') # Opening lab door
door.close() # Closing Lab DoorYet another example would be some sort of data-mapper implementation. For example, I recently made an ODM (Object Data Mapper) for MongoDB using this pattern where I wrote a proxy around mongo classes while utilizing the magic method __call(). All the method calls were proxied to the original mongo class and result retrieved was returned as it is but in case of find or findOne data was mapped to the required class objects and the object was returned instead of Cursor.
In plain words
It is concerned with assignment of responsibilities between the objects. What makes them different from structural patterns is they don't just specify the structure but also outline the patterns for message passing/communication between them. Or in other words, they assist in answering "How to run a behavior in software component?"
Wikipedia says
In software engineering, behavioral design patterns are design patterns that identify common communication patterns between objects and realize these patterns. By doing so, these patterns increase flexibility in carrying out this communication.
- Chain of Responsibility
- Command
- Iterator
- Mediator
- Memento
- Observer
- Visitor
- Strategy
- State
- Template Method
In plain words
It helps building a chain of objects. Request enters from one end and keeps going from object to object till it finds the suitable handler.
Real world example
For example, you have three payment methods (
A,BandC) setup in your account; each having a different amount in it.Ahas 100 USD,Bhas 300 USD andChaving 1000 USD and the preference for payments is chosen asAthenBthenC. You try to purchase something that is worth 210 USD. Using Chain of Responsibility, first of all accountAwill be checked if it can make the purchase, if yes purchase will be made and the chain will be broken. If not, request will move forward to accountBchecking for amount if yes chain will be broken otherwise the request will keep forwarding till it finds the suitable handler. HereA,BandCare links of the chain and the whole phenomenon is Chain of Responsibility.
Wikipedia says
In object-oriented design, the chain-of-responsibility pattern is a design pattern consisting of a source of command objects and a series of processing objects. Each processing object contains logic that defines the types of command objects that it can handle; the rest are passed to the next processing object in the chain.
Programmatic Example
Translating our account example above. First of all we have a base account having the logic for chaining the accounts together and some accounts
import inspect
class Account:
_successor = None
_balance = None
def setNext(self, account):
self._successor = account
def pay(self, amountToPay):
import inspect
myCaller = inspect.stack()[1][3]
if self.canPay(amountToPay):
print "Paid " + str(amountToPay) + " using " + myCaller
elif (self._successor):
print "Cannot pay using " + myCaller + ". Proceeding .."
self._successor.pay(amountToPay)
else:
raise ValueError('None of the accounts have enough balance')
def canPay(self, amount):
return self.balance >= amount
class Bank(Account):
_balance = None
def __init__(self, balance):
self.balance = balance
class Paypal(Account):
_balance = None
def __init__(self, balance):
self.balance = balance
class Bitcoin(Account):
_balance = None
def __init__(self, balance):
self.balance = balanceNow let's prepare the chain using the links defined above (i.e. Bank, Paypal, Bitcoin)
# Let's prepare a chain like below
# $bank->$paypal->$bitcoin
#
# First priority bank
# If bank can't pay then paypal
# If paypal can't pay then bit coin
bank = Bank(100) # Bank with balance 100
paypal = Paypal(200) # Paypal with balance 200
bitcoin = Bitcoin(300) # Bitcoin with balance 300
bank.setNext(paypal)
paypal.setNext(bitcoin)
bank.pay(259)
'''
Output will be
==============
Cannot pay using bank. Proceeding ..
Cannot pay using paypal. Proceeding ..:
Paid 259 using Bitcoin!
'''Real world example
A generic example would be you ordering food at a restaurant. You (i.e.
Client) ask the waiter (i.e.Invoker) to bring some food (i.e.Command) and waiter simply forwards the request to Chef (i.e.Receiver) who has the knowledge of what and how to cook. Another example would be you (i.e.Client) switching on (i.e.Command) the television (i.e.Receiver) using a remote control (Invoker).
In plain words
Allows you to encapsulate actions in objects. The key idea behind this pattern is to provide the means to decouple client from receiver.
Wikipedia says
In object-oriented programming, the command pattern is a behavioral design pattern in which an object is used to encapsulate all information needed to perform an action or trigger an event at a later time. This information includes the method name, the object that owns the method and values for the method parameters.
Programmatic Example
First of all we have the receiver that has the implementation of every action that could be performed
# Receiver
class Bulb:
def turnOn(self):
print "Bulb has been lit"
def turnOff(self):
print "Darkness!"then we have an interface that each of the commands are going to implement and then we have a set of commands
class Command:
def execute(self):
pass
def undo(self):
pass
def redo(self):
pass
class TurnOn(Command):
_bulb = None
def __init__(self, bulb):
self._bulb = bulb
def execute(self):
self._bulb.turnOn()
def undo(self):
self._bulb.turnOff()
def redo(self):
self.execute()
class TurnOff(Command):
_bulb = None
def __init__(self, bulb):
self.bulb = bulb
def execute(self):
self.bulb.turnOff()
def undo(self):
self.bulb.turnOn()
def redo(self):
self.execute()Then we have an Invoker with whom the client will interact to process any commands
# Invoker
class RemoteControl:
def submit(self, command):
command.execute()Finally let's see how we can use it in our client
bulb = Bulb()
turnOn = TurnOn(bulb)
turnOff = TurnOff(bulb)
remote = RemoteControl()
remote.submit(turnOn) # Bulb has been lit!
remote.submit(turnOff) # Darkness!Command pattern can also be used to implement a transaction based system. Where you keep maintaining the history of commands as soon as you execute them. If the final command is successfully executed, all good otherwise just iterate through the history and keep executing the undo on all the executed commands.
In plain words
It presents a way to access the elements of an object without exposing the underlying presentation.
Real world example
An old radio set will be a good example of iterator, where user could start at some channel and then use next or previous buttons to go through the respective channels. Or take an example of MP3 player or a TV set where you could press the next and previous buttons to go through the consecutive channels or in other words they all provide an interface to iterate through the respective channels, songs or radio stations.
Wikipedia says
In object-oriented programming, the iterator pattern is a design pattern in which an iterator is used to traverse a container and access the container's elements. The iterator pattern decouples algorithms from containers; in some cases, algorithms are necessarily container-specific and thus cannot be decoupled.
Programmatic example
class RadioStation:
_frequency = None
def __init__(self, frequency):
self._frequency = frequency
def getFrequency(self):
return self._frequencyThen we have our iterator
class StationList:
_stations = []
_counter = 0
def addStation(self, station):
self._stations.append(station)
def removeStation(self, toRemove):
toRemoveFrequency = toRemove.getFrequency()
self._stations = filter(lambda station: station.getFrequency() != toRemoveFrequency, self._stations)
def __iter__(self):
return iter(self._stations)
def next(self):
self._counter += 1And then it can be used as
stationList = StationList()
stationList.addStation(RadioStation(89))
stationList.addStation(RadioStation(101))
stationList.addStation(RadioStation(102))
stationList.addStation(RadioStation(103.2))
for station in stationList:
print station.getFrequency()
stationList.removeStation(RadioStation(89))
for station in stationList:
print station.getFrequency()In plain words
Mediator pattern adds a third party object (called mediator) to control the interaction between two objects (called colleagues). It helps reduce the coupling between the classes communicating with each other. Because now they don't need to have the knowledge of each other's implementation.
Real world example
A general example would be when you talk to someone on your mobile phone, there is a network provider sitting between you and them and your conversation goes through it instead of being directly sent. In this case network provider is mediator.
Wikipedia says
In software engineering, the mediator pattern defines an object that encapsulates how a set of objects interact. This pattern is considered to be a behavioral pattern due to the way it can alter the program's running behavior.
Programmatic Example
Here is the simplest example of a chat room (i.e. mediator) with users (i.e. colleagues) sending messages to each other.
First of all, we have the mediator i.e. the chat room
class ChatRoomMediator:
def showMessage(self, user, message):
pass
class ChatRoom(ChatRoomMediator):
def showMessage(self, user, message):
import datetime
time = datetime.datetime.now()
sender = user.getName()
print str(time) + '[' + sender + ']: ' + messageThen we have our users i.e. colleagues
class User:
_name = None
_chatMediator = None
def __init__(self, name, chatMediator):
self.name = name
self._chatMediator = chatMediator
def getName(self):
return self.name
def send(self, message):
self._chatMediator.showMessage(self, message)And the usage
mediator = ChatRoom()
john = User('John Doe', mediator)
jane = User('Jane Doe', mediator)
john.send('Hi There!')
jane.send('Hey!')
# Output will be
# Feb 14, 10:58 [John]: Hi there!
# Feb 14, 10:58 [Jane]: Hey!In plain words
Memento pattern is about capturing and storing the current state of an object in a manner that it can be restored later on in a smooth manner.
Real world example
Take the example of calculator (i.e. originator), where whenever you perform some calculation the last calculation is saved in memory (i.e. memento) so that you can get back to it and maybe get it restored using some action buttons (i.e. caretaker).
Wikipedia says
The memento pattern is a software design pattern that provides the ability to restore an object to its previous state (undo via rollback).
Usually useful when you need to provide some sort of undo functionality.
Programmatic Example
Lets take an example of text editor which keeps saving the state from time to time and that you can restore if you want.
First of all we have our memento object that will be able to hold the editor state
class EditorMemento:
_content = None
def __init__(self, content):
self.content = content
def get_content(self):
return self.contentThen we have our editor i.e. originator that is going to use memento object
class Editor:
_content = ''
def type(self, words):
self._content = self._content + ' ' + words
def get_content(self):
return self._content
def save(self):
return EditorMemento(self._content)
def restore(self, memento):
self.content = memento.get_content()And then it can be used as
editor = Editor()
editor.type('This is the first sentence')
editor.type('This is the second.')
#Save the state to restore to : This is the first sentence. This is second.
saved = editor.save()
editor.type('And this is the third')
print editor.get_content() # This is the first sentence. This is second. And this is third.
editor.restore(saved)
editor.get_content() # This is the first sentence. This is second.In plain words
Defines a dependency between objects so that whenever an object changes its state, all its dependents are notified.
Real world example
A good example would be the job seekers where they subscribe to some job posting site and they are notified whenever there is a matching job opportunity.
Wikipedia says
The observer pattern is a software design pattern in which an object, called the subject, maintains a list of its dependents, called observers, and notifies them automatically of any state changes, usually by calling one of their methods.
Programmatic example
Translating our example from above. First of all we have job seekers that need to be notified for a job posting
class JobPost:
_title = None
def __init__(self, title):
self.title = title
def getTitle(self):
return self.title
class JobSeeker:
_name = None
def __init__(self, name):
self.name = name
def onJobPosted(self, job):
print 'Hi ' + self.name + '! New job posted: ' + job.getTitle()Then we have our job postings to which the job seekers will subscribe
class EmploymentAgency:
_observers = []
def notify(self, jobPosting):
for observer in self._observers:
observer.onJobPosted(jobPosting)
def attach(self, observer):
self._observers.append(observer)
def addJob(self, jobPosting):
self.notify(jobPosting)Then it can be used as
johnDoe = JobSeeker('John Doe')
janeDoe = JobSeeker('Jane Doe')
jobPostings = EmploymentAgency()
jobPostings.attach(janeDoe)
jobPostings.attach(johnDoe)
jobPostings.addJob(JobPost('Software Engineer'))
'''
Output
Hi John Doe! New job posted: Software Engineer
Hi Jane Doe! New job posted: Software Engineer
'''In plain words
Visitor pattern lets you add further operations to objects without having to modify them.
Real world example
Consider someone visiting Dubai. They just need a way (i.e. visa) to enter Dubai. After arrival, they can come and visit any place in Dubai on their own without having to ask for permission or to do some leg work in order to visit any place here; just let them know of a place and they can visit it. Visitor pattern lets you do just that, it helps you add places to visit so that they can visit as much as they can without having to do any legwork.
Wikipedia says
In object-oriented programming and software engineering, the visitor design pattern is a way of separating an algorithm from an object structure on which it operates. A practical result of this separation is the ability to add new operations to existing object structures without modifying those structures. It is one way to follow the open/closed principle.
Programmatic example
Let's take an example of a zoo simulation where we have several different kinds of animals and we have to make them Sound. Let's translate this using visitor pattern
# Visitee
class Animal:
def accept(self, operation):
pass
# Visitor
class AnimalOperation:
def visitMonkey(self, monkey):
pass
def visitLion(self, lion):
pass
def visitDolphin(self, dolphin):
passThen we have our implementations for the animals
class Monkey(Animal):
def shout(self):
print 'Ooh oo aa aa!'
def accept(self, operation):
operation.visitMonkey(self)
class Lion(Animal):
def roar(self):
print 'Roaaar!'
def accept(self, operation):
operation.visitLion(self)
class Dolphin(Animal):
def speak(self):
print 'Tuut tuttu tuutt!'
def accept(self, operation):
operation.visitDolphin(self)Let's implement our visitor
class Speak(AnimalOperation):
def visitMonkey(self, monkey):
monkey.shout()
def visitLion(self, lion):
lion.roar()
def visitDolphin(self, dolphin):
dolphin.speak()And then it can be used as
monkey = Monkey()
lion = Lion()
dolphin = Dolphin()
speak = Speak()
monkey.accept(speak) # Ooh oo aa aa!
lion.accept(speak) # Roaaar!
dolphin.accept(speak) #Tuut tutt tuttt!We could have done this simply by having an inheritance hierarchy for the animals but then we would have to modify the animals whenever we would have to add new actions to animals. But now we will not have to change them. For example, let's say we are asked to add the jump behavior to the animals, we can simply add that by creating a new visitor i.e.
class Jump(AnimalOperation):
def visitMonkey(self, monkey):
print 'Jumped 20 feet high! on to the tree!'
def visitLion(self, lion):
print 'Jumped 7 feet! back on the ground!'
def visitDolphin(self, dolphin):
print 'Walked on water a little and disappeared'And for the usage
jump = Jump()
monkey.accept(speak) # Ooh oo aa aa!
monkey.accept(jump) # Jumped 20 feet high! on to the tree!
lion.accept(speak) # Roaaar!
lion.accept(jump) # Jumped 7 feet! Back on the ground!
dolphin.accept(speak) # Tuut tutt tuutt!
dolphin.accept(jump) # Walked on water a little and disappearedIn plain words
Strategy pattern allows you to switch the algorithm or strategy based upon the situation.
Real world example
Consider the example of sorting, we implemented bubble sort but the data started to grow and bubble sort started getting very slow. In order to tackle this we implemented Quick sort. But now although the quick sort algorithm was doing better for large datasets, it was very slow for smaller datasets. In order to handle this we implemented a strategy where for small datasets, bubble sort will be used and for larger, quick sort.
Wikipedia says
In computer programming, the strategy pattern (also known as the policy pattern) is a behavioural software design pattern that enables an algorithm's behavior to be selected at runtime.
Programmatic example
Translating our example from above. First of all we have our strategy interface and different strategy implementations
class SortStrategy:
def sort(self, dataset):
pass
class BubbleSortStrategy(SortStrategy):
def sort(self, dataset):
print 'Sorting using bubble sort'
return dataset
class QuickSortStrategy(SortStrategy):
def sort(self, dataset):
print 'Sorting using quick sort'
return datasetAnd then we have our client that is going to use any strategy
class Sorter:
_sorter = None
def __init__(self, sorter):
self._sorter = sorter
def sort(self, dataset):
return self._sorter.sort(dataset)And it can be used as
dataset = [1, 5, 4, 3, 2, 8]
sorter = Sorter(BubbleSortStrategy())
sorter.sort(dataset)
sorter = Sorter(QuickSortStrategy())
sorter.sort(dataset)In plain words
It lets you change the behavior of a class when the state changes.
Real world example
Imagine you are using some drawing application, you choose the paint brush to draw. Now the brush changes its behavior based on the selected color i.e. if you have chosen red color it will draw in red, if blue then it will be in blue etc.
Wikipedia says
The state pattern is a behavioral software design pattern that implements a state machine in an object-oriented way. With the state pattern, a state machine is implemented by implementing each individual state as a derived class of the state pattern interface, and implementing state transitions by invoking methods defined by the pattern's superclass. The state pattern can be interpreted as a strategy pattern which is able to switch the current strategy through invocations of methods defined in the pattern's interface.
Programmatic example
Let's take an example of text editor, it lets you change the state of text that is typed i.e. if you have selected bold, it starts writing in bold, if italic then in italics etc.
First of all we have our state interface and some state implementations
class WritingState:
def write(self, words):
pass
class UpperCase(WritingState):
def write(self, words):
print words.upper()
class LowerCase(WritingState):
def write(self, words):
print words.lower()
class DefaultText(WritingState):
def write(self, words):
print wordsThen we have our editor
class TextEditor():
_state = None
def __init__(self, state):
self._state = state
def setState(self, state):
self._state = state
def type(self, words):
self._state.write(words)And then it can be used as
editor = TextEditor(DefaultText())
editor.type('First Line')
editor.setState(UpperCase())
editor.type('Second Line')
editor.type('Third Line')
editor.setState(LowerCase())
editor.type('Fourth Line')
editor.type('Fifth Line')
# Output:
# First line
# SECOND LINE
# THIRD LINE
# fourth line
# fifth lineIn plain words
Template method defines the skeleton of how a certain algorithm could be performed, but defers the implementation of those steps to the children classes.
Real world example
Suppose we are getting some house built. The steps for building might look like
- Prepare the base of house
- Build the walls
- Add roof
- Add other floors
The order of these steps could never be changed i.e. you can't build the roof before building the walls etc but each of the steps could be modified for example walls can be made of wood or polyester or stone.
Wikipedia says
In software engineering, the template method pattern is a behavioral design pattern that defines the program skeleton of an algorithm in an operation, deferring some steps to subclasses. It lets one redefine certain steps of an algorithm without changing the algorithm's structure.
Programmatic Example
Imagine we have a build tool that helps us test, lint, build, generate build reports (i.e. code coverage reports, linting report etc) and deploy our app on the test server.
First of all we have our base class that specifies the skeleton for the build algorithm
class Builder:
def build(self):
self.test()
self.lint()
self.assemble()
self.deploy()
def test(self):
pass
def lint(self):
pass
def assemble(self):
pass
def deploy(self):
passThen we can have our implementations
class AndroidBuilder(Builder):
def test(self):
print 'Running android tests'
def lint(self):
print 'Linting the android code'
def assemble(self):
print 'Assembling the android build'
def deploy(self):
print 'Deploying android build to server'
class IosBuilder(Builder):
def test(self):
print 'Running ios tests'
def lint(self):
print 'Linting the ios code'
def assemble(self):
print 'Assembling the ios build'
def deploy(self):
print 'Deploying ios build to server'And then it can be used as
androidBuilder = AndroidBuilder()
androidBuilder.build()
'''
Output:
Running android tests
Linting the android code
Assembling the android build
Deploying android build to server
'''
iosBuilder = IosBuilder()
iosBuilder.build()
'''
Output:
Running ios tests
Linting the ios code
Assembling the ios build
Deploying ios build to server
'''And that about wraps it up. I will continue to improve this, so you might want to watch/star this repository to revisit. Also, I have plans on writing the same about the architectural patterns, stay tuned for it.
- Report issues
- Open pull request with improvements
- Spread the word
- Reach out with any feedback
Creative Commons v4.0
