国产av日韩一区二区三区精品,成人性爱视频在线观看,国产,欧美,日韩,一区,www.成色av久久成人,2222eeee成人天堂

Home Backend Development Python Tutorial Understanding the Factory and Factory Method Design Patterns

Understanding the Factory and Factory Method Design Patterns

Nov 05, 2024 pm 03:01 PM

Understanding the Factory and Factory Method Design Patterns

What is a Factory class? A factory class is a class that creates one or more objects of different classes.

The Factory pattern is arguably the most used design pattern in Software engineering. In this article, I will be providing an in-depth explanation of the Simple Factory and the Factory Method design patterns using a simple example problem.

The Simple Factory Pattern

Let's say we're to create a system that supports two types of animals say Dog & cat, each of the animal classes should have a method that makes the type of sound of the animal. Now a client will like to use the system to make animal sounds based on the client's user input. A basic solution to the above problem can be written as follows:

from abc import ABC, abstractmethod

class Animal(ABC):
    @abstractmethod
    def make_sound(self):
        pass

class Dog(Animal):
    def make_sound(self):
        print("Bhow Bhow!")

class Cat(Animal):
    def make_sound(self):
        print("Meow Meow!")

With this solution, our client will utilize the system like this

## client code
if __name__ == '__main__':
    animal_type = input("Which animal should make sound Dog or Cat?")
    if animal_type.lower() == 'dog':
        Dog().make_sound()
    elif animal_type.lower() == 'cat':
        Cat().make_sound()

Our solution will work fine, but Simple Factory Pattern says we can do better. Why? As you've seen in the client code above, the client will have to decide which of our animal classes to call at a time. Imagine the system having, say, ten different animal classes. You can already see how problematic it will be for our client to use the system.

So here Simple Factory pattern is simply saying instead of letting the client decide on which class to call, let's make the system decide for the client.

To solve the problem using the Simple Factory pattern, all we need to do is create a factory class with a method that takes care of the animal object creation.

...
...
class AnimalFactory:
    def make_sound(self, animal_type):
        return eval(animal_type.title())().make_sound()

With this approach, the client code becomes:

## client code
if __name__ == '__main__':
    animal_type = input("Which animal should make sound Dog or Cat?")
    AnimalFactory().make_sound(animal_type)

In summary, the Simple Factory pattern is all about creating a factory class that handles object(s) creation on behalf of a client.

Factory Method Pattern

Going back to our problem statement of having a system that supports only two types of animal (Dog & Cat), what if this limitation is removed and our system is willing to support any type of animal? Of course, our system could not afford to provide implementations for millions of animals. This is where the Factory Method Pattern comes to the rescue.

In Factory Method pattern, we define an abstract class or interface to create objects, but instead of the factory being responsible for the object creation, the responsibility is deferred to the subclass that decides the class to be instantiated.

Key Components of the Factory Method Pattern

  1. Creator: The Creator is an abstract class or interface. It declares the Factory Method, which is a method for creating objects. The Creator provides an interface for creating products but doesn’t specify their concrete classes.

  2. Concrete Creator: Concrete Creators are the subclasses of the Creator. They implement the Factory Method, deciding which concrete product class to instantiate. In other words, each Concrete Creator specializes in creating a particular type of product.

  3. Product: The product is another abstract class or interface. It defines the type of objects the Factory Method creates. These products share a common interface, but their concrete implementations can vary.

  4. Concrete Product: Concrete products are the subclasses of the Product. They provide the specific implementations of the products. Each concrete product corresponds to one type of object created by the Factory Method.

Below is how our system code will look like using the Factory Method pattern:

Step 1: Defining the Product

from abc import ABC, abstractmethod

class Animal(ABC):
    @abstractmethod
    def make_sound(self):
        pass

class Dog(Animal):
    def make_sound(self):
        print("Bhow Bhow!")

class Cat(Animal):
    def make_sound(self):
        print("Meow Meow!")

Step 2: Creating Concrete Products

## client code
if __name__ == '__main__':
    animal_type = input("Which animal should make sound Dog or Cat?")
    if animal_type.lower() == 'dog':
        Dog().make_sound()
    elif animal_type.lower() == 'cat':
        Cat().make_sound()

Step 3: Defining the Creator

...
...
class AnimalFactory:
    def make_sound(self, animal_type):
        return eval(animal_type.title())().make_sound()

Step 4: Implementing Concrete Creators

## client code
if __name__ == '__main__':
    animal_type = input("Which animal should make sound Dog or Cat?")
    AnimalFactory().make_sound(animal_type)

And the client can utilize the solution as follows:

from abc import ABC, abstractmethod

class Animal(ABC):
    @abstractmethod
    def make_sound(self):
        pass

The Factory Method Pattern solution, allows clients to be able to extend the system and provide custom animal implementations if needed.

Advantages of the Factory Method Pattern

  1. Decoupling: It decouples client code from the concrete classes, reducing dependencies and enhancing code stability.

  2. Flexibility: It brings in a lot of flexibility and makes the code generic, not being tied to a certain class for instantiation. This way, we’re dependent on the interface (Product) and not on the ConcreteProduct class.

  3. Extensibility: New product classes can be added without modifying existing code, promoting an open-closed principle.

Conclusion

The Factory Method design pattern offers a systematic way to create objects while keeping code maintainable and adaptable. It excels in scenarios where object types vary or evolve.

Frameworks, libraries, plug-in systems, and software ecosystems benefit from its power. It allows systems to adapt to evolving demands.

However, it should be used judiciously, considering the specific needs of the application and the principle of simplicity. When applied appropriately, the Factory Method pattern can contribute significantly to the overall design and architecture of a software system.

Happy coding!!!

The above is the detailed content of Understanding the Factory and Factory Method Design Patterns. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undress AI Tool

Undress AI Tool

Undress images for free

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

How does Python's unittest or pytest framework facilitate automated testing? How does Python's unittest or pytest framework facilitate automated testing? Jun 19, 2025 am 01:10 AM

Python's unittest and pytest are two widely used testing frameworks that simplify the writing, organizing and running of automated tests. 1. Both support automatic discovery of test cases and provide a clear test structure: unittest defines tests by inheriting the TestCase class and starting with test\_; pytest is more concise, just need a function starting with test\_. 2. They all have built-in assertion support: unittest provides assertEqual, assertTrue and other methods, while pytest uses an enhanced assert statement to automatically display the failure details. 3. All have mechanisms for handling test preparation and cleaning: un

How does Python handle mutable default arguments in functions, and why can this be problematic? How does Python handle mutable default arguments in functions, and why can this be problematic? Jun 14, 2025 am 12:27 AM

Python's default parameters are only initialized once when defined. If mutable objects (such as lists or dictionaries) are used as default parameters, unexpected behavior may be caused. For example, when using an empty list as the default parameter, multiple calls to the function will reuse the same list instead of generating a new list each time. Problems caused by this behavior include: 1. Unexpected sharing of data between function calls; 2. The results of subsequent calls are affected by previous calls, increasing the difficulty of debugging; 3. It causes logical errors and is difficult to detect; 4. It is easy to confuse both novice and experienced developers. To avoid problems, the best practice is to set the default value to None and create a new object inside the function, such as using my_list=None instead of my_list=[] and initially in the function

How can Python be integrated with other languages or systems in a microservices architecture? How can Python be integrated with other languages or systems in a microservices architecture? Jun 14, 2025 am 12:25 AM

Python works well with other languages ??and systems in microservice architecture, the key is how each service runs independently and communicates effectively. 1. Using standard APIs and communication protocols (such as HTTP, REST, gRPC), Python builds APIs through frameworks such as Flask and FastAPI, and uses requests or httpx to call other language services; 2. Using message brokers (such as Kafka, RabbitMQ, Redis) to realize asynchronous communication, Python services can publish messages for other language consumers to process, improving system decoupling, scalability and fault tolerance; 3. Expand or embed other language runtimes (such as Jython) through C/C to achieve implementation

How do list, dictionary, and set comprehensions improve code readability and conciseness in Python? How do list, dictionary, and set comprehensions improve code readability and conciseness in Python? Jun 14, 2025 am 12:31 AM

Python's list, dictionary and collection derivation improves code readability and writing efficiency through concise syntax. They are suitable for simplifying iteration and conversion operations, such as replacing multi-line loops with single-line code to implement element transformation or filtering. 1. List comprehensions such as [x2forxinrange(10)] can directly generate square sequences; 2. Dictionary comprehensions such as {x:x2forxinrange(5)} clearly express key-value mapping; 3. Conditional filtering such as [xforxinnumbersifx%2==0] makes the filtering logic more intuitive; 4. Complex conditions can also be embedded, such as combining multi-condition filtering or ternary expressions; but excessive nesting or side-effect operations should be avoided to avoid reducing maintainability. The rational use of derivation can reduce

How can Python be used for data analysis and manipulation with libraries like NumPy and Pandas? How can Python be used for data analysis and manipulation with libraries like NumPy and Pandas? Jun 19, 2025 am 01:04 AM

PythonisidealfordataanalysisduetoNumPyandPandas.1)NumPyexcelsatnumericalcomputationswithfast,multi-dimensionalarraysandvectorizedoperationslikenp.sqrt().2)PandashandlesstructureddatawithSeriesandDataFrames,supportingtaskslikeloading,cleaning,filterin

How can you implement custom iterators in Python using __iter__ and __next__? How can you implement custom iterators in Python using __iter__ and __next__? Jun 19, 2025 am 01:12 AM

To implement a custom iterator, you need to define the __iter__ and __next__ methods in the class. ① The __iter__ method returns the iterator object itself, usually self, to be compatible with iterative environments such as for loops; ② The __next__ method controls the value of each iteration, returns the next element in the sequence, and when there are no more items, StopIteration exception should be thrown; ③ The status must be tracked correctly and the termination conditions must be set to avoid infinite loops; ④ Complex logic such as file line filtering, and pay attention to resource cleaning and memory management; ⑤ For simple logic, you can consider using the generator function yield instead, but you need to choose a suitable method based on the specific scenario.

What are dynamic programming techniques, and how do I use them in Python? What are dynamic programming techniques, and how do I use them in Python? Jun 20, 2025 am 12:57 AM

Dynamic programming (DP) optimizes the solution process by breaking down complex problems into simpler subproblems and storing their results to avoid repeated calculations. There are two main methods: 1. Top-down (memorization): recursively decompose the problem and use cache to store intermediate results; 2. Bottom-up (table): Iteratively build solutions from the basic situation. Suitable for scenarios where maximum/minimum values, optimal solutions or overlapping subproblems are required, such as Fibonacci sequences, backpacking problems, etc. In Python, it can be implemented through decorators or arrays, and attention should be paid to identifying recursive relationships, defining the benchmark situation, and optimizing the complexity of space.

What are regular expressions in Python, and how can the re module be used for pattern matching? What are regular expressions in Python, and how can the re module be used for pattern matching? Jun 14, 2025 am 12:26 AM

Python's regular expressions provide powerful text processing capabilities through the re module, which can be used to match, extract and replace strings. 1. Use re.search() to find whether there is a specified pattern in the string; 2. re.match() only matches from the beginning of the string, re.fullmatch() needs to match the entire string exactly; 3. re.findall() returns a list of all non-overlapping matches; 4. Special symbols such as \d represents a number, \w represents a word character, \s represents a blank character, *, , ? represents a repeat of 0 or multiple times, 1 or multiple times, 0 or 1 time, respectively; 5. Use brackets to create a capture group to extract information, such as separating username and domain name from email; 6

See all articles