Skip to main content

High-level Architectural Patterns and Low-level Design Patterns

                                                                                        Photo by Alex Fu

Let's discuss high-level architectural patterns and low-level design patterns to solve software problems.

High-level architectural patterns are patterns that describe the overall structure of a software system. They are used to design systems that are scalable, maintainable, and reliable. Some examples of high-level architectural patterns include:

  • Model-View-Controller (MVC)
  • Client-Server
  • Microservices
  • Event-Driven Architecture

Low-level design patterns are patterns that describe the design of individual components within a software system. They are used to design components that are efficient, reusable, and easy to test. Some examples of low-level design patterns include:

  • Singleton
  • Observer
  • Factory Method
  • Strategy

To solve software problems using design patterns, you need to be able to identify the problem that you are trying to solve and then find the appropriate design pattern to use. You also need to be able to apply the design pattern correctly.

Here are some examples of how design patterns can be used to solve software problems:

  • The MVC pattern can be used to design a web application that is scalable and maintainable.
  • The Client-Server pattern can be used to design a distributed system that is reliable and secure.
  • The Microservices pattern can be used to design a complex system that is easy to develop and maintain.
  • The Event-Driven Architecture pattern can be used to design a system that is responsive to events.
  • The Singleton pattern can be used to ensure that there is only one instance of a particular class in a system.
  • The Observer pattern can be used to notify objects when the state of another object changes.
  • The Factory Method pattern can be used to create objects without specifying the exact class of the object that is created.
  • The Strategy pattern can be used to select an algorithm at runtime.

Design patterns are a powerful tool that can be used to solve a wide variety of software problems. However, it is important to use them wisely. If you use the wrong design pattern, or if you apply it incorrectly, it can make your code more complex and difficult to maintain.

Design patterns are a powerful tool that can be used to solve a wide variety of software problems. However, it is important to use them wisely. If you use the wrong design pattern, or if you apply it incorrectly, it can make your code more complex and difficult to maintain.

Here is an example of a design pattern with code and a design image:

Design pattern: Singleton

Definition: The singleton pattern restricts the instantiation of a class to one object. This is useful when exactly one object is needed to coordinate actions across the system.

class Singleton:

    _instance = None


    def __new__(cls):

        if cls._instance is None:

            cls._instance = super().__new__(cls)

        return cls._instance


    def __init__(self):

        pass


    def do_something(self):

        pass


# Usage:


singleton = Singleton()

singleton.do_something()


Design pattern: Factory Method

Definition: The factory method pattern defines an interface for creating an object, but lets subclasses decide which class to instantiate. Factory methods let a class defer instantiation to subclasses.

class VehicleFactory:

    def create_vehicle(self, type):

        if type == "car":

            return Car()

        elif type == "truck":

            return Truck()

        else:

            raise ValueError("Unknown vehicle type: {}".format(type))


class Car:

    pass


class Truck:

    pass


# Usage:


factory = VehicleFactory()

car = factory.create_vehicle("car")

truck = factory.create_vehicle("truck")


These are just two examples of design patterns. Many other design patterns can be used to solve common software problems.

 

Comments

Popular posts from this blog

Financial Engineering

Financial Engineering: Key Concepts Financial engineering is a multidisciplinary field that combines financial theory, mathematics, and computer science to design and develop innovative financial products and solutions. Here's an in-depth look at the key concepts you mentioned: 1. Statistical Analysis Statistical analysis is a crucial component of financial engineering. It involves using statistical techniques to analyze and interpret financial data, such as: Hypothesis testing : to validate assumptions about financial data Regression analysis : to model relationships between variables Time series analysis : to forecast future values based on historical data Probability distributions : to model and analyze risk Statistical analysis helps financial engineers to identify trends, patterns, and correlations in financial data, which informs decision-making and risk management. 2. Machine Learning Machine learning is a subset of artificial intelligence that involves training algorithms t...

Wholesale Customer Solution with Magento Commerce

The client want to have a shop where regular customers to be able to see products with their retail price, while Wholesale partners to see the prices with ? discount. The extra condition: retail and wholesale prices hasn’t mathematical dependency. So, a product could be $100 for retail and $50 for whole sale and another one could be $60 retail and $50 wholesale. And of course retail users should not be able to see wholesale prices at all. Basically, I will explain what I did step-by-step, but in order to understand what I mean, you should be familiar with the basics of Magento. 1. Creating two magento websites, stores and views (Magento meaning of website of course) It’s done from from System->Manage Stores. The result is: Website | Store | View ———————————————— Retail->Retail->Default Wholesale->Wholesale->Default Both sites using the same category/product tree 2. Setting the price scope in System->Configuration->Catalog->Catalog->Price set drop-down to...

How to Prepare for AI Driven Career

  Introduction We are all living in our "ChatGPT moment" now. It happened when I asked ChatGPT to plan a 10-day holiday in rural India. Within seconds, I had a detailed list of activities and places to explore. The speed and usefulness of the response left me stunned, and I realized instantly that life would never be the same again. ChatGPT felt like a bombshell—years of hype about Artificial Intelligence had finally materialized into something tangible and accessible. Suddenly, AI wasn’t just theoretical; it was writing limericks, crafting decent marketing content, and even generating code. The world is still adjusting to this rapid shift. We’re in the middle of a technological revolution—one so fast and transformative that it’s hard to fully comprehend. This revolution brings both exciting opportunities and inevitable challenges. On the one hand, AI is enabling remarkable breakthroughs. It can detect anomalies in MRI scans that even seasoned doctors might miss. It can trans...