Design Patterns

Introduction to Design Patterns (Python based)

Design Patterns are typical solutions to common problems in software design. Each pattern is like a blueprint that you can customize to solve a particular design problem in your code. They are not finished designs that can be transformed directly into code but are templates for how to solve a problem in various situations. Design patterns gained popularity in computer science after the book Design Patterns: Elements of Reusable Object-Oriented Software was published in 1994 by the so-called "Gang of Four" (Gamma et al.).

Why Use Design Patterns?

1. Solutions to Common Problems: Design patterns provide solutions to common software design issues. They are based on proven techniques and provide a standard terminology.
2. Best Practices: Using design patterns means employing industry best practices, enhancing code readability and reliability.
3. Communication: Design patterns provide a standard terminology and are specific to particular scenarios, making it easier for software developers to communicate.
4. Scalability and Maintainability: Patterns allow the design to be scalable and maintainable with less effort and cost.

Types of Design Patterns

Design patterns can be categorized into three types:

1. Creational Patterns

These patterns provide various object creation mechanisms, which increase flexibility and reuse of existing code.

• Singleton: Ensures a class has only one instance and provides a global point of access to it.
• Builder: Constructs a complex object step by step.
• Prototype: Creates new objects by copying an existing object.
• Factory Method: Creates an instance of several derived classes.
• Abstract Factory: Creates an instance of several families of classes.

2. Structural Patterns

These patterns explain how to assemble objects and classes into larger structures, while keeping these structures flexible and efficient.

• Adapter: Allows incompatible interfaces to work together.
• Bridge: Separates an object’s interface from its implementation.
• Composite: Composes objects into tree structures to represent part-whole hierarchies.
• Decorator: Adds new functionalities to an object dynamically.
• Facade: Provides a simplified interface to a complex subsystem.
• Flyweight: Reduces the cost of creating and manipulating a large number of similar objects.
• Proxy: Provides a surrogate or placeholder for another object to control access to it.

3. Behavioral Patterns

These patterns are concerned with algorithms and the assignment of responsibilities between objects.

• Chain of Responsibility: Passes a request along a chain of handlers.
• Command: Encapsulates a command request as an object.
• Interpreter: Implements a specialized language.
• Iterator: Sequentially accesses the elements of a collection.
• Mediator: Defines simplified communication between classes.
• Memento: Captures and externalizes an object’s internal state.
• Observer: A way of notifying change to several classes.
• State: Allows an object to alter its behavior when its internal state changes.
• Strategy: Allows one of a family of algorithms to be selected on-the-fly at runtime.
• Template Method: Defines the skeleton of an algorithm in an operation.
• Visitor: Separates an algorithm from an object structure.

Conclusion

Design patterns are crucial for creating robust, scalable, and maintainable software. They provide solutions to common problems faced in software design and help to accelerate the development process.

Criticism:

• http://www.paulgraham.com/icad.html

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