Low Level Design (LLD): Using Python

Explore object oriented programming as a design approach that models real world entities using objects and classes, highlighting abstraction, encapsulation, inheritance, and polymorphism, with practical Python considerations.

Learn how classes provide a blueprint for objects, with attributes and methods, and how a constructor with self initializes state; instantiate objects to hold data and show details with display_info.

Learn how Python uses abstract base classes from the abc module to define interfaces that enforce a contract for implementing classes, enabling multiple inheritance and real-world examples like payments.

Explore how a child class derives properties and methods from a parent class, enabling code reuse, maintainability, and method overriding via super in Python's single, multi-level, hierarchical, and multiple inheritance.

Master polymorphism in Python by exploring compile time and runtime forms, method overloading limitations, and duck typing. Learn to implement flexible interfaces with default parameters, *args, and **kwargs.

Abstraction hides implementation details and exposes only essential behavior to reduce complexity and improve maintainability; in Python, abstract base classes or ABCs enable this through abstract and concrete methods.

Explore encapsulation in object-oriented design by bundling data and methods in a class, using getters and setters to enforce data hiding with private, protected, and public access.

Explore aggregation in object oriented programming, where a container holds content objects that can exist independently. Appreciate how association enables loose coupling, modularity, and maintainability.

Explore composition as a has-a relationship in object-oriented design, showing how components like engine, wheel, and transmission form a car for reusable, flexible, and maintainable code.

Learn how association in object oriented programming links objects—1-to-1, 1-to-many, many-to-many—while keeping them independent and loosely coupled.

Learn solid principles to build maintainable, scalable, testable software using the five solid principles: single responsibility, open-closed, Liskov substitution, interface segregation, and dependency inversion.

Learn the single responsibility principle (SRP): a class should have one reason to change; split a user manager into authentication, profile management, and email notification classes to improve maintainability.

Apply the open/closed principle in Python with an abstract shape base and concrete rectangle and circle classes to compute area and perimeter, enabling new shapes like triangle without modifying code.

Learn the LSP by showing that a vehicle base class can be replaced by car or bicycle without breaking the program, using a generalized start method.

Apply the interface segregation principle to build small, focused interfaces for audio and video operations, preventing bloated interfaces and promoting loose coupling and maintainable design.

Explore the dependency inversion principle, showing how high level and low level modules depend on abstractions to enable loose coupling, with Gmail and Outlook email clients demonstrating flexible, extensible design.

Explore UML diagrams and class diagrams as a key low level design tool to model software structure and behavior, including attributes, methods, interfaces, and relationships like inheritance and association.

Learn creational design patterns in Python, focusing on decoupled object creation to improve flexibility and reusability. Begin with singleton, then factory method, abstract factory, builder, and prototype.

Learn how the singleton pattern ensures a single class instance with a global access point while preventing external construction; explore Python implementations, lazy loading, and thread-safety considerations.

Explore how the factory method decouples object creation from usage by delegating to subclasses, enabling open-closed principle, loose coupling, and flexible product instantiation for trucks, ships, and beyond.

Explore the abstract factory creational design pattern to produce families of related objects without specifying concrete classes, such as modern, Victorian, and Art Deco chairs, sofas, and coffee tables.

Explore the builder pattern, a creational design pattern that separates object construction from representation using builders and a director to create simple and luxury houses.

Explore the prototype design pattern for cloning objects without depending on their concrete classes, using a common clone interface and deep copy to safely reproduce complex objects and reduce subclassing.

Explore how structural patterns compose classes and objects to form scalable, maintainable software with flexible component interactions. Learn adapter, bridge, composite, decorator, facade, lightweight, and proxy patterns.

Explore the adapter pattern, a structural design that reconciles incompatible interfaces, enabling XML to JSON conversion in a stock market app in Python.

Apply the composite design pattern to build a tree of products and boxes in Python, allowing leaves and containers to be treated uniformly and prices to be calculated recursively.

Explore the decorator pattern in Python to wrap a basic notifier with email, SMS, Slack, and Facebook decorators. Learn runtime composition over inheritance for flexible design.

Learn to implement a facade that offers a simple interface to a complex video processing library, coordinating subcomponents like video file handling, codecs, and bit reading.

Apply the flyweight pattern to share intrinsic state among many similar objects, reducing memory usage by storing common data in tree types and keeping only unique positions per tree.

Explore the proxy design pattern in Python, providing a substitute for a heavyweight object to enable lazy initialization, access control, and logging while preserving the original interface.

Explore how behavioral design patterns govern object interactions to define clear communication, assign responsibilities, and scale low level design; study ten types, including chain of responsibility, command, and observer.

Implement a chain of responsibility to pass requests through a sequence of handlers, allowing authentication, authorization, validation, and order processing to occur in a runtime configurable chain.

The command design pattern turns a request into a standalone object, enabling undoable operations, queuing, remote execution, and decoupling the ui from business logic in a Python text editor example.

Learn how the iterator pattern decouples traversal from a collection's structure, enabling traversal across lists, trees, or graphs via a dedicated iterator object, without exposing internal representations.

Decouple UI components by routing communications through a central mediator, reducing direct dependencies. Demonstrate an authentication dialog that toggles login and registration modes, showing centralized control and easier reuse.

Explore the memento pattern in Python, enabling undo and state snapshots by letting the originator create immutable snapshots stored by a caretaker, preserving encapsulation and enabling history tracking.

Define a Python observer pattern with a publisher and subscribers that subscribe or unsubscribe to state changes, notifying customers via email or sms about new products.

Learn in Python how the state design pattern models an object's behavior through concrete states such as draft, moderation, and publish, forming a finite state machine with defined transitions.

Explains the strategy design pattern in Python: separate routing algorithms into concrete strategies (driving, walking, public transport) used by a navigator context at runtime, with pros and cons.

Explore the template method design pattern, defining the algorithm skeleton in a superclass and letting subclasses override abstract steps—open file, extract data, analyze, and report across csv, doc, pdf.

Explore the visitor design pattern to separate algorithms from objects in a geo graph, enabling XML export and statistics collection without modifying existing classes.

Design a parking garage system with a single-level layout for compact, regular, and large vehicles. Apply Python data models and the singleton pattern for parking lot, spots, tickets, and vehicles.

Design a parking garage system by outlining functional and non-functional requirements, defining API endpoints (reserve, payment, cancel, allocate), and building a scalable architecture with API gateway, caching, and read replicas.

Celebrate completing the low level design course in Python and apply object oriented programming basics, solid principles, and creational, structural, and behavioral design patterns like singleton and observer.