This repository provides reference implementations of common system design patterns in Python and Java. It is primarily an educational resource containing standalone algorithm implementations for consistent hashing, rate limiting (token bucket, leaky bucket, sliding window variants), and load balancing algorithms (round robin, weighted round robin, least connections, least response time, IP hash). The code serves as a learning tool for system design interviews and concepts rather than a production-ready application. Each implementation is self-contained with usage examples embedded in the source files. The repository has no external dependencies beyond standard libraries, making it easy to extract and study individual patterns.

This repository is an educational resource focused on teaching low-level design (LLD) and object-oriented design (OOD) principles through runnable code examples. It contains implementations of the Gang of Four design patterns (creational, structural, behavioral) across multiple programming languages including C++, Java, C#, Python, Go, and JavaScript/TypeScript. Additionally, it provides problem statements and solutions for common LLD interview questions such as parking lot systems, ATM implementations, vending machines, and social networking platforms. The repository serves as both a reference guide and a practical learning tool. Each design pattern is demonstrated with minimal, focused examples that isolate the pattern's core mechanics. The problem/solution sections show how these patterns can be applied in real-world scenarios, complete with class diagrams and explanations. While the code is functional and demonstrative, it prioritizes clarity of concept over production robustness, making it ideal for learning but requiring adaptation for actual deployment. Although primarily educational, the repository contains executable code that could be considered a collection of mini-applications. Each pattern implementation and problem solution can be run independently to observe the behavior. The multi-language approach allows developers to compare how the same pattern is expressed in different ecosystems, highlighting both universal principles and language-specific idioms.