🚀 Controller → Service → Repository Pattern in .NET In a clean architecture, we often see this flow: 🔹 Controller – Handles HTTP requests/responses, keeps things lightweight. 🔹 Service – Holds business logic. This is the brain where rules, validations, and decisions live. 🔹 Repository – Talks to the database, executes queries, and returns data. 👉 The Service Layer is very important because: It separates business logic from controllers. Makes code reusable across multiple controllers. Keeps repositories focused only on data access. Improves testability and maintainability. Example Flow: Controller gets a request → passes to Service → Service applies rules → calls Repository for DB → result returns to Controller → Response to client. This way, each layer has a clear responsibility. Clean, testable, and scalable! ✅
How to Implement Controller → Service → Repository Pattern in .NET
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      Ever tried to change the database of an application and ended up having to change even your business rules? That's exactly the pain Onion Architecture was designed to fix. Onion Architecture is all about putting your business logic at the core, surrounded by layers that depend inward, never outward. Each outer layer can depend on the one inside it, but the core is completely independent. ✅ The layers typically look like this - Core (Domain): Entities, value objects, and business rules - Application: Use cases and services that orchestrate domain logic - Infrastructure: Databases, frameworks, external APIs, UI, etc. The golden rule: the core never knows about the outside world. Databases, frameworks, and APIs depend on the domain, not the other way around. ✅ Why it matters: - Keeps business logic framework-agnostic - Makes testing simpler and faster - Encourages clean, maintainable architecture - Allows easy swapping of infrastructure (e.g., from REST to GraphQL, SQL to NoSQL) ✅ Use Onion Architecture when: - You want long-term maintainability and flexibility - Your app has complex business rules or multiple data sources - You're tired of framework lock-in and messy dependencies The takeaway: Protect your core. Everything else is just an outer layer. To view or add a comment, sign in 
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      When working with data in your application, separating data access logic from business logic helps keep your code clean, testable, and maintainable. That’s where Repository and Unit of Work patterns come into play. Together, they simplify database interactions and ensure changes are consistent. ⚙️ Repository Pattern - The Repository Pattern acts as a middle layer between your application and the data source. - It hides the implementation details of data access, providing a clean interface for CRUD operations. Key Benefits: - Abstracts away data access logic - Promotes testability (easy to mock repositories) - Supports clean separation of concerns - Works well with EF Core and CQRS Example: public interface IGenericRepository<T> where T : class { Task<IEnumerable<T>> GetAllAsync(); Task<T> GetByIdAsync(int id); Task AddAsync(T entity); void Update(T entity); void Delete(T entity); } 🔁 Unit of Work Pattern - The Unit of Work Pattern manages multiple repositories under a single transaction. - It ensures that all database operations succeed or fail as a unit, preventing inconsistent data. Example: public interface IUnitOfWork : IDisposable { IProductRepository Products { get; } IOrderRepository Orders { get; } Task<int> CompleteAsync(); } You call CompleteAsync() once — committing all changes across repositories in a single transaction. 💡 Why Use These Patterns ✅ Keeps your code clean and decoupled ✅ Centralizes transaction management ✅ Simplifies testing and mocking ✅ Ideal for enterprise-grade architectures ✅ Works beautifully with EF Core + Clean Architecture #dotnetcore #softwarearchitecture #designpatterns #repositorypattern #unitofwork #efcore #csharp #cleanarchitecture #backenddevelopment #programming To view or add a comment, sign in 
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      Auditing Architecture — Global Table or Separate Tables? Auditing in many ways is a focal point of design in enterprise systems — after each major alteration in the system. But one basic question will usually come to mind: Do we use a single global audit table, or an individual audit table for each module? It seems like a small detail… But in the world, it really matters here for architecture, scalability, and maintenance — especially for microservices systems or when auditing asynchronously with something like RabbitMQ. This is how I like to think about the tradeoffs 1. One Table for the Whole System Pros: Less schema and faster to set up Easier to add new event types to add Cons: The table grows too fast → performance problems Difficulty filtering on analytics Harder to manage access control Migration later aches on the code level 2. Tables per Module Pros: Better separation of concerns Easier to scale, secure, and store archives Simpler queries and cleaner indexing Ideal for modular or multi-tenant designs Cons: More setup effort and more tables to work with Somewhat higher initial development cost My takeaway No "one-size-fits-all" solution. But here's a rule of thumb: In small or medium systems, a single audit table can be sufficient. For large-scale, big enterprise systems, a single table per module is generally the smarter long-term choice — more flexible, decoupled, and cleaner to scale. Good architecture at its core is about intentional tradeoffs, not unnecessary complexity. What do you do when it comes to auditing in large systems? Do you prefer a central audit table or decoupled ones per service/module? #SoftwareArchitecture #SpringBoot #Microservices #Java #CleanArchitecture #SystemDesign #BackendDevelopment #Scalability #EngineeringBestPractices To view or add a comment, sign in 
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      🏗️ Clean Architecture + Repository & Service Patterns in .NET (Made Practical) 🏗️ Clean Architecture in .NET (Made Practical) Imagine you’re building a house 🏠 — you wouldn’t run electrical wiring through the bathroom plumbing, right? Everything has a purpose and stays organized. That’s exactly what Clean Architecture does for your codebase. 🔹 The Core Idea Separate your code into independent layers: 1️⃣ Domain → The heart ❤️ — business rules, entities, and interfaces. 2️⃣ Application → Use cases (e.g., CreateOrder, CalculateDiscount). 3️⃣ Infrastructure → Where data lives — EF Core, API calls, file systems. 4️⃣ Presentation → API controllers, Razor Pages, or Angular UI. 👉 Rule: Outer layers can depend on inner layers, but never the other way around. ⚙️ Repository Pattern Your Repository acts as a translator between the database and your application. Instead of sprinkling SQL or EF queries across services: var user = _userRepository.GetById(5); Now, if you ever switch from SQL Server to MongoDB, only the repository changes — the rest of the app stays intact. 🔄 ✅ Keeps data logic in one place. ✅ Improves testability with interfaces like IUserRepository. ⚙️ Service Pattern Your Service layer owns the business logic — rules, validation, and decisions. Example: public class OrderService { public void PlaceOrder(Order order) { // Check stock, calculate discounts, handle payment, etc. } } Controllers just call the service — they don’t make business decisions. That’s how you keep controllers thin and logic reusable. 💡 Practical Tip In a real .NET solution: /Domain └── Entities, Interfaces /Application └── DTOs, Services /Infrastructure └── Repositories, EF Context /Presentation └── Controllers, Views Each project references only what it needs — this makes refactoring or scaling painless. 🚀 Key Takeaway Repository → Handles data access Service → Handles business logic Controller → Handles API or UI requests Together, they create a structure that’s clean, testable, and future-proof. To view or add a comment, sign in 
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      Simple explanation Part 3: Essential Software Architecture Styles • Layered Architecture (N-Tier) — separates presentation, business, and data logic for cleaner design. • Client-Server — clients send requests, servers process and respond. • Microservices — small, independent services that scale and deploy separately. • Event-Driven — components react asynchronously to events, enabling flexibility. • Microkernel (Plugin) — a core system with extendable plugins. • Space-Based — distributed in-memory/grid systems for high performance. • Master-Slave — one master delegates tasks, slaves execute and return results. • Pipe-Filter — processes data through sequential filters connected by pipes. • Broker — mediates communication between clients and distributed services. • Peer-to-Peer (P2P) — all nodes share resources equally, acting as both client & server. • Service-Oriented (SOA) — loosely coupled services with defined interfaces. • Model-Driven (MDA) — emphasizes models and transformations over code. • Hexagonal / Clean Architecture — isolates core domain logic with adapters for UI, DB, and external systems. #Software #Architecture #Java #Simple To view or add a comment, sign in 
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      day-13 (System Design) :- >> GraphQL - A modern query language for your API. >> What is GraphQL? - It is a query language for APIs. - Provides a schema of the data in the API. - Gives clients the power to ask for exactly what they need. - It sits between clients and backend services. - Aggregates multiple resource requests into a single query. - Supports mutations and subscriptions. >> How is GraphQL similar to REST? - Both send HTTP requests. - Both make requests via a URL. - Both can return a JSON response in the same shape. >> Key Differences: - With GraphQL, clients specify the exact resources and fields they want. - In REST, the API implementer decides what gets returned. - In GraphQL, the client controls what to include. >> Unlike REST: > GraphQL doesn’t use URLs to specify available resources. Instead, it uses a GraphQL schema. >> Drawbacks of GraphQL: - In REST, you don’t need special libraries to consume an API. - GraphQL often requires heavier tooling support on both client and server. It is more difficult to cache. To view or add a comment, sign in 
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      Why DTOs and Entities Matter in .NET Architecture A common question in.NET design circles-what's the real difference between Entities and DTOs, and why does it matter? ◆ Entities These are your database models - great for storing and managing your data. But exposing them outside your data layer? That's where problems start: tight coupling, potential security risks, and unintended side effects. ◆ DTOS (Data Transfer Objects) Think of DTOs as the light, secure messengers between your API and clients. They: Carry just the data you need, nothing extra Protect sensitive info (like passwords) from leaking Keep your API flexible by tailoring objects for different endpoints Shield your app from breaking changes as your database evolves Make validation clean and straightforward Best Practice Keep Entities confined to your data layer Let DTOs travel safely across API and client boundaries Mastering this balance not only improves security and performance but also keeps your codebase maintainable and scalable. Would you like a version with more real-world examples or a concise snippet for quick sharing? To view or add a comment, sign in 
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      I was knee-deep in a legacy migration for a platform where business logic was bolted onto Express routes and Mongo models, every framework tweak meant dismantling the core. I re-factored it with Hexagonal Architecture (a.k.a. Ports and Adapters), with the domain housed in a tidy hexagon and an adapter plugging in for HTTP, DB, or even CLI. The payback? Swapped back-end framework twice without ever laying hands on the heart of the application. Hexagonal Magic: It flips the script: Instead of center depending on outside tech, outside conforms to center via ports (interfaces) and adapters (implementations). To me that translated to declaring ports for "UserRepository" and "PaymentProcessor" and switching adapters from SQL to NoSQL or REST with ease. On the front-end side, I used it loosely with React hooks, separating UI logic from state management. My Battle-Tested Views Hexa's a beast for long-lived applications where change's gonna occur like finance or healthcare. It promotes testability with mocks and leaves the domain in its natural state. It's got overhead, though; in greenfield MVPs, it can cripple you if you're on your own. To view or add a comment, sign in 
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      🚀 .NET Tip — API Architecture Simplified A well-structured API is the foundation of every scalable .NET application. Following the Controller → Service → Repository flow keeps your code clean, testable, and maintainable. Here’s how each layer plays its role: 🎛️ Controller: Handles HTTP requests and responses only — no business logic here. ⚙️ Service: Contains the business rules and coordinates between controller and repository. 🗂️ Repository: Manages data access, keeping EF Core or SQL logic isolated from business layers. 🧩 Why it matters: Easier unit testing Separation of concerns Improved scalability Cleaner maintenance and onboarding To view or add a comment, sign in 
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