How Onion Architecture solves the database and business rule problem

𝗕𝘂𝗶𝗹𝘁 𝗮 𝗦𝗶𝗺𝗽𝗹𝗲 𝗔𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝘂𝘀𝗶𝗻𝗴 .𝗡𝗘𝗧, 𝗢𝗻𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 & 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Over the past few days, I worked on implementing a simple authentication system for a microservice-based application using .NET, structured around the Onion Architecture and Repository Pattern. 🧱Project structure MyAuthSystem/ ├── Core/ # Domain Layer (Entities, Interfaces) ├── Application/ # Application Layer (Business Logic) ├── Infrastructure/# Data Access & JWT Implementation └── WebAPI/ # Presentation Layer (Controllers & Endpoints) 🧠Core layer 1) Contains pure domain models and repository interfaces. 2) I implemented User.cs to represent the domain concept of a user. 3) Defined IUserRepository to abstract data access — giving the application the flexibility to switch between storage technologies without changing business logic. ⚙️ Application Layer 1) Responsible for business logic. 2) I implemented AuthService which depends on IUserRepository and IPasswordHasher interfaces from the Core layer. This layer handles authentication logic like registration and login. 🏗️ Infrastructure Layer 1) Implements EF Core repository logic and JWT token generation. Includes: a)AppDbContext: Represents a session with the database. b)UserRepository: Implements IUserRepository using EF Core. C)JwtProvider: Handles secure JWT creation for authentication. 🌐 WebAPI Layer 1) Wires up all dependencies between services and repositories. 2)Defines API endpoints that handle HTTP requests and return appropriate responses. ✅ I successfully tested the Register, Login, and Authentication endpoints — everything worked as expected 🎉 🔸 Key Takeaways 🧠 Onion Architecture helps in building maintainable and testable solutions by clearly separating concerns. 🧰 Repository Pattern gives flexibility to change data sources without modifying business logic. 🔐 Proper layering improves scalability and makes your codebase easier to evolve into full microservices. 💬 I’m excited to keep refining this and eventually integrate it into a larger microservice ecosystem.

🚀 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! ✅

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

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

On the topic of GraphQL vs REST, let's delve into the main differences and benefits of each, along with guidance on when to employ them in your architecture design. **GraphQL:** - GraphQL is a query language for APIs that enables clients to request only the data they need. - It allows for fetching multiple resources in a single request, reducing the number of API calls. - With GraphQL, clients can specify the structure of the response, leading to efficient data retrieval. **REST:** - REST (Representational State Transfer) is an architectural style that uses standard HTTP methods for CRUD operations. - It follows a resource-based approach where each endpoint represents a resource (e.g., /users, /products). - REST is well-suited for stateless client-server communication and caching mechanisms. **Choosing Between GraphQL and REST:** - Use GraphQL when you need flexibility in data fetching, have evolving requirements, or want to reduce over-fetching of data. - Opt for REST when working with well-defined resources, leveraging HTTP caching, or following a more traditional approach to API design. By understanding the distinctions and advantages of GraphQL and REST, you can make informed decisions on which to integrate into your architecture design. #APIs #ArchitectureDesign

🧠 Top 20 System Design Concepts Every Developer Should Master Building scalable and resilient systems isn’t just about coding — it’s about understanding how components interact at scale. These 20 concepts form the backbone of distributed systems, cloud-native architecture, and high-performance design. Let’s break it down 🔧 1️⃣ Load Balancing – Distribute traffic efficiently to avoid bottlenecks. 2️⃣ Caching – Reduce latency and database load by serving from memory. 3️⃣ Database Sharding – Horizontally scale data for large workloads. 4️⃣ Replication – Improve availability and fault tolerance with redundant data copies. 5️⃣ CAP Theorem – Understand the trade-offs between consistency, availability, and partition tolerance. 6️⃣ Consistent Hashing – Enable smooth scaling of distributed nodes with minimal data movement. 7️⃣ Message Queues – Decouple services and enable asynchronous communication. 8️⃣ Rate Limiting – Protect systems from overload by controlling request frequency. 9️⃣ API Gateway – Manage routing, authentication, and throttling at the edge. 🔟 Microservices – Build independently deployable services for modular scalability. 1️⃣1️⃣ Service Discovery – Dynamically locate services in distributed environments. 1️⃣2️⃣ CDNs – Bring content closer to users for lower latency. 1️⃣3️⃣ DB Indexing – Speed up query performance by indexing key attributes. 1️⃣4️⃣ Partitioning – Split datasets for better scalability and parallelism. 1️⃣5️⃣ Eventual Consistency – Balance consistency with high availability in distributed databases. 1️⃣6️⃣ WebSockets – Enable real-time, bidirectional client-server communication. 1️⃣7️⃣ Scalability – Design for horizontal or vertical growth as demand increases. 1️⃣8️⃣ Fault Tolerance – Ensure system continuity amid hardware or software failures. 1️⃣9️⃣ Monitoring – Track metrics, logs, and alerts for system health and performance. 2️⃣0️⃣ AuthN & AuthZ – Secure user access and enforce identity verification. ⚙️ True system design expertise lies in balancing trade-offs — consistency vs. availability, latency vs. throughput, and simplicity vs. flexibility. Understanding how these principles interact helps you make architecture decisions that scale gracefully under real-world conditions. When building distributed systems, it’s not just about adding more servers — it’s about ensuring every piece works in harmony. Tools evolve, but these core principles remain timeless. 🔍 System design isn’t about memorization — it’s about knowing when and why to apply each pattern.

💡 Designing Real Multi-Tenant Architecture (Beyond Just Multiple Databases) Recently, I dove into implementing multi-tenancy in NestJS — and quickly realized the real engineering challenges don’t start at “master DB + tenant DB”… that’s just step one. The real complexity appears when you try to make it scalable, safe, and production-ready: 🧠 Connection Lifecycles: You can't open a new DB connection per request. I built a connection registry with pooling + caching per tenant — significantly reduced latency and eliminated ECONNREFUSED / too many connections issues. 🔁 Atomic Tenant Provisioning: Tenant onboarding = update master DB → encrypt/store tenant DB URI → create tenant DB → seed base data. All wrapped inside a transaction — full rollback if any step fails. ⚙️ Dynamic DB Switching Per Request: A request-scoped middleware resolves the tenant from domain and attaches a tenant-specific DB connection to the request context — without leaking DB logic into services. 🛑 Preventing Connection Leaks & Zombie Connections: Idle tenant connections are automatically cleaned up using TTL strategies — avoiding memory leaks in long-running services. ✅ This results in: • True data isolation • Horizontal scalability • Safe tenant onboarding with rollback • Clean & testable codebase Multi-tenancy isn’t about “multiple databases”. It’s about managing state, lifecycle, and isolation at scale. If you're building SaaS — this is where real engineering begins.

The System Design Master Template is your go-to blueprint for acing interviews and building scalable systems. At the heart of many architectures is the API Gateway, which serves as the single entry point to all backend services. It handles routing, authentication, rate limiting, and request forwarding—making it a key layer for security and traffic management. To ensure fast and reliable content delivery to users around the world, systems often rely on a Content Delivery Network (CDN). A CDN caches static assets like images, videos, and stylesheets across globally distributed servers. This brings content closer to the end user, improving load times and reducing latency significantly. As systems scale, databases need to be partitioned to handle the growing load. Horizontal partitioning, also known as sharding, splits rows of a table across multiple servers, helping distribute data and improve performance. On the other hand, vertical partitioning breaks tables down by columns, which is useful when different queries need access to different subsets of data. In distributed architectures, communication between services is often handled by distributed messaging systems like Apache Kafka or RabbitMQ. These systems allow asynchronous message passing, decouple components, and support high-throughput data pipelines—making them ideal for event-driven systems. For handling files at scale, distributed file systems come into play. These systems span multiple servers but offer a unified way to store and retrieve files. They ensure redundancy, scalability, and consistent access, critical for large-scale applications and data platforms. Lastly, when users need to search for text across large datasets, full-text search is the solution. It uses an inverted index—a structure that maps each word to the documents where it appears—enabling fast and relevant search results. This is how systems like Elasticsearch and Solr power search functionality in apps and websites.

⏩ Navigating the World of Software Architecture ⏩ 🔷 Architecture Layers:   ▸ Presentation Layer: User interface and interaction.   ▸ Business Layer: Core operations and logic.   ▸ Persistence Layer: Manages DB storage and retrieval.   ▸ Data Layer: Directly handles DB operations.  🔷 Key E-Commerce Components:   ▸ Shop UI: Interface for users.   ▸ Catalog Service: Manages product listings.   ▸ SC Service: Manages shopping cart operations.   ▸ Discount Service: Calculates discounts.   ▸ Order Service: Handles order processing.  🔷 Event-Driven Approach:   ▸ Publisher and Subscribers: Event-based communication.   ▸ Event Store: Logs state changes for audit trails.  🔷 Architectural Patterns:   ▸ Component-Based Architecture: Modular and reusable.   ▸ Client-Server Architecture: Centralized server for requests.   ▸ Serverless Architecture: Event-triggered functions, server-free.   ▸ Event-Driven Architecture: Scalable with loosely coupled components.   ▸ Layered (N-Tier) Architecture: Structured but may have bottlenecks.   ▸ Microservices Architecture: Independent and flexible services.   ▸ Monolithic Architecture: Single and tightly coupled codebase. 🔷 Essential Components:   ▸ API Gateway: Manages and routes API traffic.   ▸ Load Balancer: Distributes network or application traffic.  I help technical professionals build impactful career brands on LinkedIn. : Check in Comment below 👇 Create LinkedIn Diagrams for FREE using AI! Join waitlist : Check in Comment below 👇 Follow Ashish Sahu for more tech content

System Design Roadmap If you’re diving deeper into System Design & Architecture, here’s a curated and re-organized collection of must-know concepts, building blocks, patterns, and High-Level Design (HLD) problems — all in one place. 🔑 Core Concepts Scalability → https://lnkd.in/gpge_z76 Latency vs Throughput → https://lnkd.in/g_amhAtN CAP Theorem → https://lnkd.in/g3hmVamx Strong vs Eventual Consistency → https://lnkd.in/gJ-uXQXZ Fault Tolerance → https://lnkd.in/dVJ6n3wA Batch vs Stream Processing → https://lnkd.in/g4_MzM4s API Design → https://lnkd.in/ghYzrr8q ACID Transactions → https://lnkd.in/gMe2JqaF Rate Limiting → https://lnkd.in/gWsTDR3m Distributed Tracing → https://lnkd.in/d6r5RdXG Synchronous vs Asynchronous → https://lnkd.in/gC3F2nvr 🧩 Building Blocks Database → https://lnkd.in/gti8gjpz Caching → https://lnkd.in/gC9piQbJ Distributed Caching → https://lnkd.in/g7WKydNg Load Balancing → https://lnkd.in/gQaa8sXK Horizontal vs Vertical Scaling → https://lnkd.in/gAH2e9du Database Scaling → https://lnkd.in/gAXpSyWQ Database Sharding → https://lnkd.in/gMqqc6x9 Database Indexes → https://lnkd.in/gCeshYVt Data Replication → https://lnkd.in/gVAJxTpS Data Redundancy → https://lnkd.in/gNN7TF7n SQL vs NoSQL → https://lnkd.in/g3WC_yxn Proxy Server → https://lnkd.in/gi8KnKS6 WebSocket → https://lnkd.in/g76Gv2KQ API Gateway → https://lnkd.in/gnsJGJaM Message Queues → https://lnkd.in/gTzY6uk8 🏗 Architectural Patterns Event-Driven → https://lnkd.in/dp8CPvey Microservices → https://lnkd.in/gFXUrz_T Serverless → https://lnkd.in/gQNAXKkb Client–Server → https://lnkd.in/dAARQYzq 📝 High-Level Design (HLD) Problems Unique ID Generator → https://lnkd.in/dudvTKPN Bitly → https://lnkd.in/df5wThJq Search Autocomplete → https://lnkd.in/dyHjWFUb Which of these areas do you find most challenging?