Monolith vs Microservices in .NET Core 1. Monolithic Architecture Definition : A single, unified codebase where all modules (UI, business logic, data access) are part of one large application. Deployment : Deployed as a single unit (e.g., one .exe or .dll ). Scaling : Scales by cloning the entire application (vertical/horizontal scaling). Communication : Internal method calls (no network). Tech Stack : Typically limited to a single framework/runtime. Example in .NET Core : An ASP.NET Core MVC app with controllers, services, and EF Core all in the same project. Single database, one codebase, deployed to IIS/Kestrel. 2. Microservices Architecture Definition : A collection of small, independent services, each responsible for a specific business function. Deployment : Each service runs independently (often in Docker containers). Scaling : Scale individual services based on demand. Communication : Via APIs (REST, gRPC, message queues). ...
🧠 Introduction
Microservices architecture is a modern software development style that structures an application as a collection of loosely coupled services. But to implement microservices effectively, developers rely on several design patterns.
In this guide, we’ll explore the top microservices design patterns, their purpose, and how they solve real-world challenges in distributed systems.
🧱 1. Decomposition Patterns
a. Decompose by Business Capability
This pattern breaks down services based on business functionality. Each microservice owns a specific feature such as "Billing", "Inventory", or "User Management".
📝 Example:
An e-commerce app might have microservices like Order Service, Payment Service, and Shipping Service.
b. Decompose by Subdomain
Aligned with Domain-Driven Design (DDD), this pattern splits services based on bounded contexts.
🧩 Example:
In a healthcare system, you might have separate services for Patient Records, Appointments, and Billing.
🔄 2. Integration Patterns
a. API Gateway Pattern
Acts as a single entry point to multiple microservices. It handles routing, security, rate limiting, and request transformation.
🛡 Benefits:
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Centralized access control
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Easier versioning
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Simplifies client-side logic
b. Aggregator Pattern
Collects data from multiple microservices and returns a combined response.
📦 Use Case:
A dashboard service fetching user details, orders, and recommendations from three different services.
🔁 3. Database Patterns
a. Database per Service
Each microservice has its own database to ensure loose coupling and independence.
⚠️ Challenge:
Harder to perform JOIN queries across databases.
🛠 Solution:
Use API composition or event sourcing for cross-service data retrieval.
⚙️ 4. Observability Patterns
a. Log Aggregation
Centralizes logs from all services using tools like ELK Stack or Fluentd.
b. Distributed Tracing
Tracks requests across services using tools like Jaeger or Zipkin. Helps diagnose latency and failures in production.
💬 5. Communication Patterns
a. Synchronous (REST/gRPC)
Request-response model, simple but tightly coupled.
b. Asynchronous (Message Queue)
Uses brokers like RabbitMQ, Kafka. Ideal for event-driven architectures.
📩 Example:
A payment service sends a “Payment Completed” event, which the shipping service listens for.
🔄 6. Saga Pattern
Manages data consistency in distributed transactions. It breaks a big transaction into a series of local transactions, each with a rollback action.
🛒 Example:
Placing an order → Charging payment → Updating inventory
If inventory fails, a rollback triggers refunding the payment.
🧼 7. Circuit Breaker Pattern
Prevents a service from trying to call a failing service repeatedly, avoiding system overload.
⚡ Tools:
Resilience4j, Hystrix
🧩 8. Strangler Pattern
Useful for migrating monoliths to microservices. It replaces old parts of the app gradually without complete rewriting.
🌿 Example:
You keep the old payment module but slowly introduce a new payment service. Traffic shifts as the new service stabilizes.
🎯 Conclusion
Design patterns are the foundation for scalable, reliable, and maintainable microservices. Choosing the right pattern can save development time, improve system health, and enhance your architecture.
📌 Tip: Always tailor patterns based on your application’s domain, team size, and system complexity.
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