1. Monolithic Architecture
In a monolithic architecture, the entire application is built as a single, unified unit. This type of architecture has all the components (e.g., user interface, business logic, data access) in a single codebase, and it’s typically deployed as one package.
Example Diagram of Monolithic Architecture:
+-----------------------------------------+
| Monolithic App |
|-----------------------------------------|
| User Interface |
| Business Logic |
| Data Access Layer |
+-----------------------------------------+
|
|
Database
- Deployment: Single deployment unit, meaning updates require redeploying the entire application.
- Scaling: Vertical scaling is typically required (increasing hardware resources).
2. Microservices Architecture
Microservices architecture involves breaking down an application into smaller, loosely coupled services, each responsible for a specific function (e.g., user service, order service). Each service can be developed, deployed, and scaled independently.
Example Diagram of Microservices Architecture:
+-------------------+ +-------------------+
| User Service | | Order Service |
+-------------------+ +-------------------+
| |
| |
Database 1 Database 2
- Deployment: Each service is deployed independently, allowing for individual scaling, updates, and fault isolation.
- Scaling: Horizontal scaling; services can be scaled based on specific requirements.
3. Service Decomposition
Service decomposition is the process of breaking down a monolithic application into distinct, loosely coupled services in a microservices architecture. It ensures that each service has a single responsibility and manages its own data, aligning closely with the Single Responsibility Principle.
Key Considerations for Service Decomposition:
- Domain-Driven Design (DDD): Break down services based on business domains. For example, in an e-commerce platform, you might have separate services for users, orders, products, and inventory.
- Service Boundaries: Clearly define boundaries for each service to avoid data coupling. For example, only the User service should handle user-specific information.
- Data Ownership: Each service owns its data, often with a separate database, to ensure independence and data consistency within the service.
4. Pros and Cons of Microservices
Pros of Microservices:
Scalability: Individual services can be scaled independently, optimizing resource usage. For example, an order-processing service might need more instances than a user profile service.
- Independent Deployment: Teams can deploy and update services without impacting the entire system, reducing downtime and enabling continuous deployment.
- Fault Isolation: If one service fails (e.g., payment service), other services (e.g., browsing products) can continue to function, enhancing system resilience.
- Technology Flexibility: Different services can use different technology stacks (e.g., Python for one service, Java for another) as long as they communicate through a common protocol (e.g., REST or gRPC).
- Optimized Team Structure: Teams can work on individual services, promoting parallel development and ownership of specific business domains.
Cons of Microservices:
Increased Complexity: Managing multiple services, each with its own deployment and scaling requirements, is complex and requires robust DevOps practices.
- Inter-Service Communication Overhead: Microservices communicate over the network, which adds latency and requires careful handling of network failures.
- Data Consistency Challenges: Ensuring data consistency across services is complex and often relies on eventual consistency rather than strict ACID compliance.
- Deployment Overhead: Each service has its deployment pipeline, which increases deployment and maintenance overhead.
- Operational Overhead: Monitoring, logging, and debugging in a distributed system is more challenging. Observability tooling like distributed tracing (e.g., Jaeger, Zipkin) becomes necessary.