Architecture patterns commonly used in microservice based application

Microservices architecture involves breaking down a monolithic application into smaller, independently deployable services. Various architecture patterns and principles are commonly used in microservices to address specific challenges and ensure that the architecture remains scalable, maintainable, and resilient. Here, we discuss common patterns used in microservices, which are segregated based on use cases:

Service Decomposition Patterns:

• Single Responsibility Principle (SRP): Each microservice should have a single responsibility or focus on one specific business capability.
• Domain-Driven Design (DDD): Apply DDD principles to identify service boundaries and define bounded contexts around specific business domains.
• Aggregate Pattern: In DDD, use aggregates to encapsulate related entities and ensure data consistency within a microservice.

Communication Patterns:

• API Gateway: Implement an API Gateway to manage and route incoming requests to the appropriate microservices. This can help with load balancing, authentication, and API composition.
• Event-Driven Architecture: Use events and messaging patterns (publish-subscribe, message queues) for asynchronous communication between microservices.
• Service Discovery: Implement a service discovery mechanism to dynamically locate and connect to other microservices. Tools like Consul, Eureka, and Kubernetes Service Discovery can help with this.

Data Management Patterns:

• Database per Service: Each microservice should have its database to maintain data isolation. Implement data synchronization or sharing mechanisms as needed.
• Saga Pattern: Use the Saga pattern to manage distributed transactions across multiple microservices. It involves a series of local transactions and compensating actions to ensure eventual consistency.
• CQRS (Command Query Responsibility Segregation): Separate the write and read models of your data to optimize for different requirements. Commands and queries are handled by different microservices.
• Event Sourcing: Store all changes to an application’s state as a sequence of immutable events. This can be beneficial for auditing and rebuilding state.

Resiliency Patterns:

• Circuit Breaker: Implement circuit breakers to prevent repeated calls to failing services, allowing them to recover.
• Retry Mechanisms: Implement retries with exponential backoff for handling failed requests, especially to address transient failures.
• Timeouts and Bulkheads: Set timeouts for service requests and implement bulkheads to isolate failures in one microservice from affecting others.

Security Patterns:

• OAuth 2.0 and JWT: Use OAuth 2.0 for authentication and JWT (JSON Web Tokens) for authorization between microservices.
• API Security Gateway: Implement a security gateway to handle rate limit, routing, security, authentication and authorization at the edge of your microservices architecture.

Observability Patterns:

• Centralized Logging and Monitoring: Use centralized logging and monitoring tools to track and analyze the behavior of your microservices.
• Distributed Tracing: Implement distributed tracing to visualize and analyze requests as they flow through multiple microservices.

Testing Patterns:

• Consumer-Driven Contract Testing: In a microservices ecosystem, consumer-driven contract testing ensures that changes to a microservice’s API do not break its consumers.
• Chaos Engineering: Test the resilience of your microservices architecture by introducing controlled failures to uncover weaknesses.

Deployment Patterns:

• Blue-Green Deployment: Implement blue-green deployments to minimize downtime and risk during updates.
• Canary Releases: Gradually roll out new versions of microservices to a subset of users to detect issues before a full release.

Scaling Patterns:

• Horizontal Scaling: Scale microservices horizontally by adding more instances to handle increased load.
• Autoscaling: Implement autoscaling based on matrix like traffic or resource utilization.

These design patterns are not exhaustive, and the specific patterns you choose will depend on the needs and characteristics of your microservices architecture. Combining these patterns can help you create a robust, maintainable, and scalable microservices system.