Integration / Communication Patterns

These patterns define how microservices interact and exchange data. Options include synchronous APIs (REST/gRPC) for real-time needs, asynchronous messaging (event-driven, message queues) for decoupling, and hybrid approaches for flexibility. Choosing the right communication style ensures scalability and reliability.

Decomposition Patterns

These patterns guide how to break down a monolithic application into smaller, independent services. Common approaches include decomposition by business capability, subdomain, or transaction boundaries. The goal is to ensure services are loosely coupled, cohesive, and aligned with business needs.

Resiliency & Fault Tolerance Patterns

These patterns help microservices remain stable in the face of failures, latency, or overloads. Techniques like circuit breakers, retries, bulkheads, and timeouts protect systems from cascading failures. They ensure high availability and graceful degradation of services under stress.

Data Management Patterns

These patterns address challenges of managing distributed data across independent services. Options include Database-per-Service, Saga for distributed transactions, Event Sourcing, and CQRS for read/write separation. The focus is on consistency, scalability, and autonomy without tight coupling.

Observability & Deployment Patterns

These patterns enable visibility, monitoring, and smooth delivery of microservices. Observability practices include logging, tracing, and metrics for end-to-end insights, while deployment strategies like service discovery, sidecar, blue-green, and canary releases ensure reliable operations. They support both runtime stability and continuous delivery.