Hereβs a comprehensive list of microservices design patterns, organized by category. These are well-known, industry-recognized patterns used to design, build, deploy, and manage microservices architectures effectively.
π§© 1. Decomposition Patterns
How to break a system into microservices.
- Decompose by Business Capability
- Split services according to business functions (e.g.,
Order Service,Payment Service).
- Split services according to business functions (e.g.,
- Decompose by Subdomain (DDD)
- Use Domain-Driven Design (DDD) to align services with bounded contexts.
- Decompose by Transaction or Workflow
- Break services based on business transactions or workflows.
- Strangler Fig Pattern
- Incrementally replace a monolith by routing requests to new microservices.
- Self-Contained Service
- Each service owns its UI, logic, and data.
- Bulkhead Pattern
- Isolate components so failure in one doesnβt cascade.
βοΈ 2. Integration Patterns
How services communicate and integrate.
- API Gateway Pattern
- A single entry point for all clients, routing to backend services.
- Aggregator Pattern
- Combines results from multiple services into one response.
- Proxy Pattern
- Acts as a mediator between client and multiple microservices.
- Chained Microservice Pattern
- Service A calls Service B, which calls Service C, etc.
- Message Broker / Event Bus Pattern
- Asynchronous communication using message queues (e.g., Kafka, RabbitMQ).
- Backend for Frontend (BFF)
- Different gateways for different clients (web, mobile, etc.).
- API Composition
- API layer composes data from multiple microservices.
πΎ 3. Database and Data Management Patterns
How each microservice handles data.
- Database per Service
- Each service has its own database to ensure loose coupling.
- Shared Database(anti-pattern if overused)
- Multiple services access one database.
- CQRS (Command Query Responsibility Segregation)
- Separate models for reading and writing data.
- Event Sourcing
- Store changes as a series of events, not just current state.
- Saga Pattern
- Manage distributed transactions using local transactions and events.
- Outbox Pattern
- Ensures reliable event publishing along with database changes.
π§ 4. Observability Patterns
How to monitor and debug microservices.
- Log Aggregation
- Collect logs from all services into a centralized system (e.g., ELK Stack).
- Distributed Tracing
- Trace requests across multiple microservices (e.g., OpenTelemetry, Jaeger).
- Metrics and Health Checks
- Monitor performance, uptime, and service health.
- Correlation ID Pattern
- Use a unique ID to trace a request across services.
π 5. Resilience and Reliability Patterns
How services handle failure gracefully.
- Circuit Breaker
- Prevents a service from making repeated failed calls.
- Retry Pattern
- Automatically retry failed requests.
- Timeout Pattern
- Define request timeout to avoid hanging.
- Bulkhead Pattern
- Isolate services/resources to prevent cascading failures.
- Failover Pattern
- Switch to a backup instance in case of failure.
- Compensation Transaction
- Undo steps when a distributed transaction fails.
π 6. Deployment Patterns
How to deploy and version microservices.
- Service Instance per Host
- One instance per VM or container.
- Multiple Service Instances per Host
- Several services share a host.
- Service per Container
- One service per container (common with Docker/Kubernetes).
- Blue-Green Deployment
- Two environments: one live, one idle for seamless updates.
- Canary Deployment
- Release updates gradually to a small subset of users.
- Rolling Update
- Incrementally update instances.
- Sidecar Pattern
- Deploy helper containers (e.g., for logging, monitoring) alongside main service.
π 7. Security Patterns
How to secure microservices.
- Access Token Pattern (JWT / OAuth2)
- Use tokens for user identity and access control.
- API Gateway Authentication
- Offload auth logic to gateway.
- Service-to-Service Authentication (mTLS)
- Use mutual TLS for secure inter-service communication.
- Centralized Security Policy
- Define and enforce common security rules.
- Secrets Management Pattern
- Securely manage API keys, passwords, etc.
ποΈ 8. Cross-Cutting Concerns
General-purpose patterns across services.
- Service Discovery Pattern
- Automatically locate service instances (e.g., via Eureka, Consul).
- Configuration Server Pattern
- Centralize configuration (e.g., Spring Cloud Config).
- Externalized Configuration
- Store configuration outside the codebase.
- Distributed Cache
- Use caching layer shared across services.
- Anti-Corruption Layer (ACL)
- Shield microservices from legacy systems.