Saga

Middleware Patterns for Microservices Middleware patterns help microservices communicate reliably, safely, and at scale. They sit between services, enabling routing, transformation, security, and resilience without changing business logic. The right mix of patterns reduces latency, avoids tight coupling, and makes systems easier to evolve. Choosing patterns isn’t a solo move. It involves tradeoffs in latency, consistency, and failure modes. Start with the core needs: who talks to whom, how failures should behave, and how data stays consistent across services. ...

Microservices Architecture: Patterns and Pitfalls Microservices can unlock agility. But they also add complexity to how teams design, test, and operate software. This article looks at common patterns and the pitfalls to avoid. It stays practical and avoids hype, so you can compare options for real projects. One core pattern is to decompose by business capability. Each service owns its own data and has a clear responsibility. This helps teams move faster and reduces cross‑team conflicts. An API gateway can present a clean, stable surface for clients while services evolve behind it. A service mesh can handle traffic between services with less code in each service. ...

Microservices Architecture Patterns Microservices split a monolith into smaller, independent services. Each service owns its code, data, and deployment. Patterns help teams decide how services talk, how data stays consistent, and how to scale without causing outages. In practice, you pick patterns that fit your domain, team size, and tech choices. Decomposition patterns Domain segmentation: group services by business capability or domain to align with teams. Data ownership: give each service its own data store to reduce coupling. Strangler approach: gradually replace parts of a legacy system with new microservices. Communication patterns API gateway: a single entry point to route requests, enforce auth, and simplify clients. Service mesh: handles service-to-service communication with security and reliability features. Asynchronous messaging: events reduce direct dependencies and enable loose coupling. Data management patterns Database per service: each service controls its own data, improving autonomy. Event sourcing: record changes as a sequence of events for auditability. Saga pattern: coordinate long transactions with compensating actions to maintain consistency. Resilience and reliability Circuit breaker: stop calls to a failing service to avoid cascading failures. Bulkhead isolation: separate resource pools to limit impact of faults. Timeouts and retries: balanced settings prevent stalls and overloading. Deployment patterns Strangler pattern: migrate functionality gradually, reducing risk. Blue-green and canary: safe releases with quick rollback options. Observability and governance Tracing, logs, and metrics: end-to-end visibility helps diagnose issues fast. Versioned contracts: stable API schemas reduce breaking changes across services. Example: an online store An Order service emits an OrderCreated event. The Catalog and Inventory services react, update stock, and publish events in return. A Saga coordinates steps and issues compensating actions if something goes wrong, keeping the system in a consistent state. ...