Sagas

Microservices architecture patterns and tradeoffs Microservices change how we design, deploy, and run software. Patterns help solve common problems, but every choice brings tradeoffs. The goal is to fit patterns to real needs, not to copy a blueprint. Patterns to consider API gateway and edge routing: a single entry point handles auth, rate limits, and routing. Pros: simpler client calls, centralized security. Cons: it can become a bottleneck or a single point of failure if not duplicated for reliability. Service registry and discovery: services find peers without hard links. Pros: flexible deployment; cons: the registry itself must be reliable and synchronized. Database per service and data ownership: each service owns its data for autonomy. Pros: clear boundaries and easier scaling. Cons: cross-service queries are harder and may need data duplication. Event-driven messaging: services publish and react to events. Pros: loose coupling and resilience. Cons: eventual consistency, harder debugging. Saga pattern for distributed transactions: long workflows use compensating actions to maintain consistency. Pros: avoids locking. Cons: complex error handling and longer execution paths. API composition and Backend-for-Frontend: the API layer stitches data from several services. Pros: faster reading, tailored responses. Cons: more work for data duplication and potential latency. Orchestration vs choreography: central control versus event-led coordination. Pros: orchestration is easy to reason about; choreography scales but can be harder to track. Service mesh: built-in observability, security, and traffic control. Pros: visibility and resilience; Cons: adds operational overhead. CQRS and read models: separate paths for reads and writes. Pros: fast queries; Cons: dual models and eventual consistency. Serverless or container-based deployment: keeps resources matched to demand. Pros: cost efficiency; Cons: cold starts, vendor lock-in. A practical tip Start small with one or two patterns on a new service. Use clear boundaries, shared standards, and strong monitoring. Build an internal guide for tracing requests across services. In a simple online store, for example, inventory and payments can react to order events while a read model serves quick queries to the storefront. ...

Microservices Design and Orchestration Microservices design asks you to split a large app into small, independently deployable services. Each service owns its data and its API. This helps teams move faster, scale parts of the system, and recover from failures more easily. Start with a clear domain model and a bounded context for each service. Define stable interfaces and lightweight contracts so teams can evolve without breaking others. Boundaries matter. A service should own a business capability, not a technical layer. Favor autonomy: each service manages its own database or data store and communicates over well defined APIs. Keep interfaces small and readable. If a change grows a service’s surface, consider splitting or rethinking the boundary. ...

Databases for Microservices: Patterns and Pitfalls Microservices split an application into small, independently deployable parts. Each service often owns its own data store. This reduces coupling, but it also creates new data challenges. You must think about consistency, migrations, and how to surface data to clients across services. The right choices depend on your domain, team, and tolerance for latency. Patterns to consider Database per service: Each microservice manages its own database, aligned to its boundaries. This reduces cross-service coupling but requires clear data ownership and disciplined migrations. Shared database with careful governance: A single database can simplify some reads but creates tight coupling and coordination needs between teams. Event-driven integration: Services publish events when data changes. Other services react, update read models, and keep data eventually consistent. Saga pattern: Long-running, multi-service workflows use compensating actions to handle failures, avoiding distributed transactions. CQRS and read models: Separate write and read paths let you scale reads and tailor models to each consumer. Polyglot persistence: Different databases suit different workloads. A service might use a document store for catalogs and a relational store for payments. Pitfalls to watch ...

Middleware Patterns that Tie Modern Systems Together Middleware is the glue that helps services talk to each other without exposing every internal detail. A good pattern reduces latency, helps teams scale features, and keeps systems resilient as they grow. In this post, we explore practical middleware patterns you can apply today. You’ll see options that work well on their own and in combination, depending on data needs, latency goals, and team capability. ...