name: architecture-paradigm-service-based description: | Coarse-grained service architecture for deployment independence without microservices complexity.

Triggers: service-based, SOA, coarse-grained services, domain services Use when: teams need deployment independence without microservices complexity DO NOT use when: fine-grained scaling needed - use microservices. version: 1.0.0 category: architectural-pattern tags: [architecture, service-based, soa, modular, shared-database] dependencies: [] tools: [api-gateway, service-registry, schema-management] usage_patterns:

• paradigm-implementation
• monolith-refactoring
• deployment-independence complexity: medium estimated_tokens: 700

The Service-Based Architecture Paradigm

When to Employ This Paradigm

• When teams require a degree of deployment independence but are not yet prepared for the complexity of managing numerous microservices.
• When shared databases or large-scale systems (like ERPs) make full service autonomy unrealistic.
• When establishing clear service contracts for partner teams or external consumers.

Adoption Steps

1. Group Capabilities: Bundle related business functions into a small set of well-defined services, each with a designated owner.
2. Define Service Contracts: Publish formal specifications using standards like OpenAPI or AsyncAPI, including Service Level Agreements (SLAs) and a clear versioning strategy.
3. Control Database Schemas: Even when services share a database, assign explicit ownership for each schema or table. Gate all breaking changes through a formal review process.
4. Establish Service Mediation: Use a service registry or an API gateway to handle routing, authentication, and observability.
5. Plan for Evolution: Identify architectural "hotspots" that are likely candidates for being split into more granular services in the future.

Key Deliverables

• An Architecture Decision Record (ADR) that outlines service boundaries, data ownership rules, and coordination mechanisms.
• A suite of contract tests and consumer-driven contract tests for each service to validate stability.
• Runbooks that describe deployment procedures, rollback plans, and service dependencies.

Risks & Mitigations

• Coupling Through a Shared Database:
  • Mitigation: Changes to a shared database can have cascading effects across services. Mitigate this by using database views, replication, or a formal schema deprecation schedule to manage change.
• Architectural Degradation:
  • Mitigation: Without strong governance, this architecture can degrade into a "distributed monolith"—a monolith with the added complexity of network hops. Track coupling metrics closely and enforce strict ownership of services and data to prevent this.