feat: microservice architecture
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docs/content/adr/0029-microservices-architecture.md
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docs/content/adr/0029-microservices-architecture.md
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# ADR-0029: Microservices Architecture
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## Status
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Accepted
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## Context
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The platform needs to scale independently, support team autonomy, and enable flexible deployment. A microservices architecture provides these benefits from day one, and the complexity of supporting both monolith and microservices modes is unnecessary.
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## Decision
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Design the platform as **microservices architecture from day one**:
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1. **Service-Based Architecture**: All modules are independent services:
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- Each module is a separate service with its own process
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- Services communicate via gRPC (primary) or HTTP (fallback)
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- Service client interfaces for all inter-service communication
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- No direct in-process calls between services
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2. **Service Registry**: Central registry for service discovery:
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- All services register on startup
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- Service discovery via registry
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- Health checking and automatic deregistration
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- Support for Consul, etcd, or Kubernetes service discovery
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3. **Communication Patterns**:
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- **Synchronous**: gRPC service calls (primary), HTTP/REST (fallback)
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- **Asynchronous**: Event bus via Kafka
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- **Shared State**: Cache (Redis) and Database (PostgreSQL)
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4. **Service Boundaries**: Each module is an independent service:
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- Independent Go modules (`go.mod`)
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- Own database schema (via Ent)
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- Own API routes
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- Own process and deployment
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- Can be scaled independently
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5. **Development Simplification**: For local development, multiple services can run in the same process, but they still communicate via service clients (no direct calls)
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## Consequences
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### Positive
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- **Simplified Architecture**: Single architecture pattern, no dual-mode complexity
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- **Independent Scaling**: Scale individual services based on load
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- **Team Autonomy**: Teams can own and deploy their services independently
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- **Technology Diversity**: Different services can use different tech stacks (future)
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- **Fault Isolation**: Failure in one service doesn't bring down entire platform
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- **Deployment Flexibility**: Deploy services independently
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- **Clear Boundaries**: Service boundaries are explicit from the start
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### Negative
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- **Network Latency**: Inter-service calls have network overhead
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- **Distributed System Challenges**: Need to handle network failures, retries, timeouts
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- **Service Discovery Overhead**: Additional infrastructure needed
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- **Debugging Complexity**: Distributed tracing becomes essential
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- **Data Consistency**: Cross-service transactions become challenging
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- **Development Setup**: More complex local development (multiple services)
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### Mitigations
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- **Service Mesh**: Use service mesh (Istio, Linkerd) for advanced microservices features
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- **API Gateway**: Central gateway for routing and cross-cutting concerns
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- **Event Sourcing**: Use events for eventual consistency
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- **Circuit Breakers**: Implement circuit breakers for resilience
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- **Comprehensive Observability**: OpenTelemetry, metrics, logging essential
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- **Docker Compose**: Simplify local development with docker-compose
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- **Development Mode**: Run multiple services in same process for local dev (still use service clients)
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## Implementation Strategy
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### Phase 1: Service Client Interfaces (Phase 1)
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- Define service client interfaces for all core services
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- All inter-service communication goes through interfaces
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### Phase 2: Service Registry (Phase 3)
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- Create service registry interface
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- Implement service discovery
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- Support for Consul, Kubernetes service discovery
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### Phase 3: gRPC Services (Phase 5)
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- Implement gRPC service definitions
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- Create gRPC servers for all services
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- Create gRPC clients for service communication
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- HTTP clients as fallback option
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## References
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- [Service Abstraction Pattern](https://microservices.io/patterns/data/service-per-database.html)
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- [Service Discovery Patterns](https://microservices.io/patterns/service-registry.html)
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- [gRPC Documentation](https://grpc.io/docs/)
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