goplt/docs/content/adr/0029-microservices-architecture.md

ADR-0029: Microservices Architecture

Status

Accepted

Context

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.

Decision

Design the platform as microservices architecture from day one:

1. Service-Based Architecture: All modules are independent services:

   • Each module is a separate service with its own process
   • Services communicate via gRPC (primary) or HTTP (fallback)
   • Service client interfaces for all inter-service communication
   • No direct in-process calls between services

2. Service Registry: Central registry for service discovery:

   • All services register on startup
   • Service discovery via registry
   • Health checking and automatic deregistration
   • Support for Consul, etcd, or Kubernetes service discovery

3. Communication Patterns:

   • Synchronous: gRPC service calls (primary), HTTP/REST (fallback)
   • Asynchronous: Event bus via Kafka
   • Shared State: Cache (Redis) and Database (PostgreSQL)

4. Service Boundaries: Each module is an independent service:

   • Independent Go modules (go.mod)
   • Own database schema (via Ent)
   • Own API routes
   • Own process and deployment
   • Can be scaled independently

5. Development Simplification: For local development, multiple services can run in the same process, but they still communicate via service clients (no direct calls)

Consequences

Positive

• Simplified Architecture: Single architecture pattern, no dual-mode complexity
• Independent Scaling: Scale individual services based on load
• Team Autonomy: Teams can own and deploy their services independently
• Technology Diversity: Different services can use different tech stacks (future)
• Fault Isolation: Failure in one service doesn't bring down entire platform
• Deployment Flexibility: Deploy services independently
• Clear Boundaries: Service boundaries are explicit from the start

Negative

• Network Latency: Inter-service calls have network overhead
• Distributed System Challenges: Need to handle network failures, retries, timeouts
• Service Discovery Overhead: Additional infrastructure needed
• Debugging Complexity: Distributed tracing becomes essential
• Data Consistency: Cross-service transactions become challenging
• Development Setup: More complex local development (multiple services)

Mitigations

• Service Mesh: Use service mesh (Istio, Linkerd) for advanced microservices features
• API Gateway: Central gateway for routing and cross-cutting concerns
• Event Sourcing: Use events for eventual consistency
• Circuit Breakers: Implement circuit breakers for resilience
• Comprehensive Observability: OpenTelemetry, metrics, logging essential
• Docker Compose: Simplify local development with docker-compose
• Development Mode: Run multiple services in same process for local dev (still use service clients)

Implementation Strategy

Phase 1: Service Client Interfaces (Phase 1)

• Define service client interfaces for all core services
• All inter-service communication goes through interfaces

Phase 2: Service Registry (Phase 3)

• Create service registry interface
• Implement service discovery
• Support for Consul, Kubernetes service discovery

Phase 3: gRPC Services (Phase 5)

• Implement gRPC service definitions
• Create gRPC servers for all services
• Create gRPC clients for service communication
• HTTP clients as fallback option

References

• Service Abstraction Pattern
• Service Discovery Patterns
• gRPC Documentation