System Design & Architecture
Dokumentasi lengkap arsitektur sistem MStore Backend dari high-level design hingga implementation details.π― Architecture Principles
MStore Backend dibangun dengan prinsip:- β Clean Architecture: Separation of concerns (handler β service β repository)
- β Domain-Driven Design: Business logic organized by domain
- β Multi-Tenant: Merchant-scoped data isolation
- β Microservices-Ready: Modular design untuk future decomposition
- β Event-Driven: Message broker integration (NATS, RabbitMQ, Kafka)
- β API-First: RESTful API dengan OpenAPI/Swagger documentation
- β Observability: LGTM stack (Loki, Grafana, Tempo, Mimir)
ποΈ High-Level Architecture
π¦ Clean Architecture Layers
1. Handler Layer (Presentation)
Responsibility: HTTP request/response handling, validation, authentication2. Service Layer (Business Logic)
Responsibility: Business rules, orchestration, transaction management3. Repository Layer (Data Access)
Responsibility: Database operations, query buildingπ Domain Organization
π Request Flow
Typical API Request Flow
ποΈ Database Architecture
Multi-Database Strategy
MySQL (Primary - Transactional)- Transactions
- Inventory & Warehouse
- Approvals
- Accounting & Journals
- Master data (Products, Branches, Users)
- POS offline transactions (temporary)
- Audit logs
- Analytics data
- Unstructured documents
- Session storage
- API rate limiting
- Real-time data cache
- Pub/Sub for real-time updates
Multi-Tenant Data Isolation
π Security Architecture
Authentication Flow
Authorization (RBAC)
π‘ Event-Driven Architecture
Message Broker Integration
π Observability Architecture
LGTM Stack Integration
Loki (Logs)- Transaction volume & revenue
- API latency & error rate
- Database query performance
- Cache hit rate
- Message broker throughput
π Deployment Architecture
Development Environment
Production Architecture
π Performance Characteristics
Target Metrics
| Metric | Target | Notes |
|---|---|---|
| API Latency (P95) | < 100ms | Excluding external API calls |
| API Latency (P99) | < 500ms | Including database queries |
| Throughput | 10k req/s | Per instance |
| Database Query | < 50ms | P95 for simple queries |
| Cache Hit Rate | > 80% | Redis cache |
| Availability | 99.9% | ~8.7 hours downtime/year |
| Error Rate | < 0.1% | 4xx + 5xx errors |
Scalability Strategy
Horizontal Scaling- Stateless application servers
- Load balancer distribution
- Database read replicas
- Redis cluster for cache
- Database server resources
- Cache memory allocation
- Message broker capacity
- L1: In-memory cache (per instance)
- L2: Redis cluster (shared)
- Cache invalidation via events
π‘ Best Practices
DO β
- Follow clean architecture layers
- Use dependency injection
- Implement proper error handling
- Log with structured logging (Zap)
- Use context for cancellation & timeout
- Implement circuit breaker for external APIs
- Use database transactions for atomic operations
- Implement idempotency for critical operations
- Monitor with OpenTelemetry
- Document APIs with Swagger
DONβT β
- Jangan skip input validation
- Jangan hardcode credentials
- Jangan ignore context cancellation
- Jangan block goroutines indefinitely
- Jangan skip error logging
- Jangan expose internal errors ke client
- Jangan skip database indexes
- Jangan ignore connection pooling
π Migration Strategy
Microservices Decomposition (Future)
Decomposition Strategy:- Start with bounded contexts (domains)
- Extract stateless services first
- Implement API gateway
- Use event bus for inter-service communication
- Gradual migration per domain
π Related Documentation
Tech Stack
Library dan tools yang digunakan
Service Template
Template untuk membuat service baru
API Styleguide
Panduan API design & conventions
Database Schema
ERD lengkap database
Observability
LGTM stack setup & monitoring
Need Help? Contact backend team atau check GitHub Issues