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turash/docs/concept/17_monitoring_observability.md
Damir Mukimov 000eab4740
Major repository reorganization and missing backend endpoints implementation 
Repository Structure:
- Move files from cluttered root directory into organized structure
- Create archive/ for archived data and scraper results
- Create bugulma/ for the complete application (frontend + backend)
- Create data/ for sample datasets and reference materials
- Create docs/ for comprehensive documentation structure
- Create scripts/ for utility scripts and API tools

Backend Implementation:
- Implement 3 missing backend endpoints identified in gap analysis:
  * GET /api/v1/organizations/{id}/matching/direct - Direct symbiosis matches
  * GET /api/v1/users/me/organizations - User organizations
  * POST /api/v1/proposals/{id}/status - Update proposal status
- Add complete proposal domain model, repository, and service layers
- Create database migration for proposals table
- Fix CLI server command registration issue

API Documentation:
- Add comprehensive proposals.md API documentation
- Update README.md with Users and Proposals API sections
- Document all request/response formats, error codes, and business rules

Code Quality:
- Follow existing Go backend architecture patterns
- Add proper error handling and validation
- Match frontend expected response schemas
- Maintain clean separation of concerns (handler -> service -> repository)
2025-11-25 06:01:16 +01:00

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15. Monitoring & Observability

Recommendation: Comprehensive observability from day one.

Metrics to Track

Business Metrics (Daily/Monthly Dashboard):

  • Active businesses: 500+ (Year 1), 2,000+ (Year 2), 5,000+ (Year 3)
  • Sites & resource flows: 85% data completion rate target
  • Match rate: 60% conversion from suggested to implemented matches
  • Average savings: €25,000 per implemented connection
  • Platform adoption: 15-20% free-to-paid conversion rate

Technical Metrics (Real-time Monitoring):

  • API response times: p50 <500ms, p95 <2s, p99 <5s
  • Graph query performance: <1s for 95% of queries
  • Match computation latency: <30s for complex optimizations
  • Error rates: <1% API errors, <0.1% critical errors
  • Database connection pool: 70-90% utilization target
  • Cache hit rates: >85% Redis hit rate, >95% application cache
  • Uptime: >99.5% availability target

Domain-Specific Metrics:

  • Matching accuracy: >90% user satisfaction with match quality
  • Economic calculation precision: ±€100 accuracy on savings estimates
  • Geospatial accuracy: <100m error on location-based matching
  • Real-time updates: <5s delay for new resource notifications

Alerting

Critical Alerts:

  • API error rate > 1%
  • Database connection failures
  • Match computation failures
  • Cache unavailable

Warning Alerts:

  • High latency (p95 > 2s)
  • Low cache hit rate (< 70%)
  • Disk space low

Tools:

  • Prometheus: Metrics collection
  • Grafana: Visualization and dashboards
  • AlertManager: Alert routing and notification
  • Loki or ELK: Logging (Elasticsearch, Logstash, Kibana)
  • Jaeger or Zipkin: Distributed tracing
  • Sentry: Error tracking

Observability Tools

  • Metrics: Prometheus + Grafana
  • Logging: Loki or ELK stack
  • Tracing: Jaeger or Zipkin for distributed tracing
  • APM: Sentry for error tracking
  • OpenTelemetry: go.opentelemetry.io/otel for instrumentation