 # Scaling and Load Balancing
 
 ## Purpose
 Outline how services can be scaled based on existing Docker/Gunicorn/Celery configuration and external dependencies.
 
 ## Code Locations (exact paths)
 - Compose stack: `docker-compose.app.yml`
 - Backend process model: `docker-compose.app.yml` (Gunicorn command), `backend/Dockerfile`
 - Celery worker/beat commands: `docker-compose.app.yml`
 - Celery settings: `backend/igny8_core/settings.py` (Celery config)
 
 ## High-Level Responsibilities
 - Describe horizontal/vertical scaling levers for backend and workers.
 - Note reliance on external Postgres/Redis and shared network.
 
 ## Detailed Behavior
 - Backend container runs Gunicorn with `--workers 4 --timeout 120` (from compose). Scaling options:
   - Increase workers via compose command args.
   - Run additional backend containers on the same `igny8_net` behind a reverse proxy (proxy not defined here; compose assumes external Caddy/infra stack handles routing).
 - Celery:
   - Worker command `celery -A igny8_core worker --loglevel=info --concurrency=4`; concurrency can be increased per container or by adding more worker replicas.
   - Beat runs separately to schedule tasks.
   - Broker/backend: Redis from external infra; settings enforce prefetch multiplier 1 and task soft/hard time limits (25/30 min).
 - Frontend/marketing/sites:
   - Served via dev servers in current compose; for production, build static assets (frontend Dockerfile uses Caddy). Can scale by running additional containers behind the proxy.
 - Network:
   - `igny8_net` is external; load balancer (e.g., Caddy/infra) not defined in this repo but must front multiple backend/frontend replicas if added.
 
 ## Data Structures / Models Involved (no code)
 - None; operational scaling only.
 
 ## Execution Flow
 - Scaling out: start additional containers with the same images on `igny8_net`; configure external proxy to round-robin to backend instances.
 - Scaling up: raise Gunicorn worker count or Celery concurrency in compose overrides.
 
 ## Cross-Module Interactions
 - Celery workload includes automation/AI tasks; ensure Redis/Postgres sized accordingly when increasing concurrency.
 - Request ID and resource tracking middleware remain per-instance; logs aggregate by container.
 
 ## State Transitions (if applicable)
 - New replicas join network immediately; no shared session storage configured (stateless JWT APIs), so backend replicas are safe behind load balancer.
 
 ## Error Handling
 - If backend not reachable, healthcheck fails and depends_on blocks frontend start.
 - Celery tasks exceeding time limits are terminated per settings.
 
 ## Tenancy Rules
 - Unchanged by scaling; tenancy enforced per request in each instance.
 
 ## Billing Rules (if applicable)
 - None; credit deductions occur in application code regardless of scale.
 
 ## Background Tasks / Schedulers (if applicable)
 - Celery beat should remain single active scheduler; if running multiple beats, use external lock (not implemented) to avoid duplicate schedules.
 
 ## Key Design Considerations
 - Ensure reverse proxy/ingress (outside this repo) balances across backend replicas and terminates TLS.
 - Keep Redis/Postgres highly available and sized for additional connections when scaling workers/backends.
 
 ## How Developers Should Work With This Module
 - Use compose override or Portainer to adjust worker counts; validate resource limits.
 - Avoid multiple Celery beat instances unless coordination is added.
 - When introducing production-ready load balancing, add proxy config to infra repo and keep ports consistent with compose.
