improve frontend and celery task architecture

def/k8splan/envsink8s

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+You're absolutely right! In Kubernetes, environment variables are handled at the deployment level through ConfigMaps and Secrets, not
+  individual service .env files.
+
+  Kubernetes approach:
+  # k8s/configmap.yaml
+  apiVersion: v1
+  kind: ConfigMap
+  metadata:
+    name: media-analyzer-config
+  data:
+    DB_HOST: "postgres-service"
+    HLS_BASE_URL: "http://nginx-hls-service"
+
+  # k8s/deployment.yaml
+  env:
+  - name: DB_HOST
+    valueFrom:
+      configMapKeyRef:
+        name: media-analyzer-config
+        key: DB_HOST
+
+  Better structure:
+  - Development: Single .env for docker-compose
+  - Production: K8s ConfigMaps/Secrets per environment
+  - No service-specific .env files
+
+  Recommended approach:
+
+● Write(.env.development)
+  ⎿  Wrote 8 lines to .env.development                     
+     # Development environment variables for docker-compose
+     DB_HOST=postgres
+     DB_NAME=media_analyzer
+     DB_USER=media_user
+     DB_PASSWORD=media_pass
+     RTMP_PORT=1935
+     HLS_BASE_URL=http://localhost:8081
+     DEBUG=1
+
+● Then K8s handles it via:
+  - k8s/base/configmap.yaml - Non-secret config
+  - k8s/base/secret.yaml - Passwords, keys
+  - k8s/overlays/staging/ - Staging overrides
+  - k8s/overlays/production/ - Production overrides
+
+  This way each deployment environment controls its own config, not the services themselves.

def/k8splan/moving_to_k8s

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+> just keep k8s in mind but I think is better to dedicate a session for k8s. anything I should know now? I understand the concepts well and have some practical 
+  experience (on the job and running a cluster locally) but its been a while
+
+● Good to plan ahead! Here are the key things to keep in mind for the K8s session:
+
+  K8s-Specific Considerations for Your App
+
+  Stateful Components:
+  - PostgreSQL (needs StatefulSet + PVC)
+  - Redis (can be deployment but consider persistence)
+  - Media files storage (shared PVC across workers)
+
+  Service Architecture:
+  - Django ASGI (HTTP + WebSocket)
+  - Celery workers (separate deployment)
+  - Nginx (HLS file serving)
+  - External services (PostgreSQL, Redis)
+
+  Scaling Patterns:
+  - HPA for Celery workers based on queue depth
+  - Video processing is CPU/memory intensive
+  - WebSocket connections need session affinity
+
+  Storage:
+  - Shared volume for HLS segments (/media)
+  - Database persistence
+  - Consider GCP Cloud Storage integration
+
+  Networking:
+  - Ingress for external access
+  - Internal services communication
+  - WebSocket support in ingress
+
+  GCP Integration Points:
+  - GKE cluster
+  - Cloud Storage for media
+  - Cloud SQL (optional PostgreSQL upgrade)
+  - Container Registry for images
+
+  The current Docker Compose setup translates well to K8s, but you'll need persistent volumes and proper service discovery. Your video processing workload is perfect
+  for demonstrating K8s scaling capabilities.
+
+  We can tackle the architecture cleanup first, then dedicate a full session to the K8s deployment!