jobtrackingapp/deploy

Production deployment notes

Gitea Actions

This repo includes .gitea/workflows/ci-deploy.yml for:

• backend build
• backend tests
• frontend tests
• frontend production build
• deployment to Ubuntu after successful tests on main

Required secrets in Gitea

• PROD_HOST
• PROD_USER
• PROD_SSH_KEY

Ubuntu server setup

Recommended app path:

• /opt/job-tracker/app

Persistent runtime secrets path:

• /opt/job-tracker/shared/.env

Requirements:

• Docker Engine
• Docker Compose plugin
• reverse proxy in front (Nginx, Caddy, or Traefik)
• shared env file present on server in /opt/job-tracker/shared/.env
• network connectivity from the backend container to your mariadb container/service

The deploy script will automatically create a symlink from:

• /opt/job-tracker/shared/.env

to:

• /opt/job-tracker/app/.env

This keeps secrets outside the uploaded repo checkout so they are not wiped by CI deploys.

Frontend API base URL

The production frontend already proxies /api to the backend container via Nginx.

Recommended default:

• leave REACT_APP_API_BASE_URL unset/empty in production

Only set REACT_APP_API_BASE_URL if the UI must call a different external API origin on purpose.

Example production .env

DATABASE_PROVIDER=mariadb
JOBTRACKER_CONNECTION_STRING=server=mariadb;port=3306;database=jobtracker;user=jobtracker;password=REPLACE_ME
AUTH_JWT_KEY=replace_with_long_random_secret
AUTH_ADMIN_EMAIL=you@example.com
AUTH_ADMIN_PASSWORD=replace_with_strong_password
APP_PUBLIC_BASE_URL=https://your-domain.example
AI_SERVICE_BASE_URL=http://ai-service:8001
OLLAMA_BASE_URL=http://ollama:11434
OLLAMA_MODEL=qwen2.5:7b
EMAIL_FOLLOWUPREMINDERS_ENABLED=true
EMAIL_FOLLOWUPREMINDERS_UPCOMINGDAYS=2
# Optional backward-compatible alias if older config still references the previous name:
SUMMARIZER_BASE_URL=http://ai-service:8001

Database recommendation

For production, yes — use a real database.

Recommended direction

Short term:

• SQLite is acceptable for a single-user or very small deployment
• keep backups and volume persistence

Better production choice:

• MariaDB or PostgreSQL

My recommendation

• PostgreSQL if you want the best long-term maintainability and fewer edge cases
• MariaDB is also fine if that is what you already know or host elsewhere

If you stay on SQLite:

• okay for small personal use
• not ideal for concurrent writes, larger scale, or operational robustness

Practical recommendation for this project

If this app is going to be a real production service on Ubuntu:

• move to PostgreSQL first if possible
• MariaDB is still a reasonable option if preferred

Deployment flow

1. push to main
2. Gitea Actions runs tests
3. if green, workflow uploads repo to server
4. deploy/deploy.sh links /opt/job-tracker/shared/.env into the repo checkout, then runs docker compose build && docker compose up -d
5. if OLLAMA_MODEL is set, the deploy script waits for Ollama, pulls the configured model if missing, then restarts ai-service so hybrid CV classification can use it
6. workflow checks service status after deployment

Post-deploy verification you should also do manually the first time

• confirm reverse proxy routes to the frontend correctly
• confirm API auth/login works with production config
• confirm backend can connect to MariaDB
• confirm AI service container is reachable from backend
• confirm reminder and admin/system pages load
• verify follow-up reminder emails are enabled only when intended and that links open the correct job/tab hat links open the correct job/tab