BanGUI/Docs/Deployment.md

Deployment Guide

Health Checks

The backend container includes a health check endpoint at GET /api/health that reports application and fail2ban daemon status:

• HTTP 200 with {"status": "ok", "fail2ban": "online"} — backend is healthy and fail2ban is reachable
• HTTP 503 with {"status": "unavailable", "fail2ban": "offline"} — fail2ban is unreachable (backend will restart)

Docker Health Check:

The Dockerfile includes a HEALTHCHECK that queries the endpoint. Docker interprets HTTP 503 as unhealthy and restarts the container after 3 consecutive failures (90 seconds by default).

Why 503 for offline fail2ban?

If fail2ban goes offline but the backend always returns 200, Docker treats the container as healthy. This can mask infrastructure failures. By returning 503 when fail2ban is unreachable, orchestration tools (Docker, Kubernetes, Docker Swarm) will automatically restart the backend container until fail2ban recovers.

---

CORS Configuration

Cross-Origin Resource Sharing (CORS) must be explicitly configured when the frontend and backend are served from different origins.

Development

By default, the backend allows requests from common localhost development origins:

• http://localhost:5173
• http://127.0.0.1:5173
• https://localhost:5173
• https://127.0.0.1:5173

No additional configuration is needed for local development — just run the frontend and backend normally.

Production

In production, override the default with your actual frontend origin(s):

Docker Compose:

environment:
  BANGUI_CORS_ALLOWED_ORIGINS: "https://example.com,https://www.example.com"

Environment File (.env):

BANGUI_CORS_ALLOWED_ORIGINS=https://example.com,https://www.example.com

Multiple Origins: Separate multiple allowed origins with commas (no spaces):

BANGUI_CORS_ALLOWED_ORIGINS=https://example.com,https://app.example.com,https://admin.example.com

Disable CORS: To disable CORS entirely (e.g., when the frontend is served from the same origin as the backend):

BANGUI_CORS_ALLOWED_ORIGINS=

Security Considerations

• Always specify exact origins — never use wildcard * in production, especially with allow_credentials=true (credentials mode is required for the session cookie).
• Use HTTPS in production — the backend enforces the Secure cookie flag, which requires HTTPS (or localhost for development).
• Validate in reverse proxy — if using Nginx or a CDN reverse proxy, validate the Origin header before forwarding requests to ensure only legitimate origins reach the backend.

Troubleshooting

Symptom	Cause	Solution
Access-Control-Allow-Origin header missing from response	CORS not configured or origin not whitelisted	Check BANGUI_CORS_ALLOWED_ORIGINS and ensure your frontend origin is included
Browser blocks requests with CORS error	Credentials mode enabled but origin not exactly whitelisted	Ensure BANGUI_CORS_ALLOWED_ORIGINS includes the exact origin (protocol + domain + port) of your frontend
Works in development but fails in production	Default localhost origins used instead of production frontend domain	Override BANGUI_CORS_ALLOWED_ORIGINS in production environment

---

In multi-instance deployments (e.g., Kubernetes, Docker Swarm), the scheduler lock prevents duplicate execution of background tasks by ensuring only one instance runs the scheduler at a time.

How It Works

The lock is stored in the SQLite database and enforced via:

1. Lock Acquisition — At startup, each instance tries to insert a lock record. Only one succeeds; others reject startup with a clear error message.
2. Heartbeat — The lock-holding instance sends a heartbeat every 5 seconds to prove it's still alive.
3. Stale Lock Cleanup — On startup, any lock older than 60 seconds (without a heartbeat) is automatically deleted, allowing recovery from instance crashes.

Configuration

Parameter	Value	Rationale
Heartbeat Interval	5 seconds	Allows ~12 missed heartbeats before lock expires
Lock TTL	60 seconds	Time before a lock without heartbeat is considered abandoned
Min Safe Ratio	12x (TTL / interval)	Robust protection against temporary delays or high load

With a 60-second TTL and 5-second heartbeat interval, the lock survives even if the instance becomes unresponsive for up to ~55 seconds. This provides strong protection against false positives while still detecting genuine crashes.

Monitoring

Check logs for these key events:

• scheduler_lock_acquired — Lock successfully acquired at startup (INFO)
• scheduler_lock_heartbeat_updated — Heartbeat successfully updated (DEBUG)
• scheduler_lock_heartbeat_failed — Heartbeat update failed; lock may be lost (WARNING)
• scheduler_lock_heartbeat_timeout — Heartbeat exceeded 5-second timeout (ERROR)
• scheduler_lock_held_by_other_instance — Another instance holds the lock (WARNING at startup)

Troubleshooting: "Blocklist import runs twice"

Symptom: Blocklist import task executes simultaneously in two instances, causing duplicate entries or data corruption.

Cause: The scheduler lock was released prematurely (e.g., instance crash, database timeout) while a task was still running.

Solution:

1. Check heartbeat timing — Ensure the instance isn't hanging for >60 seconds (monitor CPU/memory/disk).
2. Verify database health — Run SELECT * FROM scheduler_lock; to see if a stale lock exists. If present, delete it: DELETE FROM scheduler_lock;
3. Review logs — Look for scheduler_lock_heartbeat_failed or scheduler_lock_heartbeat_timeout errors in the time window when duplication occurred.
4. Increase resource limits — If the backend is memory/CPU constrained, increase limits in docker-compose.yml to prevent slowdowns that trigger false lock timeouts.
5. Check database performance — Slow database queries can delay heartbeat updates. Run PRAGMA integrity_check; to check for corruption.

If duplication occurs frequently, consider migrating to Redis-backed locking (see Advanced section below) for higher reliability.

Advanced: Migrating to Redis

For very high-traffic deployments with strict data consistency requirements, you can replace the SQLite-backed lock with Redis:

• Why: Redis is single-threaded and atomic by design; clock skew and timeout issues are eliminated.
• How: Install redlock-py or aioredis, replace scheduler_lock.py with a Redis implementation, update heartbeat interval to 2-3 seconds.
• Trade-off: Adds a Redis dependency but eliminates database lock contention and provides microsecond-precision atomicity.

This is not required for typical deployments but is recommended if you see frequent scheduler conflicts in logs.

---

All containers have hard limits (max usage) and soft reservations (guaranteed allocation). This ensures:

• Isolation: A misbehaving container cannot crash others or the host
• Predictability: Reservations guarantee minimum resources even under load
• Efficiency: Unused reserved capacity can be borrowed by other containers

Container Resource Limits

Container	Limit CPU	Limit Memory	Reserved CPU	Reserved Memory	Purpose
fail2ban	0.5	128M	0.1	64M	Monitors logs, bans IPs—typically idle
backend	2.0	512M	1.0	256M	Core app: database, fail2ban API, config management
frontend	0.5	128M	0.25	64M	Nginx: serves SPA + API proxy

Rationale

• fail2ban: Lightweight log monitoring. Occasionally CPU spikes during ban processing but memory usage is minimal.
• backend: Heavy lifting—Python runtime, SQLite database, background jobs. May need extra memory for large blocklists. Reservation of 1.0 CPU ensures responsive API even when frontend is busy.
• frontend: Nginx is efficient. Limit of 0.5 CPU and 128M memory is more than sufficient for reverse proxy duties.

---

Memory Considerations

Backend Memory Requirements

The backend typically runs in 256–512M under normal load. Memory usage depends on:

• Blocklist size: Large blocklists (>1M entries) require more heap space
• Cache warmth: First query after startup may require more memory as caches fill
• Concurrent connections: Each active user session uses a small amount of memory

Tuning: If you see OOM kills in logs, increase backend limits and reservations (e.g., 1024M limit). Test under realistic load before finalizing.

Frontend Memory Usage

Nginx is typically <50M. If you see memory pressure on frontend, check for:

• Misconfigured cache headers on static assets
• Large log volumes (nginx access logs)

---

Docker Swarm & Kubernetes

For production deployments using orchestration platforms:

Docker Swarm

The deploy sections in docker-compose.yml are compatible with docker stack deploy:

docker stack deploy -c Docker/docker-compose.yml bangui

Swarm respects the same limits and reservations fields.

Kubernetes

For Kubernetes, translate resource constraints to equivalent resources fields in your deployment manifests:

containers:
  - name: backend
    image: git.lpl-mind.de/lukas.pupkalipinski/bangui/backend:latest
    resources:
      limits:
        cpu: "2"
        memory: "512Mi"
      requests:
        cpu: "1"
        memory: "256Mi"

Kubernetes equivalent mappings:

• Docker deploy.limits → Kubernetes resources.limits
• Docker deploy.reservations → Kubernetes resources.requests

---

Monitoring Resource Usage

Docker Compose (Development)

docker stats

Shows real-time CPU and memory usage for all running containers.

Production (Docker Swarm / Kubernetes)

Use native monitoring:

• Docker Swarm: Prometheus + Grafana
• Kubernetes: Metrics Server + dashboard or Prometheus

---

Environment Variables

Resource limits are configured in Docker/docker-compose.yml and cannot be overridden via environment variables. To adjust limits:

1. Edit Docker/docker-compose.yml
2. Modify the deploy.limits and deploy.reservations sections
3. Restart containers: make down && make up

---

Troubleshooting

Issue	Symptom	Solution
Backend OOM kills	"Exit code 137" in logs	Increase backend memory limit
Throttling	CPU at 100%, requests slow	Increase CPU limit or optimize code
Service startup timeout	Containers not becoming "healthy"	Increase reservation to guarantee capacity at startup
Host unresponsive	System-wide lag	Reduce container limits to prevent host starvation

---

Next Steps

• Development: Run make up to start with default limits
• Staging: Test with realistic data volumes and monitor resource usage
• Production: Adjust limits based on observed usage patterns, then commit changes