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docs: sync guides to verified behavior
Rewrites AUTOSCALING.md — it was generic Cloudflare/Swarm advice that predated the platform autoscaler. Now documents the real /scaling API, per-service policy fields, manual scale, and the in-memory caveat. Adds OPERATIONS.md covering cron jobs, exec console, managed databases, preview environments (bookkeeping-only caveat), security scans (heuristic, not Trivy), HA, and node-agent onboarding. Authored By: TDvorak <[email protected]> Generated with [Devin](https://devin.ai) Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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@@ -1,288 +1,49 @@
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# Autoscaling with Cloudflare Tunnel
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# Autoscaling
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## Overview
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Containr has a built-in autoscaler that adjusts service replica counts against
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CPU/memory targets. Policies and scale events are held in backend memory —
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they do not survive a backend restart (tracked on the roadmap).
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This document explains how autoscaling works when using Cloudflare Tunnel with the Containr application.
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## Per-service policy
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## Architecture
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Open a service → **Scaling** section. Set:
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| Field | Meaning |
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|-------|---------|
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| Min replicas | Lower bound, ≥1 |
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| Max replicas | Upper bound |
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| Target CPU % | Scale up when average CPU exceeds this |
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| Target memory % | Scale up when average memory exceeds this |
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| Enabled | Whether the autoscaler evaluates this service |
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Creating a policy registers the service with the autoscaler and initializes
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its scaling state at `min_replicas`.
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## Manual scale
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The Scaling section also has a replica input + **Scale** button for an
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immediate change, clamped to the policy bounds when a policy exists
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(1–20 otherwise). Scaling dispatches to the node scheduler — without
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registered node agents it fails with `no ready nodes available`.
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## API
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```
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Internet → Cloudflare Edge → Cloudflare Tunnel → Traefik → Backend Services
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GET /api/v1/scaling/policies/:serviceId
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POST /api/v1/scaling/policies {service_id, min_replicas, max_replicas,
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target_cpu, target_memory, enabled}
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PUT /api/v1/scaling/policies/:serviceId
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DELETE /api/v1/scaling/policies/:serviceId (disables, does not remove)
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GET /api/v1/scaling/services/:serviceId (current/desired replicas)
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POST /api/v1/scaling/services/:serviceId/scale {replicas, reason}
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GET /api/v1/scaling/services/:serviceId/history
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GET /api/v1/scaling/status | /metrics | /events
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```
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## Autoscaling Considerations
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## Traffic distribution
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### 1. Cloudflare Tunnel Limitations
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**Cloudflare Tunnel itself does NOT provide autoscaling.** It's a secure tunneling service that:
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- Creates a persistent connection between your infrastructure and Cloudflare's edge
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- Routes traffic through Cloudflare's global network
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- Provides DDoS protection and CDN features
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### 2. Where Autoscaling Happens
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Autoscaling must be implemented at different layers:
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#### A. Container Level (Docker Swarm/Kubernetes)
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```yaml
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# Example with Docker Swarm
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backend:
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image: containr-backend
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deploy:
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replicas: 3
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update_config:
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parallelism: 1
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delay: 10s
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restart_policy:
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condition: on-failure
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```
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#### B. Application Level (Load Balancing)
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Traefik automatically load balances between multiple backend instances:
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```yaml
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# Multiple backend containers
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backend-1:
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# ... backend config
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labels:
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- "traefik.http.services.backend.loadbalancer.server.port=8080"
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backend-2:
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# ... backend config
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labels:
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- "traefik.http.services.backend.loadbalancer.server.port=8080"
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```
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#### C. Cloud Level (Cloudflare Load Balancer - Paid Feature)
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For true autoscaling, you'd need:
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- Multiple deployments in different regions
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- Cloudflare Load Balancer ($$$/month)
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- Health checks and failover
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## Implementation Options
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### Option 1: Docker Swarm (Recommended for Single Host)
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```bash
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# Initialize Docker Swarm
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docker swarm init
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# Deploy with autoscaling
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docker stack deploy -c docker-compose.yml containr
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```
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### Option 2: Kubernetes
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```yaml
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# deployment.yaml
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: backend
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spec:
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replicas: 3
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selector:
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matchLabels:
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app: backend
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template:
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metadata:
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labels:
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app: backend
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spec:
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containers:
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- name: backend
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image: containr-backend
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ports:
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- containerPort: 8080
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---
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apiVersion: autoscaling/v2
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kind: HorizontalPodAutoscaler
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metadata:
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name: backend-hpa
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spec:
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scaleTargetRef:
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apiVersion: apps/v1
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kind: Deployment
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name: backend
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minReplicas: 2
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maxReplicas: 10
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metrics:
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- type: Resource
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resource:
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name: cpu
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target:
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type: Utilization
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averageUtilization: 70
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```
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### Option 3: Manual Scaling with Scripts
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```bash
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#!/bin/bash
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# scale-backend.sh
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scale_up() {
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local current=$(docker ps --filter "name=containr-backend" --format "table {{.Names}}" | wc -l)
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local target=$((current + 1))
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echo "Scaling backend to $target instances..."
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for i in $(seq 1 $target); do
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docker run -d \
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--name containr-backend-$i \
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--network containr_containr-network \
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-e DATABASE_URL="..." \
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-e REDIS_URL="..." \
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containr-backend
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done
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}
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scale_down() {
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local current=$(docker ps --filter "name=containr-backend" --format "table {{.Names}}" | wc -l)
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local target=$((current - 1))
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if [ $target -lt 1 ]; then
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echo "Cannot scale below 1 instance"
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exit 1
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fi
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echo "Scaling backend to $target instances..."
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docker stop containr-backend-$target
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docker rm containr-backend-$target
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}
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case "$1" in
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up) scale_up ;;
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down) scale_down ;;
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*) echo "Usage: $0 [up|down]" ;;
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esac
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```
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## Monitoring and Metrics
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### Health Checks
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All services include health checks:
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```yaml
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healthcheck:
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test: ["CMD", "curl", "-f", "http://localhost:8080/health"]
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interval: 30s
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timeout: 10s
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retries: 3
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```
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### Metrics Collection
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Traefik provides Prometheus metrics:
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```yaml
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# In docker-compose.yml
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command:
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- "--metrics.prometheus=true"
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- "--metrics.prometheus.addentrypointslabels=true"
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- "--metrics.prometheus.addserviceslabels=true"
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```
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### Scaling Triggers
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Monitor these metrics for scaling decisions:
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- CPU usage (> 70%)
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- Memory usage (> 80%)
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- Response time (> 500ms)
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- Error rate (> 5%)
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- Queue depth (if using message queues)
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## Production Recommendations
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### 1. Use Docker Swarm or Kubernetes
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- Better orchestration
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- Built-in load balancing
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- Health management
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- Rolling updates
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### 2. Implement Horizontal Pod Autoscaler (HPA)
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- Automatic scaling based on metrics
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- Min/max replica limits
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- Configurable thresholds
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### 3. Use Cloudflare Load Balancer (if budget allows)
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- Geographic distribution
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- Advanced health checks
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- Traffic steering
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- DDoS protection
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### 4. Monitoring and Alerting
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- Prometheus + Grafana
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- Alertmanager
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- Log aggregation (ELK stack)
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## Example: Complete Autoscaling Setup
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```yaml
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# docker-compose.autoscale.yml
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version: '3.8'
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services:
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traefik:
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image: traefik:v3.2
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command:
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- "--api.dashboard=true"
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- "--providers.docker=true"
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- "--providers.docker.swarmMode=true"
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- "--metrics.prometheus=true"
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deploy:
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replicas: 1
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placement:
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constraints:
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- node.role == manager
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backend:
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image: containr-backend
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deploy:
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replicas: 3
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update_config:
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parallelism: 1
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delay: 10s
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restart_policy:
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condition: on-failure
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labels:
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- "traefik.http.services.backend.loadbalancer.server.port=8080"
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- "traefik.http.routers.backend.rule=Host(`api.${DOMAIN}`)"
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- "traefik.enable=true"
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prometheus:
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image: prom/prometheus
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deploy:
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replicas: 1
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volumes:
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- ./prometheus.yml:/etc/prometheus/prometheus.yml
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grafana:
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image: grafana/grafana
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deploy:
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replicas: 1
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environment:
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- GF_SECURITY_ADMIN_PASSWORD=admin
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```
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## Summary
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1. **Cloudflare Tunnel ≠ Autoscaling** - It's for secure connectivity
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2. **Autoscaling happens at container/orchestration level**
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3. **Traefik provides load balancing between instances**
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4. **Use Docker Swarm or Kubernetes for production autoscaling**
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5. **Monitor metrics and implement HPA for automatic scaling**
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6. **Consider Cloudflare Load Balancer for multi-region setups**
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## Quick Start Commands
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```bash
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# Start with autoscaling (Docker Swarm)
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docker swarm init
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docker stack deploy -c docker-compose.autoscale.yml containr
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# Scale manually
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docker service scale containr_backend=5
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# Check status
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docker service ls
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docker service ps containr_backend
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# View logs
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docker service logs containr_backend
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```
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Replicas are scheduled across registered node agents. In front of them you
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still want a load balancer — Traefik (bundled) discovers containers via
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labels, or Cloudflare Tunnel can front the platform for external traffic.
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Neither provides autoscaling itself; they distribute traffic to whatever
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replicas the autoscaler has placed.
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