Capstone — Full Monitoring Dashboard for Campus Library

Time for the real thing. This challenge chains everything you learned into one complete monitoring dashboard. Plan for about one hour.

Goal

By the end, you should have a Grafana dashboard with CPU, memory, request rate, and error panels — with variables, thresholds, and alert notifications.

Step 1 · The Stack

docker-compose.yml

services:
  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_USER=admin
      - GF_SECURITY_ADMIN_PASSWORD=admin
    volumes:
      - grafana-data:/var/lib/grafana
      - ./provisioning:/etc/grafana/provisioning

  prometheus:
    image: prom/prometheus:latest
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml

  node-exporter:
    image: prom/node-exporter:latest
    ports:
      - "9100:9100"
    volumes:
      - /proc:/host/proc:ro
      - /sys:/host/sys:ro
      - /:/rootfs:ro
    command:
      - '--path.procfs=/host/proc'
      - '--path.sysfs=/host/sys'
      - '--path.rootfs=/rootfs'

  app:
    image: nginx:alpine
    ports:
      - "8080:80"

volumes:
  grafana-data:

Step 2 · Prometheus Config

prometheus.yml

global:
  scrape_interval: 15s

scrape_configs:
  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']

  - job_name: 'node'
    static_configs:
      - targets: ['node-exporter:9100']

  - job_name: 'app'
    static_configs:
      - targets: ['app:80']

Step 3 · Auto-Provision Data Source

provisioning/datasources/prometheus.yml

apiVersion: 1

datasources:
  - name: Prometheus
    type: prometheus
    url: http://prometheus:9090
    access: proxy
    isDefault: true

Step 4 · Start and Build

Start everything

docker compose up -d

Open http://localhost:3000 → Create a new dashboard with these panels:

Row 1: Overview (Stat panels)

Panel	Query	Type
CPU Usage	100 - (avg(rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)	Gauge
Memory Usage	(1 - node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes) * 100	Gauge
Active Targets	count(up == 1)	Stat

Row 2: Traffic (Time series)

Panel	Query
Request Rate	sum(rate(http_requests_total[5m])) by (job)
Network Receive	rate(node_network_receive_bytes_total[device!="lo"][5m])

Row 3: Saturation (Time series)

Panel	Query
Memory Over Time	(1 - node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes) * 100
Disk Usage	(1 - node_filesystem_avail_bytes{mountpoint="/"} / node_filesystem_size_bytes{mountpoint="/"}) * 100

Step 5 · Add Variables

1. Dashboard Settings → Variables → Add variable
2. Name: job, Query: label_values(up, job)
3. Add to panel queries: rate(http_requests_total{job="$job"}[5m])

Step 6 · Add Alerts

1. Edit the CPU Usage panel → Alert tab → Create alert rule
2. Condition: CPU > 80% for 5 minutes
3. Notification: Send to Slack or email

Step 7 · Clean Up

Stop everything

docker compose down -v
rm -f docker-compose.yml prometheus.yml
rm -rf provisioning/

Remember

This capstone chained: data source → panels → queries → variables → thresholds → alerts. This is exactly how production dashboards are built.