Grafana Dashboards & Visualization

What is Grafana?

Grafana is an open-source visualization platform that turns metrics data into beautiful, actionable dashboards.

Grafana + Prometheus Stack

┌──────────┐     ┌────────────┐     ┌─────────┐
│  Targets │────▶│ Prometheus │────▶│ Grafana │
│(exporters)│    │  (collect) │     │(visualize)│
└──────────┘     └────────────┘     └─────────┘

Installing Grafana

With Docker

docker run -d \
  --name grafana \
  -p 3000:3000 \
  -v grafana_data:/var/lib/grafana \
  -e "GF_SECURITY_ADMIN_PASSWORD=admin123" \
  grafana/grafana:latest

Docker Compose (Full Stack)

version: "3.8"
services:
  prometheus:
    image: prom/prometheus:latest
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
      - prometheus_data:/prometheus
 
  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=admin123
    volumes:
      - grafana_data:/var/lib/grafana
    depends_on:
      - prometheus
 
  node-exporter:
    image: quay.io/prometheus/node-exporter:latest
    ports:
      - "9100:9100"
    pid: host
    volumes:
      - "/:/host:ro,rslave"
    command:
      - "--path.rootfs=/host"
 
volumes:
  prometheus_data:
  grafana_data:

Connecting Prometheus

Open Grafana at http://localhost:3000
Login with admin / admin123
Navigate to Configuration → Data Sources
Click Add data source → Prometheus
Set URL: http://prometheus:9090
Click Save & Test

Building Dashboards

Panel Types

Panel	Best For
Time Series	Metrics over time (CPU, memory, requests)
Stat	Single value with thresholds
Gauge	Current value with min/max
Bar Chart	Comparisons across categories
Table	Detailed data with multiple columns
Heatmap	Distribution patterns over time
Logs	Log entries from Loki

Creating a Dashboard

Click + → Dashboard → Add visualization
Select Prometheus data source
Enter PromQL query
Configure visualization options
Set title and description
Save the dashboard

Example Panels

CPU Usage:

100 - (avg by(instance) (rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)

Memory Usage:

(node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes) / node_memory_MemTotal_bytes * 100

Disk I/O:

rate(node_disk_read_bytes_total[5m])
rate(node_disk_written_bytes_total[5m])

Network Traffic:

rate(node_network_receive_bytes_total{device="eth0"}[5m])
rate(node_network_transmit_bytes_total{device="eth0"}[5m])

HTTP Request Rate:

sum(rate(http_requests_total[5m])) by (status_code)

Error Rate:

sum(rate(http_requests_total{status_code=~"5.."}[5m])) / sum(rate(http_requests_total[5m])) * 100

Dashboard Variables

Variables make dashboards dynamic and reusable.

# Variable: instance
Type: Query
Query: label_values(node_cpu_seconds_total, instance)

# Use in panels:
node_memory_MemTotal_bytes{instance="$instance"}

Importing Community Dashboards

Grafana has thousands of pre-built dashboards:

Go to Dashboards → Import
Enter dashboard ID from grafana.com
Select data source
Click Import

Popular Dashboard IDs

ID	Dashboard	Purpose
1860	Node Exporter Full	System metrics
3662	Prometheus Stats	Prometheus self-monitoring
12740	Kubernetes Cluster	K8s overview
14031	Docker Monitoring	Container metrics

Alerting in Grafana

# Alert rule example
- alert: HighErrorRate
  condition: B
  data:
    - refId: A
      queryType: range
      expr: sum(rate(http_requests_total{status=~"5.."}[5m]))
    - refId: B
      queryType: classic_condition
      conditions:
        - evaluator:
            type: gt
            params: [0.05]
          reducer:
            type: last
  for: 5m
  annotations:
    summary: Error rate above 5%

Summary

You've learned:

Installing Grafana and connecting Prometheus
Building custom dashboards with various panel types
Writing PromQL queries for common metrics
Using variables for dynamic dashboards
Importing community dashboards
Setting up Grafana alerting

Next Steps

Next, we'll set up comprehensive alerting strategies and incident response workflows.

Learning Objectives