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How to Configure Grafana with Prometheus in 2026

18 minINTERMEDIATE
GrafanaPrometheusMonitoring
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Introduction

Grafana has become the go-to tool for metrics visualization in 2026. Paired with Prometheus, it enables real-time monitoring of complex infrastructures. This intermediate tutorial guides you step by step through deploying a complete stack, configuring automatic provisioning, and setting up alerts. You will learn to create reusable dashboards and avoid common production pitfalls.

Prerequisites

  • Docker and Docker Compose v2
  • Basic knowledge of Prometheus
  • Access to a Linux/macOS terminal
  • Understanding of YAML and JSON

Stack Deployment

docker-compose.yml
version: '3.8'
services:
  prometheus:
    image: prom/prometheus:latest
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
    ports:
      - "9090:9090"
  grafana:
    image: grafana/grafana:latest
    volumes:
      - grafana-storage:/var/lib/grafana
      - ./provisioning:/etc/grafana/provisioning
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=admin
volumes:
  grafana-storage:

This docker-compose file deploys Prometheus and Grafana with persistent volumes. The provisioning folder enables automatic configuration of datasources and dashboards at startup.

Prometheus Configuration

Prometheus collects the metrics. We will now create its main configuration to scrape targets.

Prometheus Configuration

prometheus.yml
global:
  scrape_interval: 15s
evaluation_interval: 15s
scrape_configs:
  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']
  - job_name: 'node-exporter'
    static_configs:
      - targets: ['node-exporter:9100']

This file defines the scraping jobs. The 15-second interval suits most intermediate use cases. Add your own targets according to your services.

Grafana Provisioning

Provisioning allows automatic configuration of datasources and dashboards without manual interface interaction.

Datasource Provisioning

provisioning/datasources/prometheus.yml
apiVersion: 1
datasources:
  - name: Prometheus
    type: prometheus
    access: proxy
    url: http://prometheus:9090
    isDefault: true
    editable: false

This YAML file is automatically loaded by Grafana at startup. It creates the Prometheus connection without manual intervention.

Complete Dashboard JSON

provisioning/dashboards/cpu-dashboard.json
{
  "dashboard": {
    "title": "CPU & Mémoire",
    "panels": [
      {
        "title": "CPU Usage",
        "type": "timeseries",
        "targets": [
          {
            "expr": "100 - (avg by (instance) (irate(node_cpu_seconds_total{mode=\"idle\"}[5m])) * 100)",
            "refId": "A"
          }
        ]
      }
    ],
    "time": {
      "from": "now-1h",
      "to": "now"
    }
  }
}

This JSON dashboard displays CPU usage. Place it in the provisioning/dashboards folder so it appears automatically in Grafana.

Alerting Rules

prometheus/rules.yml
groups:
  - name: cpu_alerts
    rules:
      - alert: HighCPUUsage
        expr: 100 - (avg by (instance) (irate(node_cpu_seconds_total{mode="idle"}[5m])) * 100) > 80
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "CPU élevée sur {{ $labels.instance }}"

These rules define an alert when CPU exceeds 80% for 5 minutes. Include this file in the Prometheus configuration to enable alerting.

Best Practices

  • Always use provisioning for reproducible environments
  • Limit permissions on shared dashboards
  • Enable OAuth or LDAP authentication in production
  • Store your JSON dashboards in Git
  • Test alerts with realistic thresholds

Common Errors to Avoid

  • Forgetting to mount the provisioning volume in Docker Compose
  • Using overly expensive PromQL expressions without aggregation
  • Not configuring Prometheus retention rules
  • Ignoring Grafana volume permissions (403 errors)

Going Further

Explore Grafana Enterprise plugins and integrations with Loki and Tempo. Join our advanced training at Learni Group.