---

name: grafana-dashboard description: Create professional Grafana dashboards with visualizations, templating, and alerts. Use when building monitoring dashboards, creating data visualizations, or setting up operational insights.

Grafana Dashboard

Overview

Design and implement comprehensive Grafana dashboards with multiple visualization types, variables, and drill-down capabilities for operational monitoring.

When to Use

• Creating monitoring dashboards
• Building operational insights
• Visualizing time-series data
• Creating drill-down dashboards
• Sharing metrics with stakeholders

Instructions

1. Grafana Dashboard JSON

{
  "dashboard": {
    "title": "Application Performance",
    "description": "Real-time application metrics",
    "tags": ["production", "performance"],
    "timezone": "UTC",
    "refresh": "30s",
    "templating": {
      "list": [
        {
          "name": "datasource",
          "type": "datasource",
          "datasource": "prometheus"
        },
        {
          "name": "service",
          "type": "query",
          "datasource": "prometheus",
          "query": "label_values(requests_total, service)"
        }
      ]
    },
    "panels": [
      {
        "id": 1,
        "title": "Request Rate",
        "type": "graph",
        "gridPos": {"x": 0, "y": 0, "w": 12, "h": 8},
        "targets": [
          {
            "expr": "sum(rate(requests_total{service=\"$service\"}[5m]))",
            "legendFormat": "{{ method }}"
          }
        ],
        "yaxes": [
          {
            "format": "rps",
            "label": "Requests per Second"
          }
        ]
      },
      {
        "id": 2,
        "title": "Error Rate",
        "type": "graph",
        "gridPos": {"x": 12, "y": 0, "w": 12, "h": 8},
        "targets": [
          {
            "expr": "sum(rate(requests_total{status_code=~\"5..\",service=\"$service\"}[5m])) / sum(rate(requests_total{service=\"$service\"}[5m]))",
            "legendFormat": "Error Rate"
          }
        ]
      },
      {
        "id": 3,
        "title": "Response Latency (p95)",
        "type": "graph",
        "gridPos": {"x": 0, "y": 8, "w": 12, "h": 8},
        "targets": [
          {
            "expr": "histogram_quantile(0.95, rate(request_duration_seconds_bucket{service=\"$service\"}[5m]))",
            "legendFormat": "p95"
          }
        ]
      },
      {
        "id": 4,
        "title": "Active Connections",
        "type": "stat",
        "gridPos": {"x": 12, "y": 8, "w": 12, "h": 8},
        "targets": [
          {
            "expr": "sum(active_connections{service=\"$service\"})"
          }
        ]
      }
    ]
  }
}

2. Grafana Provisioning Configuration

# /etc/grafana/provisioning/dashboards/dashboards.yaml
apiVersion: 1

providers:
  - name: 'Dashboards'
    orgId: 1
    folder: 'Production'
    type: file
    disableDeletion: false
    updateIntervalSeconds: 10
    options:
      path: /var/lib/grafana/dashboards

# /etc/grafana/provisioning/datasources/prometheus.yaml
apiVersion: 1

datasources:
  - name: Prometheus
    type: prometheus
    access: proxy
    orgId: 1
    url: http://prometheus:9090
    isDefault: true
    editable: true
    jsonData:
      timeInterval: '30s'

3. Grafana Alert Configuration

# /etc/grafana/provisioning/alerting/alerts.yaml
groups:
  - name: application_alerts
    interval: 1m
    rules:
      - uid: alert_high_error_rate
        title: High Error Rate
        condition: B
        data:
          - refId: A
            model:
              expr: 'sum(rate(requests_total{status_code=~"5.."}[5m]))'
          - refId: B
            conditions:
              - evaluator:
                  params: [0.05]
                  type: gt
                query:
                  params: [A, 5m, now]
        for: 5m
        annotations:
          description: 'Error rate is {{ $values.A }}'
        labels:
          severity: critical
          team: platform

4. Grafana API Client

// grafana-api-client.js
const axios = require('axios');

class GrafanaClient {
  constructor(baseUrl, apiKey) {
    this.baseUrl = baseUrl;
    this.client = axios.create({
      baseURL: baseUrl,
      headers: {
        'Authorization': `Bearer ${apiKey}`,
        'Content-Type': 'application/json'
      }
    });
  }

  async createDashboard(dashboard) {
    const response = await this.client.post('/api/dashboards/db', {
      dashboard: dashboard,
      overwrite: true
    });
    return response.data;
  }

  async getDashboard(uid) {
    const response = await this.client.get(`/api/dashboards/uid/${uid}`);
    return response.data;
  }

  async createAlert(alert) {
    const response = await this.client.post('/api/alerts', alert);
    return response.data;
  }

  async listDashboards() {
    const response = await this.client.get('/api/search?query=');
    return response.data;
  }
}

module.exports = GrafanaClient;

5. Docker Compose Setup

version: '3.8'
services:
  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    environment:
      GF_SECURITY_ADMIN_PASSWORD: ${GRAFANA_PASSWORD:-admin}
      GF_USERS_ALLOW_SIGN_UP: 'false'
      GF_SERVER_ROOT_URL: http://grafana.example.com
    volumes:
      - ./provisioning:/etc/grafana/provisioning
      - grafana_storage:/var/lib/grafana
    depends_on:
      - prometheus

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

volumes:
  grafana_storage:
  prometheus_storage:

Best Practices

✅ DO

• Use meaningful dashboard titles
• Add documentation panels
• Implement row-based organization
• Use variables for flexibility
• Set appropriate refresh intervals
• Include runbook links in alerts
• Test alerts before deploying
• Use consistent color schemes
• Version control dashboard JSON

❌ DON'T

• Overload dashboards with too many panels
• Mix different time ranges without justification
• Create without runbooks
• Ignore alert noise
• Use inconsistent metric naming
• Set refresh too frequently
• Forget to configure datasources
• Leave default passwords

Visualization Types

• Graph: Time-series trends
• Stat: Single value with thresholds
• Gauge: Percentage or usage
• Heatmap: Pattern detection
• Bar Chart: Category comparison
• Pie Chart: Composition