Grafana Dashboards

Create and manage production-ready Grafana dashboards for comprehensive system observability.

Purpose

Design effective Grafana dashboards for monitoring applications, infrastructure, and business metrics.

When to Use

• Visualize Prometheus metrics

• Create custom dashboards

• Implement SLO dashboards

• Monitor infrastructure

• Track business KPIs

Dashboard Design Principles

1. Hierarchy of Information

┌─────────────────────────────────────┐ │ Critical Metrics (Big Numbers) │ ├─────────────────────────────────────┤ │ Key Trends (Time Series) │ ├─────────────────────────────────────┤ │ Detailed Metrics (Tables/Heatmaps) │ └─────────────────────────────────────┘

2. RED Method (Services)

• Rate - Requests per second

• Errors - Error rate

• Duration - Latency/response time

3. USE Method (Resources)

• Utilization - % time resource is busy

• Saturation - Queue length/wait time

• Errors - Error count

Dashboard Structure

API Monitoring Dashboard

{ "dashboard": { "title": "API Monitoring", "tags": ["api", "production"], "timezone": "browser", "refresh": "30s", "panels": [ { "title": "Request Rate", "type": "graph", "targets": [ { "expr": "sum(rate(http_requests_total[5m])) by (service)", "legendFormat": "{{service}}" } ], "gridPos": {"x": 0, "y": 0, "w": 12, "h": 8} }, { "title": "Error Rate %", "type": "graph", "targets": [ { "expr": "(sum(rate(http_requests_total{status=~"5.."}[5m])) / sum(rate(http_requests_total[5m]))) * 100", "legendFormat": "Error Rate" } ], "alert": { "conditions": [ { "evaluator": {"params": [5], "type": "gt"}, "operator": {"type": "and"}, "query": {"params": ["A", "5m", "now"]}, "type": "query" } ] }, "gridPos": {"x": 12, "y": 0, "w": 12, "h": 8} }, { "title": "P95 Latency", "type": "graph", "targets": [ { "expr": "histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service))", "legendFormat": "{{service}}" } ], "gridPos": {"x": 0, "y": 8, "w": 24, "h": 8} } ] } }

Reference: See assets/api-dashboard.json

Panel Types

1. Stat Panel (Single Value)

{ "type": "stat", "title": "Total Requests", "targets": [{ "expr": "sum(http_requests_total)" }], "options": { "reduceOptions": { "values": false, "calcs": ["lastNotNull"] }, "orientation": "auto", "textMode": "auto", "colorMode": "value" }, "fieldConfig": { "defaults": { "thresholds": { "mode": "absolute", "steps": [ {"value": 0, "color": "green"}, {"value": 80, "color": "yellow"}, {"value": 90, "color": "red"} ] } } } }

2. Time Series Graph

{ "type": "graph", "title": "CPU Usage", "targets": [{ "expr": "100 - (avg by (instance) (rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)" }], "yaxes": [ {"format": "percent", "max": 100, "min": 0}, {"format": "short"} ] }

3. Table Panel

{ "type": "table", "title": "Service Status", "targets": [{ "expr": "up", "format": "table", "instant": true }], "transformations": [ { "id": "organize", "options": { "excludeByName": {"Time": true}, "indexByName": {}, "renameByName": { "instance": "Instance", "job": "Service", "Value": "Status" } } } ] }

4. Heatmap

{ "type": "heatmap", "title": "Latency Heatmap", "targets": [{ "expr": "sum(rate(http_request_duration_seconds_bucket[5m])) by (le)", "format": "heatmap" }], "dataFormat": "tsbuckets", "yAxis": { "format": "s" } }

Variables

Query Variables

{ "templating": { "list": [ { "name": "namespace", "type": "query", "datasource": "Prometheus", "query": "label_values(kube_pod_info, namespace)", "refresh": 1, "multi": false }, { "name": "service", "type": "query", "datasource": "Prometheus", "query": "label_values(kube_service_info{namespace="$namespace"}, service)", "refresh": 1, "multi": true } ] } }

Use Variables in Queries

sum(rate(http_requests_total{namespace="$namespace", service=~"$service"}[5m]))

Alerts in Dashboards

{ "alert": { "name": "High Error Rate", "conditions": [ { "evaluator": { "params": [5], "type": "gt" }, "operator": {"type": "and"}, "query": { "params": ["A", "5m", "now"] }, "reducer": {"type": "avg"}, "type": "query" } ], "executionErrorState": "alerting", "for": "5m", "frequency": "1m", "message": "Error rate is above 5%", "noDataState": "no_data", "notifications": [ {"uid": "slack-channel"} ] } }

Dashboard Provisioning

dashboards.yml:

apiVersion: 1

providers:

• name: 'default' orgId: 1 folder: 'General' type: file disableDeletion: false updateIntervalSeconds: 10 allowUiUpdates: true options: path: /etc/grafana/dashboards

Common Dashboard Patterns

Infrastructure Dashboard

Key Panels:

• CPU utilization per node

• Memory usage per node

• Disk I/O

• Network traffic

• Pod count by namespace

• Node status

Reference: See assets/infrastructure-dashboard.json

Database Dashboard

Key Panels:

• Queries per second

• Connection pool usage

• Query latency (P50, P95, P99)

• Active connections

• Database size

• Replication lag

• Slow queries

Reference: See assets/database-dashboard.json

Application Dashboard

Key Panels:

• Request rate

• Error rate

• Response time (percentiles)

• Active users/sessions

• Cache hit rate

• Queue length

Best Practices

• Start with templates (Grafana community dashboards)

• Use consistent naming for panels and variables

• Group related metrics in rows

• Set appropriate time ranges (default: Last 6 hours)

• Use variables for flexibility

• Add panel descriptions for context

• Configure units correctly

• Set meaningful thresholds for colors

• Use consistent colors across dashboards

• Test with different time ranges

Dashboard as Code

Terraform Provisioning

resource "grafana_dashboard" "api_monitoring" { config_json = file("${path.module}/dashboards/api-monitoring.json") folder = grafana_folder.monitoring.id }

resource "grafana_folder" "monitoring" { title = "Production Monitoring" }

Ansible Provisioning

• name: Deploy Grafana dashboards copy: src: "{{ item }}" dest: /etc/grafana/dashboards/ with_fileglob:
  • "dashboards/*.json" notify: restart grafana

Related Skills

• prometheus-configuration

• For metric collection

• slo-implementation

• For SLO dashboards