接続

Grafana Dashboard Engineer

anton-abyzovbyanton-abyzov
•
13
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分析と監視

Automates the creation and management of production-grade Grafana dashboards for real-time system and application observability.

This skill empowers Claude to design, implement, and maintain professional Grafana dashboards following industry-standard observability patterns like RED and USE. It enables the automated generation of complex dashboard JSON configurations, panel visualizations, and dynamic variables for monitoring microservices, infrastructure, or databases. By providing standardized templates and implementation patterns, this skill ensures your observability stack adheres to best practices in data hierarchy, alerting, and provisioning through infrastructure-as-code tools like Terraform or Ansible.

主な機能

01Generates production-ready Grafana JSON configurations for various panel types
02Configures dynamic dashboard variables and templating for environment flexibility
03Implements industry-standard monitoring frameworks like RED and USE
04Integrates alerting thresholds and notification configurations directly into dashboards
05Supports Dashboard-as-Code workflows using Terraform and Ansible provisioning
0613 GitHub stars

ユースケース

01Building comprehensive API monitoring dashboards with latency and error rate tracking
02Visualizing infrastructure health and resource utilization across cloud clusters
03Creating operational observability interfaces for database performance and query analysis
name description
grafana-dashboards
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.

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

  1. Start with templates (Grafana community dashboards)
  2. Use consistent naming for panels and variables
  3. Group related metrics in rows
  4. Set appropriate time ranges (default: Last 6 hours)
  5. Use variables for flexibility
  6. Add panel descriptions for context
  7. Configure units correctly
  8. Set meaningful thresholds for colors
  9. Use consistent colors across dashboards
  10. 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

Reference Files

  • assets/api-dashboard.json - API monitoring dashboard
  • assets/infrastructure-dashboard.json - Infrastructure dashboard
  • assets/database-dashboard.json - Database monitoring dashboard
  • references/dashboard-design.md - Dashboard design guide

Related Skills

  • prometheus-configuration - For metric collection
  • slo-implementation - For SLO dashboards