Grafana Dashboards

作者 wshobson46891e7e60da無授權條款收錄於 2026年10月8日更新於 2026年10月8日

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.

僅含說明Data & AnalyticsDevOps & Cloud
AI 產生的概覽

建立與管理用於視覺化系統與應用程式指標的 Grafana 儀表板。

功能
此技能提供建置正式環境 Grafana 儀表板的指引與 JSON 範例，用於視覺化請求速率、錯誤率、延遲與資源使用等指標。內容涵蓋面板類型（stat、時間序列、表格、熱區圖）、範本變數、儀表板警示，以及透過檔案、Terraform 或 Ansible 進行佈建。它也說明 RED 與 USE 等設計原則，以及常見的基礎架構、資料庫與應用程式儀表板模式。
適用情境
適用於建置監控儀表板、視覺化 Prometheus 指標、實作 SLO 儀表板，或建立維運可觀測性介面的情境。也適合需要一致儀表板結構與佈建方式的團隊。
執行需求
不含指令碼，僅有說明文件與 JSON/YAML/HCL 範例。要產生實際儀表板，需要 Grafana 執行個體與 Prometheus 等指標資料來源；佈建範例假設使用 Terraform 或 Ansible 等工具。

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

json
{  "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 }      }    ]  }}

Panel Types

1. Stat Panel (Single Value)

json
{  "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

json
{  "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

json
{  "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

json
{  "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

json
{  "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

json
{  "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:

yaml
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

Database Dashboard

Key Panels:

  • Queries per second
  • Connection pool usage
  • Query latency (P50, P95, P99)
  • Active connections
  • Database size
  • Replication lag
  • Slow queries

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

hcl
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

yaml
- 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

來源與署名

來源：wshobson/agents位於plugins/observability-monitoring/skills/grafana-dashboards提交46891e7

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