Grafana Dashboards

作者 wshobson46891e7e60da无许可证收录于 2026年10月8日更新于 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.

仅含说明Data & AnalyticsDevOps & Cloud
AI 生成的概览

创建和管理用于可视化系统与应用指标的 Grafana 仪表板。

功能
该技能提供构建生产级 Grafana 仪表板的指导和 JSON 示例，用于可视化请求速率、错误率、延迟和资源使用等指标。内容涵盖面板类型（stat、时间序列、表格、热力图）、模板变量、仪表板告警，以及通过文件、Terraform 或 Ansible 进行配置。它还介绍 RED 和 USE 等设计原则，以及常见的基础设施、数据库和应用仪表板模式。
适用场景
适用于构建监控仪表板、可视化 Prometheus 指标、实现 SLO 仪表板或创建运维可观测性界面的场景。也适合需要统一仪表板结构和配置方式的团队。
运行要求
不包含脚本，仅为说明文档和 JSON/YAML/HCL 示例。要生成实际仪表板，需要 Grafana 实例以及 Prometheus 等指标数据源；配置示例假定使用 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

来源与署名

来源：wshobson/agents位于plugins/observability-monitoring/skills/grafana-dashboards提交46891e7

许可证： 无许可证

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