Grafana 可视化实战指南

Grafana 是一个开源的数据可视化和监控平台，支持多种数据源，提供丰富的可视化选项。本文将带你深入了解 Grafana 的核心功能和实战技巧。

什么是 Grafana

Grafana 允许你：

• 查询、可视化、告警和探索指标
• 支持 100+ 数据源（Prometheus、InfluxDB、Elasticsearch、MySQL 等）
• 创建动态可重用的 Dashboard
• 设置告警规则并发送通知

安装与配置

Docker 安装

# 快速启动
docker run -d -p 3000:3000 --name=grafana grafana/grafana:latest

# 带持久化存储
docker run -d \
  -p 3000:3000 \
  --name=grafana \
  -v grafana-storage:/var/lib/grafana \
  grafana/grafana:latest

Docker Compose 配置

version: '3.8'

services:
  grafana:
    image: grafana/grafana:latest
    container_name: grafana
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_USER=admin
      - GF_SECURITY_ADMIN_PASSWORD=admin123
      - GF_USERS_ALLOW_SIGN_UP=false
    volumes:
      - grafana-storage:/var/lib/grafana
      - ./provisioning:/etc/grafana/provisioning
    networks:
      - monitoring
    
  prometheus:
    image: prom/prometheus:latest
    container_name: prometheus
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
      - prometheus-data:/prometheus
    networks:
      - monitoring

volumes:
  grafana-storage:
  prometheus-data:

networks:
  monitoring:
    driver: bridge

数据源配置

Prometheus 数据源

1. 登录 Grafana（默认 admin/admin）
2. 进入 Configuration → Data Sources
3. 点击 "Add data source"
4. 选择 Prometheus
5. 配置 URL：http://prometheus:9090
6. 点击 "Save & Test"

配置文件方式

# provisioning/datasources/prometheus.yml
apiVersion: 1

datasources:
  - name: Prometheus
    type: prometheus
    access: proxy
    url: http://prometheus:9090
    isDefault: true
    editable: false

Dashboard 设计

基础面板

{
  "dashboard": {
    "title": "System Metrics",
    "panels": [
      {
        "title": "CPU Usage",
        "type": "stat",
        "targets": [
          {
            "expr": "100 - (avg(irate(node_cpu_seconds_total{mode=\"idle\"}[5m])) * 100)",
            "legendFormat": "CPU %"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "unit": "percent",
            "thresholds": {
              "steps": [
                {"color": "green", "value": null},
                {"color": "yellow", "value": 70},
                {"color": "red", "value": 85}
              ]
            }
          }
        }
      }
    ]
  }
}

常用面板类型

类型	用途	适用场景
Time series	时序图	指标趋势展示
Stat	统计值	当前值展示
Gauge	仪表盘	百分比/阈值
Bar gauge	条形图	多值比较
Table	表格	详细数据
Pie chart	饼图	占比分析
Heatmap	热力图	分布密度
Logs	日志	日志查看

变量模板

{
  "templating": {
    "list": [
      {
        "name": "node",
        "type": "query",
        "query": "label_values(node_uname_info, nodename)",
        "refresh": 1,
        "includeAll": true,
        "multi": true
      },
      {
        "name": "interval",
        "type": "interval",
        "query": "1m,5m,10m,30m,1h,6h,12h,1d",
        "current": {"text": "5m", "value": "5m"}
      }
    ]
  }
}

PromQL 查询示例

基础查询

# CPU 使用率
100 - (avg(irate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)

# 内存使用率
100 * (1 - node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes)

# 磁盘使用率
100 - (node_filesystem_avail_bytes{mountpoint="/"} / node_filesystem_size_bytes{mountpoint="/"} * 100)

# 网络流量
irate(node_network_receive_bytes_total[5m])
irate(node_network_transmit_bytes_total[5m])

高级查询

# Top 10 CPU 消耗进程
topk(10, sum by (process) (process_cpu_seconds_total))

# 请求速率
sum(rate(http_requests_total[5m])) by (status)

# 错误率
sum(rate(http_requests_total{status=~"5.."}[5m])) / sum(rate(http_requests_total[5m]))

# 95 分位延迟
histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))

告警配置

告警规则

# provisioning/alerting/alert-rules.yml
groups:
  - name: system_alerts
    interval: 30s
    rules:
      - alert: HighCPUUsage
        expr: 100 - (avg(irate(node_cpu_seconds_total{mode="idle"}[5m])) * 100) > 80
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High CPU usage on {{ $labels.instance }}"
          description: "CPU usage is above 80% (current value: {{ $value }}%)"
      
      - alert: HighMemoryUsage
        expr: (1 - node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes) * 100 > 85
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "High memory usage on {{ $labels.instance }}"
          description: "Memory usage is above 85% (current value: {{ $value }}%)"
      
      - alert: DiskSpaceLow
        expr: (1 - node_filesystem_avail_bytes{mountpoint="/"} / node_filesystem_size_bytes{mountpoint="/"}) * 100 > 90
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "Low disk space on {{ $labels.instance }}"
          description: "Disk usage is above 90% (current value: {{ $value }}%)"

通知渠道

# provisioning/notifiers/notifiers.yml
notifiers:
  - name: email-notifier
    type: email
    settings:
      addresses: admin@example.com;ops@example.com
    
  - name: slack-notifier
    type: slack
    settings:
      url: $SLACK_WEBHOOK_URL
      title: '{{ template "default.title" . }}'
      text: '{{ template "default.message" . }}'
    
  - name: webhook-notifier
    type: webhook
    settings:
      url: https://api.example.com/alerts
      httpMethod: POST

Dashboard 导入导出

导入官方 Dashboard

1. 访问 Grafana Dashboards
2. 搜索需要的 Dashboard（如 Node Exporter）
3. 复制 Dashboard ID
4. 在 Grafana 中点击 Import
5. 粘贴 ID 并导入

推荐 Dashboard

Dashboard ID	名称	用途
1860	Node Exporter Full	主机监控
6417	Kubernetes Cluster	K8s 集群监控
315	MySQL Overview	MySQL 监控
9628	PostgreSQL Database	PostgreSQL 监控
10991	Redis	Redis 监控

最佳实践

1. 命名规范

Dashboard: [Team] - [Service] - [Purpose]
Panel: [Metric] - [Aggregation]
Variable: [resource]_[attribute]

2. 颜色规范

颜色	含义
Green	正常/健康
Yellow	警告
Red	严重/错误
Blue	信息/中性

3. 刷新频率

• 实时监控：5s-10s
• 常规监控：30s-1m
• 历史趋势：5m-1h

4. 组织管理

Folders:
├── Infrastructure
│   ├── Nodes
│   ├── Containers
│   └── Network
├── Applications
│   ├── API Services
│   ├── Databases
│   └── Cache
└── Business
    ├── User Metrics
    └── Revenue

总结

Grafana 是构建监控可视化系统的强大工具。通过合理配置数据源、设计 Dashboard 和设置告警，可以构建完整的可观测性平台。

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参考资源：

• Grafana 官方文档
• Grafana Dashboards 市场
• PromQL 查询指南