MAcode/tcs-iptv/deploy/monitoring/README.md

TCS-IPTV 监控部署说明

组件

文件	说明
prometheus.yml	Prometheus 采集配置，抓取 api-svc / chain-svc / hash-api / catalog-svc 的 /metrics
grafana-dashboard.json	Grafana 面板 JSON，覆盖 4 金指标

快速部署

1. Prometheus

# 启动 Prometheus（Docker）
docker run -d --name prometheus \
  -p 9090:9090 \
  -v $(pwd)/prometheus.yml:/etc/prometheus/prometheus.yml \
  prom/prometheus

2. Grafana

# 启动 Grafana（Docker）
docker run -d --name grafana \
  -p 3000:3000 \
  -v $(pwd)/grafana-dashboard.json:/etc/grafana/provisioning/dashboards/tcs-iptv.json \
  grafana/grafana

3. 导入面板

1. 打开 Grafana → http://localhost:3000
2. Dashboards → Import → 上传 grafana-dashboard.json
3. 数据源选择 Prometheus（http://prometheus:9090）

面板指标说明

面板	指标	Prometheus Metric
请求延迟	P50/P95/P99	tcs_http_request_duration_seconds (Histogram)
请求总量	QPS by path	tcs_http_requests_total (Counter)
错误率	4xx/5xx QPS	tcs_http_errors_total (Counter)
在途请求	并发数	tcs_http_in_flight_requests (Gauge)
错误率百分比	错误/总请求	上述两个 Counter 计算
延迟热力图	by path	Histogram bucket 热力图

告警规则建议

在 Prometheus 中配置 alerting_rules.yml：

groups:
  - name: tcs-iptv-alerts
    rules:
      - alert: HighErrorRate
        expr: sum(rate(tcs_http_errors_total[5m])) / sum(rate(tcs_http_requests_total[5m])) > 0.1
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "错误率超过 10%"

      - alert: HighLatency
        expr: histogram_quantile(0.95, sum(rate(tcs_http_request_duration_seconds_bucket[5m])) by (le)) > 2
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "P95 延迟超过 2 秒"

      - alert: HighInFlight
        expr: tcs_http_in_flight_requests > 100
        for: 2m
        labels:
          severity: warning
        annotations:
          summary: "在途请求超过 100"