selfrelease
81 lines
2.2 KiB
Markdown
81 lines
2.2 KiB
Markdown
# TCS-IPTV 监控部署说明
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## 组件
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| 文件 | 说明 |
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|------|------|
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| `prometheus.yml` | Prometheus 采集配置，抓取 api-svc / chain-svc / hash-api / catalog-svc 的 `/metrics` |
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| `grafana-dashboard.json` | Grafana 面板 JSON，覆盖 4 金指标 |
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## 快速部署
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### 1. Prometheus
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```bash
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# 启动 Prometheus（Docker）
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docker run -d --name prometheus \
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-p 9090:9090 \
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-v $(pwd)/prometheus.yml:/etc/prometheus/prometheus.yml \
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prom/prometheus
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```
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### 2. Grafana
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```bash
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# 启动 Grafana（Docker）
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docker run -d --name grafana \
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-p 3000:3000 \
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-v $(pwd)/grafana-dashboard.json:/etc/grafana/provisioning/dashboards/tcs-iptv.json \
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grafana/grafana
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```
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### 3. 导入面板
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1. 打开 Grafana → http://localhost:3000
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2. Dashboards → Import → 上传 `grafana-dashboard.json`
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3. 数据源选择 Prometheus（http://prometheus:9090）
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## 面板指标说明
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| 面板 | 指标 | Prometheus Metric |
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|------|------|-------------------|
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| 请求延迟 | P50/P95/P99 | `tcs_http_request_duration_seconds` (Histogram) |
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| 请求总量 | QPS by path | `tcs_http_requests_total` (Counter) |
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| 错误率 | 4xx/5xx QPS | `tcs_http_errors_total` (Counter) |
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| 在途请求 | 并发数 | `tcs_http_in_flight_requests` (Gauge) |
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| 错误率百分比 | 错误/总请求 | 上述两个 Counter 计算 |
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| 延迟热力图 | by path | Histogram bucket 热力图 |
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## 告警规则建议
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在 Prometheus 中配置 `alerting_rules.yml`：
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```yaml
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groups:
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- name: tcs-iptv-alerts
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rules:
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- alert: HighErrorRate
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expr: sum(rate(tcs_http_errors_total[5m])) / sum(rate(tcs_http_requests_total[5m])) > 0.1
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for: 5m
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labels:
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severity: critical
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annotations:
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summary: "错误率超过 10%"
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- alert: HighLatency
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expr: histogram_quantile(0.95, sum(rate(tcs_http_request_duration_seconds_bucket[5m])) by (le)) > 2
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for: 5m
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labels:
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severity: warning
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annotations:
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summary: "P95 延迟超过 2 秒"
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- alert: HighInFlight
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expr: tcs_http_in_flight_requests > 100
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for: 2m
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labels:
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severity: warning
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annotations:
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summary: "在途请求超过 100"
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```
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