# 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

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

### 2. Grafana

```bash
# 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`：

```yaml
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"
```