微服务间调用链路追踪实现

微服务间调用链路追踪实现

在机器学习模型监控体系中，微服务间的调用链路追踪是保障系统稳定性的关键环节。本文将详细介绍如何通过OpenTelemetry实现跨服务的调用链路监控。

核心监控指标配置

# tracing.yaml
receivers:
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318

processors:
  batch:
  filter:
    traces:
      span:
        - name != "health_check"

exporters:
  otlp:
    endpoint: jaeger-collector:4317
    tls:
      insecure: true

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch, filter]
      exporters: [otlp]

关键告警配置方案

{
  "alert_rules": [
    {
      "name": "high_latency",
      "query": "histogram_quantile(0.95, sum(rate(ml_service_duration_seconds_bucket{service=\"model-api\"}[5m])) by (le)) > 2",
      "threshold": 2,
      "duration": "5m",
      "severity": "warning"
    },
    {
      "name": "error_rate_spike",
      "query": "rate(ml_service_requests_total{status_code=~\"5..\"}[1m]) / rate(ml_service_requests_total[1m]) > 0.05",
      "threshold": 0.05,
      "duration": "1m",
      "severity": "critical"
    }
  ]
}

复现步骤

1. 部署OpenTelemetry Collector作为链路追踪中心
2. 在每个微服务中集成OpenTelemetry SDK
3. 配置trace采样率和导出器
4. 验证链路数据是否正常上报至Jaeger UI

通过以上配置，可实现模型服务间调用延迟、错误率等核心指标的实时监控。