Distributed Tracing

分布式追踪

使用 Jaeger 和 Tempo 实现分布式追踪，实现微服务间请求流的可见性。

何时不使用此技能

• 任务与分布式追踪无关
• 需要此范围之外的其他领域或工具

说明

• 明确目标、约束和所需输入。
• 应用相关最佳实践并验证结果。
• 提供可操作的步骤和验证方法。
• 如需详细示例，请打开 resources/implementation-playbook.md。

目的

跨分布式系统追踪请求，以了解延迟、依赖关系和故障点。

使用此技能的场景

• 调试延迟问题
• 理解服务依赖关系
• 识别瓶颈
• 追踪错误传播
• 分析请求路径

分布式追踪概念

Trace 结构

Trace (Request ID: abc123)
  ↓
Span (frontend) [100ms]
  ↓
Span (api-gateway) [80ms]
  ├→ Span (auth-service) [10ms]
  └→ Span (user-service) [60ms]
      └→ Span (database) [40ms]

核心组件

• Trace - 端到端请求旅程
• Span - trace 中的单个操作
• Context - 服务间传播的元数据
• Tags - 用于过滤的键值对
• Logs - span 内的时间戳事件

Jaeger 设置

Kubernetes 部署

# 部署 Jaeger Operator
kubectl create namespace observability
kubectl create -f https://github.com/jaegertracing/jaeger-operator/releases/download/v1.51.0/jaeger-operator.yaml -n observability

# 部署 Jaeger 实例
kubectl apply -f - <<EOF
apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: jaeger
  namespace: observability
spec:
  strategy: production
  storage:
    type: elasticsearch
    options:
      es:
        server-urls: http://elasticsearch:9200
  ingress:
    enabled: true
EOF

Docker Compose

version: '3.8'
services:
  jaeger:
    image: jaegertracing/all-in-one:latest
    ports:
      - "5775:5775/udp"
      - "6831:6831/udp"
      - "6832:6832/udp"
      - "5778:5778"
      - "16686:16686"  # UI
      - "14268:14268"  # Collector
      - "14250:14250"  # gRPC
      - "9411:9411"    # Zipkin
    environment:
      - COLLECTOR_ZIPKIN_HOST_PORT=:9411

参考： 参见 references/jaeger-setup.md

应用程序埋点

OpenTelemetry（推荐）

Python (Flask)

from opentelemetry import trace
from opentelemetry.exporter.jaeger.thrift import JaegerExporter
from opentelemetry.sdk.resources import SERVICE_NAME, Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.instrumentation.flask import FlaskInstrumentor
from flask import Flask

# 初始化 tracer
resource = Resource(attributes={SERVICE_NAME: "my-service"})
provider = TracerProvider(resource=resource)
processor = BatchSpanProcessor(JaegerExporter(
    agent_host_name="jaeger",
    agent_port=6831,
))
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

# 埋点 Flask
app = Flask(__name__)
FlaskInstrumentor().instrument_app(app)

@app.route('/api/users')
def get_users():
    tracer = trace.get_tracer(__name__)

    with tracer.start_as_current_span("get_users") as span:
        span.set_attribute("user.count", 100)
        # 业务逻辑
        users = fetch_users_from_db()
        return {"users": users}

def fetch_users_from_db():
    tracer = trace.get_tracer(__name__)

    with tracer.start_as_current_span("database_query") as span:
        span.set_attribute("db.system", "postgresql")
        span.set_attribute("db.statement", "SELECT * FROM users")
        # 数据库查询
        return query_database()

Node.js (Express)

const { NodeTracerProvider } = require('@opentelemetry/sdk-trace-node');
const { JaegerExporter } = require('@opentelemetry/exporter-jaeger');
const { BatchSpanProcessor } = require('@opentelemetry/sdk-trace-base');
const { registerInstrumentations } = require('@opentelemetry/instrumentation');
const { HttpInstrumentation } = require('@opentelemetry/instrumentation-http');
const { ExpressInstrumentation } = require('@opentelemetry/instrumentation-express');

// 初始化 tracer
const provider = new NodeTracerProvider({
  resource: { attributes: { 'service.name': 'my-service' } }
});

const exporter = new JaegerExporter({
  endpoint: 'http://jaeger:14268/api/traces'
});

provider.addSpanProcessor(new BatchSpanProcessor(exporter));
provider.register();

// 埋点库
registerInstrumentations({
  instrumentations: [
    new HttpInstrumentation(),
    new ExpressInstrumentation(),
  ],
});

const express = require('express');
const app = express();

app.get('/api/users', async (req, res) => {
  const tracer = trace.getTracer('my-service');
  const span = tracer.startSpan('get_users');

  try {
    const users = await fetchUsers();
    span.setAttributes({ 'user.count': users.length });
    res.json({ users });
  } finally {
    span.end();
  }
});

Go

package main

import (
    "context"
    "go.opentelemetry.io/otel"
    "go.opentelemetry.io/otel/exporters/jaeger"
    "go.opentelemetry.io/otel/sdk/resource"
    sdktrace "go.opentelemetry.io/otel/sdk/trace"
    semconv "go.opentelemetry.io/otel/semconv/v1.4.0"
)

func initTracer() (*sdktrace.TracerProvider, error) {
    exporter, err := jaeger.New(jaeger.WithCollectorEndpoint(
        jaeger.WithEndpoint("http://jaeger:14268/api/traces"),
    ))
    if err != nil {
        return nil, err
    }

    tp := sdktrace.NewTracerProvider(
        sdktrace.WithBatcher(exporter),
        sdktrace.WithResource(resource.NewWithAttributes(
            semconv.SchemaURL,
            semconv.ServiceNameKey.String("my-service"),
        )),
    )

    otel.SetTracerProvider(tp)
    return tp, nil
}

func getUsers(ctx context.Context) ([]User, error) {
    tracer := otel.Tracer("my-service")
    ctx, span := tracer.Start(ctx, "get_users")
    defer span.End()

    span.SetAttributes(attribute.String("user.filter", "active"))

    users, err := fetchUsersFromDB(ctx)
    if err != nil {
        span.RecordError(err)
        return nil, err
    }

    span.SetAttributes(attribute.Int("user.count", len(users)))
    return users, nil
}

参考： 参见 references/instrumentation.md

Context 传播

HTTP 头

traceparent: 00-0af7651916cd43dd8448eb211c80319c-b7ad6b7169203331-01
tracestate: congo=t61rcWkgMzE

HTTP 请求中的传播

Python

from opentelemetry.propagate import inject

headers = {}
inject(headers)  # 注入 trace context

response = requests.get('http://downstream-service/api', headers=headers)

Node.js

const { propagation } = require('@opentelemetry/api');

const headers = {};
propagation.inject(context.active(), headers);

axios.get('http://downstream-service/api', { headers });

Tempo 设置（Grafana）

Kubernetes 部署

apiVersion: v1
kind: ConfigMap
metadata:
  name: tempo-config
data:
  tempo.yaml: |
    server:
      http_listen_port: 3200

    distributor:
      receivers:
        jaeger:
          protocols:
            thrift_http:
            grpc:
        otlp:
          protocols:
            http:
            grpc:

    storage:
      trace:
        backend: s3
        s3:
          bucket: tempo-traces
          endpoint: s3.amazonaws.com

    querier:
      frontend_worker:
        frontend_address: tempo-query-frontend:9095
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: tempo
spec:
  replicas: 1
  template:
    spec:
      containers:
      - name: tempo
        image: grafana/tempo:latest
        args:
          - -config.file=/etc/tempo/tempo.yaml
        volumeMounts:
        - name: config
          mountPath: /etc/tempo
      volumes:
      - name: config
        configMap:
          name: tempo-config

参考： 参见 assets/jaeger-config.yaml.template

采样策略

概率采样

# 采样 1% 的 trace
sampler:
  type: probabilistic
  param: 0.01

限速采样

# 每秒最多采样 100 个 trace
sampler:
  type: ratelimiting
  param: 100

自适应采样

from opentelemetry.sdk.trace.sampling import ParentBased, TraceIdRatioBased

# 基于 trace ID 采样（确定性）
sampler = ParentBased(root=TraceIdRatioBased(0.01))

Trace 分析

查找慢请求

Jaeger 查询：

service=my-service
duration > 1s

查找错误

Jaeger 查询：

service=my-service
error=true
tags.http.status_code >= 500

服务依赖图

Jaeger 自动生成服务依赖图，显示：

• 服务关系
• 请求速率
• 错误率
• 平均延迟

最佳实践

1. 合理采样（生产环境 1-10%）
2. 添加有意义的标签（user_id、request_id）
3. 跨所有服务边界传播 context
4. 在 span 中记录异常
5. 使用一致的命名规范操作
6. 监控追踪开销（CPU 影响 <1%）
7. 设置 trace 错误告警
8. 实现分布式 context（baggage）
9. 使用 span 事件标记重要里程碑
10. 文档化埋点标准

与日志集成

关联日志

import logging
from opentelemetry import trace

logger = logging.getLogger(__name__)

def process_request():
    span = trace.get_current_span()
    trace_id = span.get_span_context().trace_id

    logger.info(
        "Processing request",
        extra={"trace_id": format(trace_id, '032x')}
    )

故障排查

没有 trace 出现：

• 检查 collector 端点
• 验证网络连接
• 检查采样配置
• 查看应用程序日志

延迟开销过高：

• 降低采样率
• 使用批量 span 处理器
• 检查 exporter 配置

参考文件

• references/jaeger-setup.md - Jaeger 安装
• references/instrumentation.md - 埋点模式
• assets/jaeger-config.yaml.template - Jaeger 配置

相关技能

• prometheus-configuration - 用于指标
• grafana-dashboards - 用于可视化
• slo-implementation - 用于延迟 SLO

限制

• 仅当任务明确符合上述描述的范围时使用此技能。
• 不要将输出视为环境特定验证、测试或专家审查的替代品。
• 如果缺少所需输入、权限、安全边界或成功标准，请停下来请求澄清。