Distributed Tracing

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• Best Practices

Overview

Set up distributed tracing infrastructure with Jaeger or Zipkin to track requests across microservices and identify performance bottlenecks.

When to Use

• Debugging microservice interactions
• Identifying performance bottlenecks
• Tracking request flows
• Analyzing service dependencies
• Root cause analysis

Quick Start

Minimal working example:

# docker-compose.yml
version: "3.8"
services:
  jaeger:
    image: jaegertracing/all-in-one:latest
    ports:
      - "5775:5775/udp"
      - "6831:6831/udp"
      - "16686:16686"
      - "14268:14268"
    networks:
      - tracing

networks:
  tracing:

Reference Guides

Detailed implementations in the references/ directory:

| Guide | Contents | |---|---| | Jaeger Setup | Jaeger Setup, Node.js Jaeger Instrumentation | | Express Tracing Middleware | Express Tracing Middleware | | Python Jaeger Integration | Python Jaeger Integration | | Distributed Context Propagation | Distributed Context Propagation | | Zipkin Integration | Zipkin Integration, Trace Analysis |

Best Practices

✅ DO

• Sample appropriately for your traffic volume
• Propagate trace context across services
• Add meaningful span tags
• Log errors with spans
• Use consistent service naming
• Monitor trace latency
• Document trace format
• Keep instrumentation lightweight

❌ DON'T

• Sample 100% in production
• Skip trace context propagation
• Log sensitive data in spans
• Create excessive spans
• Ignore sampling configuration
• Use unbounded cardinality tags
• Deploy without testing collection