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distributed-tracing

Name: distributed-tracing
Author: wshobson

Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.

TL;DR

Implement distributed tracing with Jaeger/Tempo to track requests across microservices and identify performance bottlenecks.

What Is It

Track requests across microservices using traces and spans
Propagate context between services via HTTP headers
Visualize request flows and identify latency or errors
Enable observability in distributed systems

Key Features

Track end-to-end request journeys (trace)
Break down operations into spans (operations within a trace)
Propagate context between services (context propagation)
Use tags and logs to filter and analyze data
Supports multiple languages (Python, Node.js, Go)
Integrates with Kubernetes and Docker
Provides UI for real-time monitoring
Identifies performance bottlenecks and errors

How to Use

1.Deploy Jaeger or Tempo using Kubernetes or Docker Compose
2.Instrument applications (Flask, Express, Go) with OpenTelemetry
3.Configure context propagation via HTTP headers
4.Start and monitor spans in the Jaeger UI

Guardrails & Gotchas

MUST use consistent trace IDs across services
FORBIDDEN to omit context propagation in service calls
CRITICAL: misconfigured spans can misrepresent latency
MUST ensure proper error propagation to trace errors

GitHub Stats

Author

@wshobson

Repository

wshobson/agents

Stars

37,445

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