How do I correlate traces with my application logs?

The skill provides specific patterns for injecting trace IDs into log metadata, allowing you to filter logs for a specific request journey in tools like Kibana or Grafana.

What is the performance impact of adding distributed tracing?

When implemented with appropriate sampling rates (1-10%) and asynchronous batch exporters, tracing typically adds less than 1% CPU overhead to applications.

Which programming languages are covered?

It provides detailed instrumentation examples for Python (Flask), Node.js (Express), and Go using the OpenTelemetry SDK.

Can I use this for production environments?

Yes, it includes production-ready strategies such as probabilistic sampling, persistent storage configuration for Kubernetes, and batch span processing to minimize overhead.

What tools does this tracing skill support?

This skill primarily supports OpenTelemetry for instrumentation, with deployment and configuration guidance for Jaeger and Grafana Tempo as storage backends.

Distributed Tracing

Name: Distributed Tracing
Author: HermeticOrmus

byHermeticOrmus

분석 및 모니터링

Implements distributed tracing with Jaeger and Tempo to monitor microservice request flows and pinpoint performance bottlenecks.

소개

This skill provides a comprehensive framework for implementing end-to-end observability in distributed systems. It guides developers through the deployment of Jaeger and Tempo, the instrumentation of applications using OpenTelemetry across Python, Node.js, and Go, and the management of trace context propagation. By leveraging these patterns, teams can gain deep visibility into service dependencies, latency issues, and error propagation, making it an essential resource for debugging complex microservice architectures and maintaining high-performance production environments.

주요 기능

Multi-language OpenTelemetry instrumentation for Python, Node.js, and Go
Context propagation patterns for HTTP service boundaries
0 GitHub stars
Advanced sampling strategies including probabilistic and rate-limiting
Kubernetes and Docker Compose deployment patterns for Jaeger and Tempo
Trace-log correlation techniques for unified debugging

사용 사례

Visualizing service dependencies and mapping system architecture
Identifying high-latency bottlenecks in complex request chains
Root cause analysis for intermittent errors across distributed services

소개

주요 기능

Multi-language OpenTelemetry instrumentation for Python, Node.js, and Go
Context propagation patterns for HTTP service boundaries
0 GitHub stars
Advanced sampling strategies including probabilistic and rate-limiting
Kubernetes and Docker Compose deployment patterns for Jaeger and Tempo
Trace-log correlation techniques for unified debugging

사용 사례

Visualizing service dependencies and mapping system architecture
Identifying high-latency bottlenecks in complex request chains
Root cause analysis for intermittent errors across distributed services