How does it help with performance debugging?

It allows you to find slow requests using specific trace queries and visualizes the entire request journey through spans to pinpoint exactly where delays or errors occur.

Does it support log correlation?

Yes, it includes best practices for injecting trace IDs into application logs, allowing you to correlate specific spans with their corresponding log entries for faster troubleshooting.

Which languages are supported for instrumentation?

This skill provides standardized OpenTelemetry implementation patterns and code examples for Python (Flask), Node.js (Express), and Go.

Can I use this with existing Jaeger or Tempo setups?

Yes, the skill includes configuration templates and connection logic that can be adapted to both new and existing observability stacks, including Kubernetes-based deployments.

Distributed Tracing Implementation

Name: Distributed Tracing Implementation
Author: HermeticOrmus

byHermeticOrmus

분석 및 모니터링

Implements end-to-end request tracing across microservices using OpenTelemetry, Jaeger, and Tempo for deep observability.

소개

This skill provides comprehensive guidance for implementing distributed tracing in microservice architectures to achieve full visibility into request flows. It streamlines the process of tracking requests, identifying latency bottlenecks, and debugging service dependencies by providing standardized patterns for OpenTelemetry instrumentation in Python, Node.js, and Go. With ready-to-use configurations for Jaeger and Grafana Tempo, it enables developers to visualize complex system interactions and significantly improve troubleshooting efficiency in distributed environments.

주요 기능

0 GitHub stars
OpenTelemetry instrumentation for Python, Node.js, and Go
Jaeger and Tempo infrastructure setup via Kubernetes and Docker
Standardized context propagation for cross-service request tracking
Configurable sampling strategies including probabilistic and rate-limiting
Trace-to-log correlation for enhanced debugging and performance analysis

사용 사례

Debugging high latency and identifying performance bottlenecks in microservice clusters
Visualizing service dependency graphs to understand system architecture and failure points
Implementing distributed observability and SLO monitoring for cloud-native applications

소개

주요 기능

0 GitHub stars
OpenTelemetry instrumentation for Python, Node.js, and Go
Jaeger and Tempo infrastructure setup via Kubernetes and Docker
Standardized context propagation for cross-service request tracking
Configurable sampling strategies including probabilistic and rate-limiting
Trace-to-log correlation for enhanced debugging and performance analysis

사용 사례

Debugging high latency and identifying performance bottlenecks in microservice clusters
Visualizing service dependency graphs to understand system architecture and failure points
Implementing distributed observability and SLO monitoring for cloud-native applications