Can I use this with multiple programming languages?

Yes, the skill provides specific implementation patterns and code examples for Python (Flask), Node.js (Express), and Go using the OpenTelemetry SDK.

How does this help with performance monitoring?

It allows you to visualize the entire lifecycle of a request across service boundaries, pinpointing exactly which service, operation, or database query is causing latency.

What tracing backends are supported by this skill?

This skill provides configuration and setup patterns for Jaeger and Grafana Tempo, which are the industry standards for trace storage and visualization.

Does this include deployment configurations?

Yes, it includes production-ready Docker Compose and Kubernetes manifests (including Jaeger Operator) for deploying tracing collectors and query engines.

How do I manage the performance overhead of tracing?

The skill includes various sampling strategies, such as probabilistic and rate-limiting sampling, to ensure observability doesn't negatively impact system performance.

Distributed Tracing Implementation

Name: Distributed Tracing Implementation
Author: HermeticOrmus

byHermeticOrmus

分析与监控

Implements distributed tracing across microservices using Jaeger and Tempo to identify performance bottlenecks and visualize request flows.

关于

This skill provides comprehensive patterns and boilerplate for implementing distributed tracing in microservices architectures using OpenTelemetry, Jaeger, and Grafana Tempo. It assists developers in setting up observability infrastructure, instrumenting applications across multiple languages (Python, Node.js, Go), and configuring context propagation to track requests end-to-end. By providing standardized sampling strategies and analysis queries, it helps teams identify high-latency spans, debug error propagation, and visualize complex service dependencies in distributed environments.

主要功能

Advanced sampling strategies including probabilistic and rate-limited sampling
Infrastructure setup for Jaeger and Grafana Tempo via Kubernetes and Docker
End-to-end context propagation implementation for HTTP headers
Multi-language instrumentation (Python, Node.js, Go) using OpenTelemetry
Integration patterns for correlating traces with application logs
0 GitHub stars

使用场景

Debugging latency and performance bottlenecks in complex microservices environments
Analyzing error propagation and identifying failure points in backend services
Visualizing request flow and service dependencies across distributed systems

关于

主要功能

Advanced sampling strategies including probabilistic and rate-limited sampling
Infrastructure setup for Jaeger and Grafana Tempo via Kubernetes and Docker
End-to-end context propagation implementation for HTTP headers
Multi-language instrumentation (Python, Node.js, Go) using OpenTelemetry
Integration patterns for correlating traces with application logs
0 GitHub stars

使用场景

Debugging latency and performance bottlenecks in complex microservices environments
Analyzing error propagation and identifying failure points in backend services
Visualizing request flow and service dependencies across distributed systems