How does distributed tracing help with debugging?

It assigns a unique ID to every request, allowing you to track that request as it passes through multiple services, pinpointing exactly which service or database call is causing a delay or error.

Can I use this with existing Kubernetes clusters?

Absolutely. The skill includes Kubernetes manifests for the Jaeger Operator and Tempo deployments specifically designed for cloud-native environments.

What is the performance impact of adding tracing?

When using the recommended sampling strategies (like 1% production sampling) and batch span processors included in this skill, the CPU overhead is typically less than 1%.

Does this skill support OpenTelemetry standards?

Yes, it focuses on OpenTelemetry (OTel) as the primary instrumentation standard, ensuring compatibility with most modern observability backends.

Distributed Tracing Implementation

Name: Distributed Tracing Implementation
Author: nguyendinhquocx

bynguyendinhquocx

0•

Analíticas y Monitorización

Implements end-to-end request tracking across microservices using Jaeger and Tempo to identify and resolve performance bottlenecks.

This skill provides a comprehensive framework for implementing distributed tracing within complex microservices architectures. It offers production-ready deployment configurations for Jaeger and Grafana Tempo using Kubernetes and Docker Compose, alongside detailed instrumentation patterns for Python, Node.js, and Go via OpenTelemetry. By leveraging this skill, developers can gain deep visibility into request flows, automate context propagation, and utilize advanced sampling strategies to monitor distributed systems with minimal performance overhead.

Características Principales

01Multi-language OpenTelemetry instrumentation patterns for Python, Node.js, and Go

02Advanced sampling strategies including Probabilistic, Rate Limiting, and Adaptive sampling

03Production-grade Kubernetes manifests and Docker Compose setup for Jaeger and Tempo

04Integration patterns for correlated logging and service dependency mapping

05Automated context propagation for HTTP headers and service boundaries

060 GitHub stars

Casos de Uso

01Root cause analysis for error propagation in distributed architectures

02Debugging high-latency requests across complex microservices environments

03Visualizing request paths to identify hidden service dependencies and bottlenecks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nguyendinhquocx/code-ai distributed-tracing

For use in Claude.ai and ChatGPT

Características Principales

01Multi-language OpenTelemetry instrumentation patterns for Python, Node.js, and Go

02Advanced sampling strategies including Probabilistic, Rate Limiting, and Adaptive sampling

03Production-grade Kubernetes manifests and Docker Compose setup for Jaeger and Tempo

04Integration patterns for correlated logging and service dependency mapping

05Automated context propagation for HTTP headers and service boundaries

060 GitHub stars

Casos de Uso

01Root cause analysis for error propagation in distributed architectures

02Debugging high-latency requests across complex microservices environments

03Visualizing request paths to identify hidden service dependencies and bottlenecks