What is the difference between Jaeger and Tempo in this skill?

Jaeger is used for trace collection and visualization, while Grafana Tempo is featured as a high-scale, cost-effective storage alternative that integrates with S3.

Can I use this for production environments?

Yes, it includes production-grade best practices for sampling strategies and Kubernetes deployments via the Jaeger Operator with Elasticsearch storage.

How does distributed tracing help with debugging?

It allows you to visualize the entire journey of a request across different services, making it easy to see exactly where failures or delays occur in the stack.

What languages does this skill support for instrumentation?

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

Does it support log integration?

Yes, the skill includes techniques for correlating logs with trace IDs, allowing you to view relevant logs directly alongside specific trace spans.

Distributed Tracing & Observability

Name: Distributed Tracing & Observability
Author: carloscroc

bycarloscroc

Analíticas y Monitorización

Implement distributed tracing with Jaeger and Tempo to monitor microservices performance and visualize request flows across complex systems.

Acerca de

This skill provides a comprehensive framework for implementing distributed tracing across microservices architectures using industry-standard tools like Jaeger, Grafana Tempo, and OpenTelemetry. It enables developers to gain end-to-end visibility into request journeys, pinpoint performance bottlenecks, and understand complex service dependencies. By providing standardized instrumentation patterns for Python, Node.js, and Go, along with deployment configurations for Kubernetes and Docker, it serves as a complete toolkit for establishing robust observability and troubleshooting distributed system failures in production environments.

Características Principales

0 GitHub stars
Deployment configurations for Jaeger (Kubernetes Operator/Docker) and Grafana Tempo
Context propagation strategies for seamless cross-service request tracking
Log-to-trace correlation techniques for integrated debugging
Multi-language instrumentation patterns for Python, Node.js, and Go
Advanced sampling strategy optimization including probabilistic and rate-limiting

Casos de Uso

Visualizing service dependency graphs and failure propagation patterns
Pinpointing the root cause of high latency in complex microservice chains
Optimizing database query performance through granular span analysis

Acerca de

Características Principales

0 GitHub stars
Deployment configurations for Jaeger (Kubernetes Operator/Docker) and Grafana Tempo
Context propagation strategies for seamless cross-service request tracking
Log-to-trace correlation techniques for integrated debugging
Multi-language instrumentation patterns for Python, Node.js, and Go
Advanced sampling strategy optimization including probabilistic and rate-limiting

Casos de Uso

Visualizing service dependency graphs and failure propagation patterns
Pinpointing the root cause of high latency in complex microservice chains
Optimizing database query performance through granular span analysis