Can I use this for production environments?

Absolutely. It follows best practices such as sampling (1-10%), log correlation, and minimal overhead monitoring designed for production reliability.

How does it help with microservice performance?

It guides you through creating spans and propagating context so you can see the exact duration of a request as it moves through different services.

Does this skill provide help with Jaeger queries?

Yes, it includes optimized query syntax for finding slow requests, identifying error-prone services, and filtering traces by tags.

What languages does this distributed tracing skill support?

The skill provides specific implementation patterns and code snippets for Python and Node.js using the OpenTelemetry SDK.

Distributed Tracing & Observability

Name: Distributed Tracing & Observability
Author: eyadsibai

byeyadsibai

Analíticas y Monitorización

Implements request flow visibility across microservices using OpenTelemetry, Jaeger, and Tempo to identify latency bottlenecks.

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Enhance your application's observability with standardized distributed tracing patterns for microservice architectures. This skill provides expert guidance on instrumenting Python and Node.js applications with OpenTelemetry, configuring Jaeger and Tempo exporters, and managing complex trace contexts. Whether you are debugging latency issues, correlating logs with traces, or implementing custom sampling strategies, this tool ensures consistent, production-grade observability across your entire distributed infrastructure.

Características Principales

0 GitHub stars
Configurable sampling strategies including probabilistic and rate-limiting
Correlation patterns between traces and application logs
Standardized OpenTelemetry instrumentation for Python and Node.js
Distributed context propagation across service boundaries
Jaeger and Tempo integration for end-to-end request visualization

Casos de Uso

Debugging high latency and performance bottlenecks in microservice request chains
Monitoring database and external API performance within application spans
Identifying specific points of failure and error propagation in distributed systems

Acerca de

Características Principales

0 GitHub stars
Configurable sampling strategies including probabilistic and rate-limiting
Correlation patterns between traces and application logs
Standardized OpenTelemetry instrumentation for Python and Node.js
Distributed context propagation across service boundaries
Jaeger and Tempo integration for end-to-end request visualization

Casos de Uso

Debugging high latency and performance bottlenecks in microservice request chains
Monitoring database and external API performance within application spans
Identifying specific points of failure and error propagation in distributed systems