Can I deploy the tracing backend to Kubernetes?

Yes, the skill includes detailed configurations for deploying Jaeger via Operator and Grafana Tempo using ConfigMaps and Deployments on Kubernetes.

How does distributed tracing help with debugging?

It creates a visual map of a request's journey, showing the duration of every service call and database query, allowing you to see exactly where a delay or error occurred.

Which programming languages are supported for instrumentation?

This skill provides specific implementation patterns and code snippets for Python (Flask), Node.js (Express), and Go using OpenTelemetry.

Does this skill help with log correlation?

Yes, it includes best practices for injecting Trace IDs into application logs to enable seamless navigation between traces and specific log entries.

What is the performance overhead of distributed tracing?

When using the recommended sampling strategies (typically 1-10% in production) and batch span processors, the CPU impact is usually less than 1%.

Distributed Tracing

Name: Distributed Tracing
Author: HermeticOrmus

byHermeticOrmus

Analíticas y Monitorización

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

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The Distributed Tracing skill provides a comprehensive framework for implementing observability within complex microservice architectures. By utilizing OpenTelemetry standards and tools like Jaeger and Tempo, it enables developers to visualize the entire lifecycle of a request as it traverses various services. This skill offers ready-to-use instrumentation patterns for Python, Node.js, and Go, alongside deployment configurations for Kubernetes and Docker. It is particularly valuable for teams needing to debug latency issues, map service dependencies, and pinpoint the exact origin of failures in distributed environments.

Características Principales

Trace-to-log correlation for deeper debugging
Advanced context propagation and span management
OpenTelemetry instrumentation for Python, Node.js, and Go
Jaeger and Tempo setup for Kubernetes and Docker Compose
Probabilistic and rate-limiting sampling strategies
0 GitHub stars

Casos de Uso

Debugging high latency and performance bottlenecks in microservices
Mapping complex service dependencies and request flows
Pinpointing error propagation across distributed system boundaries

Acerca de

Características Principales

Trace-to-log correlation for deeper debugging
Advanced context propagation and span management
OpenTelemetry instrumentation for Python, Node.js, and Go
Jaeger and Tempo setup for Kubernetes and Docker Compose
Probabilistic and rate-limiting sampling strategies
0 GitHub stars

Casos de Uso

Debugging high latency and performance bottlenecks in microservices
Mapping complex service dependencies and request flows
Pinpointing error propagation across distributed system boundaries