Is OpenTelemetry required for this skill?

OpenTelemetry is the recommended standard used in this skill because it provides a vendor-neutral way to collect and export telemetry data to backends like Jaeger.

Can I use this with Kubernetes?

Yes, the skill includes specific deployment manifests for Jaeger Operators and Grafana Tempo configurations tailored for Kubernetes environments.

Which languages are supported for instrumentation?

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

Does tracing impact application performance?

The skill includes best practices for sampling (typically 1-10%) and using batch span processors to ensure tracing overhead remains below 1% CPU impact.

How does it help with debugging errors?

It allows you to search for specific trace IDs or filter by status codes (e.g., >= 500) to visualize exactly where a request failed in a multi-service chain.

Distributed Tracing & Observability

Name: Distributed Tracing & Observability
Author: rmyndharis

byrmyndharis

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504

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分析与监控

Implements end-to-end request tracking using Jaeger and Tempo to monitor microservice performance and resolve latency bottlenecks.

This skill provides a comprehensive framework for implementing distributed tracing across microservices using industry-standard tools like Jaeger, Tempo, and OpenTelemetry. It enables developers to visualize request flows, identify service dependencies, and pinpoint the exact source of latency or failures in complex distributed systems. With ready-to-use instrumentation patterns for Python, Node.js, and Go, it streamlines the process of adding observability to modern cloud-native applications, ensuring robust performance monitoring and faster debugging through correlated traces and logs.

主要功能

01Automated context propagation across HTTP boundaries

02504 GitHub stars

03Multi-language instrumentation for Python, Node.js, and Go using OpenTelemetry

04Production-ready deployment configurations for Jaeger and Grafana Tempo

05Configurable sampling strategies to balance visibility with performance overhead

06Deep integration with logging for correlated trace-log analysis

使用场景

01Visualizing complex service-to-service dependency graphs

02Debugging high-latency requests in microservice architectures

03Identifying the specific component causing error propagation in a distributed system

主要功能

01Automated context propagation across HTTP boundaries

02504 GitHub stars

03Multi-language instrumentation for Python, Node.js, and Go using OpenTelemetry

04Production-ready deployment configurations for Jaeger and Grafana Tempo

05Configurable sampling strategies to balance visibility with performance overhead

06Deep integration with logging for correlated trace-log analysis

使用场景

01Visualizing complex service-to-service dependency graphs

02Debugging high-latency requests in microservice architectures

03Identifying the specific component causing error propagation in a distributed system