What tools does this skill support for tracing?

This skill provides implementation patterns for OpenTelemetry, Jaeger, and Grafana Tempo, supporting Python, Node.js, and Go applications.

How does this help with microservices debugging?

It allows you to track a single request as it travels through multiple services, making it easy to see exactly where a delay or error occurs in a complex system.

Is tracing expensive in terms of performance?

This skill includes sampling strategies (like probabilistic and rate-limiting sampling) to ensure monitoring overhead remains minimal, typically below 1% CPU impact.

Can I integrate these traces with my existing logs?

Yes, the skill includes best practices for log correlation, providing code examples to attach Trace IDs to your application logs for unified troubleshooting.

Does it support Kubernetes deployments?

Yes, it provides ready-to-use manifests for deploying Jaeger and Tempo within Kubernetes environments using Operators and ConfigMaps.

Distributed Tracing

Name: Distributed Tracing
Author: kivilaid

bykivilaid

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Analytics & Monitoring

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

About

Distributed Tracing provides a comprehensive framework for instrumenting microservices architectures to gain deep visibility into request flows. By integrating tools like Jaeger and Tempo via OpenTelemetry, this skill enables developers to map complex service dependencies, profile latency across distributed systems, and debug hard-to-trace failure points. It offers standardized implementation patterns for Python, Node.js, and Go, covering everything from infrastructure setup and context propagation to advanced sampling strategies and log correlation.

Key Features

6 GitHub stars
Multi-language instrumentation patterns for Python, Node.js, and Go
Infrastructure setup guides for Kubernetes and Docker Compose
Standardized OpenTelemetry integration for Jaeger and Grafana Tempo
Advanced sampling strategies including probabilistic and adaptive methods
Automated context propagation for cross-service request tracking

Use Cases

Debugging high latency and performance bottlenecks in microservices
Visualizing service dependencies and request paths in complex architectures
Correlating application logs with specific trace IDs for faster troubleshooting

About

Key Features

6 GitHub stars
Multi-language instrumentation patterns for Python, Node.js, and Go
Infrastructure setup guides for Kubernetes and Docker Compose
Standardized OpenTelemetry integration for Jaeger and Grafana Tempo
Advanced sampling strategies including probabilistic and adaptive methods
Automated context propagation for cross-service request tracking

Use Cases

Debugging high latency and performance bottlenecks in microservices
Visualizing service dependencies and request paths in complex architectures
Correlating application logs with specific trace IDs for faster troubleshooting