Can I correlate logs with my traces?

Yes, it includes specific code patterns for injecting trace IDs into application logs to enable seamless jumping between traces and log entries.

Does this support the OpenTelemetry standard?

Yes, it focuses on OpenTelemetry (OTel) for instrumentation, ensuring compatibility with most modern observability stacks and vendors.

Which tracing backends are supported by this skill?

The skill provides detailed configuration and deployment patterns for both Jaeger and Grafana Tempo backends.

How does this help with performance optimization?

It provides specific Jaeger queries and analysis patterns to identify slow requests, database bottlenecks, and service-to-service latency.

What is the expected performance overhead of tracing?

The skill includes best practices for sampling (typically 1-10%) and batch processing to keep performance impact below 1% CPU overhead.

Distributed Tracing & Observability

Name: Distributed Tracing & Observability
Author: HermeticOrmus

byHermeticOrmus

분석 및 모니터링

Implements end-to-end request tracking across microservices using Jaeger and Tempo to identify performance bottlenecks and debug complex distributed systems.

소개

This skill provides comprehensive patterns and configurations for implementing distributed tracing using industry-standard tools like Jaeger, Grafana Tempo, and OpenTelemetry. It enables developers to gain deep visibility into request lifecycles, service dependencies, and error propagation across Python, Node.js, and Go environments. By providing standardized infrastructure-as-code snippets for Kubernetes and Docker, along with sophisticated sampling strategies, this skill helps teams reduce Mean Time to Resolution (MTTR) and optimize system performance without significant overhead.

주요 기능

Advanced sampling strategies including probabilistic and rate-limiting
OpenTelemetry-compliant context propagation and span management
Trace-to-log correlation workflows for enhanced root cause analysis
Multi-language instrumentation patterns for Python, Node.js, and Go
Infrastructure setup for Jaeger and Grafana Tempo backends
0 GitHub stars

사용 사례

Mapping service dependencies and analyzing request flow paths
Debugging high latency in complex microservice architectures
Performing root cause analysis for errors propagating across distributed boundaries

소개

주요 기능

Advanced sampling strategies including probabilistic and rate-limiting
OpenTelemetry-compliant context propagation and span management
Trace-to-log correlation workflows for enhanced root cause analysis
Multi-language instrumentation patterns for Python, Node.js, and Go
Infrastructure setup for Jaeger and Grafana Tempo backends
0 GitHub stars

사용 사례

Mapping service dependencies and analyzing request flow paths
Debugging high latency in complex microservice architectures
Performing root cause analysis for errors propagating across distributed boundaries