distributed-tracing

Distributed Tracing Skill (Distributed Tracing)

Overview

Implement distributed tracing using Jaeger and Tempo to visualize request traces in a microservices environment, locate performance bottlenecks, and analyze service dependencies.

Use Cases

Debugging microservice performance issues

When user requests are responding slowly, trace the full request path to identify which service or database operation is causing the latency.

Analyzing inter-service call relationships

In complex microservice architectures, automatically generate service dependency graphs to understand call frequency and success/failure rates between services.

Tracing error propagation paths

When a request fails, quickly locate the service where the error first occurred and analyze how the error propagates across upstream/downstream services.

Core Features

Multi-platform tracing deployment

Supports the two mainstream distributed tracing platforms Jaeger and Tempo, and provides multiple deployment options such as Kubernetes and Docker Compose.

Multi-language application integration

Provides OpenTelemetry integration code examples for mainstream languages like Python (Flask), Node.js (Express), and Go to enable automatic tracing.

Trace data analysis

Supports filtering trace data by service name, duration, error tags, etc., to quickly locate problematic requests.

Frequently Asked Questions

How does distributed tracing differ from traditional log monitoring?

Traditional log monitoring can only show execution details inside a single service; when a request passes through multiple services, it is difficult to correlate logs across services. Distributed tracing links the entire request path by propagating trace context (traceparent) between services, allowing you to clearly see the time and status of each step.

What sampling rate is appropriate for production?

It is recommended to set the production sampling rate to around 1% so you retain enough problem samples while controlling storage costs and performance overhead. High-traffic or critical paths can be sampled lower, while low-traffic or test environments can be set to 100%.

Will tracing affect application performance?

A properly configured tracing system has minimal performance impact, typically keeping CPU overhead under 1%. Key measures include: using batch span processors, reporting tracing data asynchronously, and setting an appropriate sampling rate.