distributed-tracing

Distributed tracing provides end-to-end visibility into requests traveling across microservices, visualizing the request flow to break down latency and map service dependencies. It enables rapid incident diagnosis by pinpointing bottlenecks and failures within individual service spans.

You instrument Python, Node.js, and Go services with OpenTelemetry by installing language-specific SDKs to generate traces across your microservices. This implementation applies tracing to visualize request flow, manage context, and analyze performance hot spots.

Yes, you can configure Jaeger and Tempo as distributed tracing backends to aggregate trace data. They enable trace search, dashboards, and performance analysis to identify latency hot spots, failures, and service dependency mappings across your microservices architecture.

Trace propagation is the mechanism of passing trace context across service boundaries to correlate requests end-to-end. It is needed for microservices observability to maintain the parent-child relationship between spans as a request travels through multiple distinct services.

You configure trace sampling in OpenTelemetry to control the volume of trace data exported to your backends. This guidance helps manage overhead while retaining enough traces to identify errors, understand dependencies, and debug latency issues in production environments.