distributed-tracing

Distributed tracing automatically tracks request paths through your microservice architecture to identify which services cause delays. Instrument your services with OpenTelemetry, deploy a tracing backend like Jaeger or Tempo, and visualize the complete request flow with latency data for each span.

Deploy a tracing backend (Jaeger or Tempo) on Kubernetes, instrument your applications with OpenTelemetry SDKs to emit spans, and configure context propagation across service boundaries. This provides end-to-end visibility into request paths and service dependencies.

Yes. Instrument Flask with OpenTelemetry, configure it to send traces to Jaeger or Tempo backends, and propagate trace context across HTTP requests. This enables you to see how requests flow through your Flask services and identify performance bottlenecks.

Distributed tracing maps the call graph by recording which services invoke each other during a request. By analyzing span relationships and timing data, you can visualize service dependencies, detect circular dependencies, and understand your architecture's actual topology.

Distributed tracing backends like Jaeger and Tempo run on both Docker Compose and Kubernetes. Choose Docker Compose for local development and testing, Kubernetes for production deployments requiring high availability and scalability.

Distributed tracing captures errors and exceptions as span events and attributes, then propagates trace context headers across service calls. This preserves the error chain so you can follow failures from the entry point through all downstream services to pinpoint root causes.