distributed-tracing

Distributed tracing implements request monitoring across microservices using Jaeger and Tempo to visualize request flows and pinpoint latency bottlenecks. It tracks single requests as they traverse services, databases, and gateways to identify exactly where operations slow down.

Distributed tracing provides end-to-end visibility into request lifecycles across microservice architectures. You need it to debug complex distributed systems by mapping service dependencies, analyzing performance, and observing how errors propagate through the call chain.

Yes, you can configure OpenTelemetry instrumentation for a Python Flask application using this distributed tracing skill. It facilitates integration with OpenTelemetry SDKs to monitor request lifecycles and export trace data to backends like Jaeger and Tempo.

Yes, you need backend storage providers like Elasticsearch or S3 to store distributed tracing data. The monitoring of request lifecycles using Jaeger and Tempo requires these storage providers to retain and query the collected trace information.

Trace the root cause of failures by observing how errors propagate through the service call chain using distributed tracing. It provides visibility into request paths across microservices, allowing you to track error propagation from origin to gateway.