distributed-tracing

Implement distributed tracing by configuring trace propagation using W3C TraceContext or B3 standards, then setting up a backend like Jaeger or Zipkin to collect and visualize the spans across your microservices.

Debug a slow API request by using distributed tracing to visualize the entire request flow across microservices, analyzing span relationships to pinpoint the exact service causing the latency bottleneck.

Yes, the distributed tracing setup supports backend integration for both Jaeger and Zipkin, providing configuration examples and Docker Compose snippets to start collecting traces.

Link distributed traces with logs and metrics by following correlation guidance that maps trace IDs to log entries and metric data points, ensuring unified observability across microservice architectures.

Choose a sampling strategy for microservice observability based on traffic volume and diagnostic needs, with support for head-based, tail-based, and adaptive sampling techniques to manage trace data collection.

Yes, understanding microservice communication patterns is required, as implementing trace propagation with W3C TraceContext standards relies on knowledge of how services pass context between each other.