opentelemetry

To instrument a Python service for distributed tracing and metrics, you apply standardized OpenTelemetry libraries to generate traces, metrics, and logs, then export them via the OTLP protocol for centralized analysis.

Correlating slow API requests with specific service calls requires distributed tracing to propagate context across services, linking traces with associated logs to pinpoint latency sources and errors.

Context propagation in OpenTelemetry works by passing trace context metadata across service boundaries, ensuring requests can be traced continuously through multiple services within a distributed system.

You use an OpenTelemetry Collector to facilitate vendor-neutral data export and processing, configuring a centralized pipeline to manage telemetry before analysis, though direct OTLP export is also supported.

Managing metric cardinality requires careful instrumentation to limit high-dimensional label combinations, ensuring efficient troubleshooting and performance monitoring without overwhelming the telemetry pipeline.

Adhering to semantic conventions for logging and tracing standardizes telemetry data formats, enabling consistent context propagation and unified analysis across different services within a distributed system pipeline.