Practical introduction to OpenTelemetry tracing
summary
Demonstration wires OpenTelemetry into a multi-language microservice demo using a OpenTelemetry agent for JVM auto-instrumentation, explicit SDKs for Rust/Python, and a local OpenTelemetry Collector feeding Jaeger via OTLP. Traces show caching eliminating downstream calls and reduce span count — one request generates about 25 spans before optimization. Asynchronous work carries MQTT message context by embedding trace metadata; manual instrumentation tags a product_id to detect mismatches. End-to-end tracing works but analytics replay and visualization need careful collector and UI configuration.
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chapters (16)
- 0:00Introduction and welcome
- 1:12Speaker background and experience
- 2:36From monitoring to observability
- 7:41What is tracing and its importance
- 9:05W3C Trace Context specification
- 11:53OpenTelemetry and its role
- 14:47Getting started with OpenTelemetry
- 18:01Demo introduction and setup
- 20:49Auto instrumentation with Java
- 21:30Python instrumentation demo
- 25:31Rust and Go instrumentation
- 27:19Manual instrumentation overview
- 29:04Adding manual spans in Java
- 31:17Manual instrumentation in Python
- 32:01Asynchronous messaging tracing
- 36:47Q&A and resources
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