Practical introduction to OpenTelemetry tracing
summary
Explains why tracing matters, shows a Docker Compose demo that wires Flask, Spring Boot (Kotlin), an API gateway (Apache APISIX), and a Jaeger image to prove traces flow end to end, and reports spans rising from ~20 to ~27 after adding manual annotations. Uses auto instrumentation for JVM and Python, adds manual spans via SDK/annotations, sends data to an OpenTelemetry collector, and inspects calls and attributes in the Jaeger UI.
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chapters (15)
- 0:00From dashboards to observability
- 1:27Three pillars of observability
- 3:14Logging best practices
- 6:07What is distributed tracing
- 7:55W3C Trace Context
- 9:24OpenTelemetry introduction
- 11:40Auto vs manual instrumentation
- 13:07Demo setup and architecture
- 15:14Starting the OpenTelemetry demo
- 19:57Analyzing traces in Jaeger
- 21:21Custom attributes in API Gateway
- 23:30Manual instrumentation
- 26:26Impact of manual instrumentation
- 27:34Key takeaways for devs
- 29:08Q&A
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