Leveraging Distributed Tracing in Kubernetes & Containerized Environments
summary
Distributed tracing plus auto-instrumentation in Kubernetes and containers claims to cut noise by building service maps and letting teams correlate metrics logs and traces so they focus only on the microservices in a request path. The demo traces a kafka → dynamodb flow, surfaces a missing username and a missing key id, and pinpoints an expired json token (recommend refresh tokens on Azure). Note: three quarters of issues reach customers first and downtime costs roughly $8,000 per minute.
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chapters (11)
- 0:00Welcome
- 1:02Microservices challenges
- 3:48Observability challenges
- 4:30Traditional APM limits
- 5:55Metrics and logs limits
- 7:42Distributed tracing solution
- 8:46Engineering flywheel
- 9:47Service maps benefits
- 11:11Metrics traces logs correlation
- 12:15Debugging with tracing
- 13:17Recap and key takeaways
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