Unmask hidden issues with Observability superpowers!
summary
Live demo uses Spring Boot with Micrometer Observability, Prometheus, Grafana, Tempo/Zipkin and Loki to triage two real faults: a 404 bug fixed by inserting a missing DB row (found by grabbing a log with the trace id and inspecting the trace) and a latency spike traced to an injected 100ms via toxiproxy (confirmed by exemplars correlating metric points to traces and JDBC/Hikari metrics). Instrumentation relies on observation handlers, conventions and generated docs, and an SSE music demo maps request outcomes to sound.
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chapters (24)
- 0:00Introduction and setup
- 1:57What is observability?
- 3:28Demo: T service failure
- 4:31Using metrics to detect issues
- 6:36Jumping from metrics to logs
- 8:00Correlating logs with traces
- 10:51Fixing the English breakfast error
- 12:36Diagnosing high latency
- 14:01Using exemplars for correlation
- 16:32Micrometer observability explained
- 22:00Fixing the latency issue
- 22:22Micrometer setup in Spring Boot
- 26:18Manual vs automatic instrumentation
- 31:02Observation handlers and context
- 35:15Generating documentation from observations
- 38:27Turning observability into music
- 41:43Summary and key takeaways
- 44:14Q&A: Performance impact of tracing
- 46:19Q&A: Spring Boot version compatibility
- 47:02Q&A: Accessing the demo code
- 47:24Q&A: Logger configuration for Loki
- 48:48Q&A: Log volume and cost management
- 50:15Q&A: Grafana stack alternatives
- 50:55Q&A: Trace sampling strategies
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