Observability for Modern Spring Applications
summary
Demo of a small Tea House app (three services, two MySQL DBs) shows concrete observability wiring: logs in Loki link via trace ID to Zipkin/Brave traces in Grafana, Micrometer metrics and Prometheus exemplars point to slow traces, and the Observation API unifies metrics/tracing. A 35 ms vs 440 ms trace comparison exposes a slow voter DB call; HikariCP/MySQL details and a Toxiproxy-injected latency reproduce the spike. Instrumentation uses Spring Boot (2/3), micrometer, Loki, Grafana and tracing adapters.
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chapters (24)
- 0:00Introduction and setup
- 1:28What is observability
- 3:14Why observability matters
- 6:07Demo application overview
- 7:51Debugging the tea service error
- 9:15Analyzing metrics and logs
- 10:59Jumping from logs to traces
- 13:08Understanding trace details
- 15:13Drilling down to root cause
- 17:40Fixing the database issue
- 18:46Verifying the fix
- 19:08Cross-signal navigation
- 21:14Jumping from traces to metrics
- 23:43Investigating latency spikes
- 24:45Introducing exemplars
- 26:10Using exemplars in Prometheus
- 29:23Database and network analysis
- 31:57Summary of navigation techniques
- 32:39Logging in Spring applications
- 34:46Metrics with Micrometer
- 37:14Distributed tracing options
- 38:20Micrometer Tracing
- 40:25Observation API explained
- 43:35Future of Micrometer
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