Super vision smart goggles
summary
Claims a move from slow monitoring to swift root cause analysis using Elastic APM agent to correlate traces logs and metrics. Describes instrumenting Java apps via a JVM premain agent toggled by environment variables, automatic log reformatting to JSON with correlation IDs, and live tracing across Kubernetes services. Manages 400+ custom applications; demo trace resolves to a concrete error table DB example doesn't exist. Argues this enables ML anomaly detection, proactive fixes, fewer outages and clearer cross‑team ownership.
AI-written summary · about
chapters (9)
- 0:00Observability challenges
- 2:01Applications at Chamber of Commerce
- 3:05Swift root cause analysis
- 6:19What is observability
- 7:43Instrumenting applications
- 13:01Improving customer experience
- 16:34Proactive monitoring with ML
- 17:20Team and governance benefits
- 19:07Conclusion & Q&A
related talks
20mSuper vision smart goggles
01h 04mTracing distributed services: experiences with implementing APM for the JVM
25mFrom Blind Spots to Total Vision: O11y at Massive Scale
19mOops my app performance sucks... what if observability could be a game changer?
52mIs your JVM app flying blind? Unmask hidden issues with Observability superpowers!
48mSetting up monitoring for Java apps
26mReal-Time Application Maps for Proactive and Actionable Visibility
37mThrough the looking glass: Effective observability for cloud native applications