Measuring the Moving Parts of User Experience in Java/.NET applications
summary
Demo shows a working full‑stack APM: browser RUM uses bytecode tagging to follow users, server-side BTM instruments Java by dropping a war file onto WebLogic and begins yielding data in ~30 minutes, and traces reveal slow database queries causing page slowness. Key metrics are page load breakdown (request, network, server, download, render), JVM/CLR heap and GC timings, and the aptx = (satisfied + 0.5·tolerating)/samples formula for user satisfaction. Targets include the one second response rule and end‑to‑end monitoring across browser, app, DB, network.
AI-written summary · about
chapters (25)
- 0:00Intro
- 2:30Agenda
- 4:15Why is it terrible when a website is slow
- 5:20What metrics are important for monitoring applications
- 7:45Web application example
- 8:15Topdown approach
- 8:52Aptx
- 11:00Real User Monitor
- 13:30Visual User Monitor
- 14:50Business Transactions
- 15:15Business Transaction Monitoring
- 16:00Live Demo
- 17:30Real User Monitoring
- 18:50Database Monitoring
- 22:05More Slides
- 22:30Recap
- 23:50Response Time
- 25:38Interaction Elasticity
- 28:03Monitoring User Experience
- 29:51What Not to Do
- 31:42Summary
- 32:42Sales Pitch
- 33:42QA
- 35:13Conclusion
- 36:12Wrap Up
related talks
16mHome Brewing R.U.M - Analyzing application performance with real user monitoring
18mWebsite performance from user perspective
30mMeasuring Web Performance - Metrics & Tools
36mMoneyball for Performance Metrics
01h 04mTracing distributed services: experiences with implementing APM for the JVM
47mPractical Performance Analysis
19mOops my app performance sucks... what if observability could be a game changer?
35mWeb Application Performance Testing from Users Perspective