Implementing Metrics-Driven DevOps: Why and How
summary
Claims that CI/CD can stop bad builds early by extracting architectural metrics from tests and failing regressions. Shows Jenkins and Selenium hooked to app metrics, a dynaTrace-style pipeline with a pipeline state UFO and AWS deployment. A migration exposes an N+1 problem: 33 search results cause 171 SQL calls, spiking response from 0.52s to 2.6s then 25s and a 60% bounce; fix reduces calls and cost but barely moves users. Practical, tool-level examples and measurable thresholds.
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chapters (25)
- 0:00Intro
- 1:28How I prepared
- 1:59Metrics
- 3:10Pipeline State UFO
- 3:56Availability Monitoring
- 4:24User Experience Monitoring
- 5:53Performance
- 6:38Load Testing
- 8:24Examples
- 8:54Target
- 9:14CapitalOne
- 10:10Ecommerce
- 11:02Why Companies Fail
- 11:20Automating
- 11:41Rapid Deployment Model
- 12:40Sheer Pure Path
- 12:58Swedish Example
- 14:12Transaction Flow
- 15:34Transition
- 16:01Response Time
- 16:20What Went Wrong
- 18:15Good News
- 18:30Why
- 22:41MetricsDriven Pipeline
- 23:03Performance Heroes
