Monitoring Microservices at Scale on OpenShift (OpenShift Commons Briefing #52)
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Microservices promise to increase time-to-market, support growth and foster innovation by enforcing Agile, product-centered and self-enabled teams. However, building a system of microservices that actually works is not an easy endeavour - after all, you're building a highly dynamic, distributed and fault-tolerant system. In this presentation I'll share important learnings around microservices and how to use the Dynatrace digital performance management platform on Red Hat's OpenShift to manage the inherent complexities of microservices-oriented architectures.
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Monitoring Microservices at Scale on OpenShift (OpenShift Commons Briefing #52)
- 1. Martin Etmajer, Technology Lead Monitoring Microservices at Scale on OpenShift with Dynatrace
- 3. Digital Performance Management for Biz, Devs and Ops Modernize Operations Optimize Customer Experience Accelerate Innovation
- 7. Velocity: Independent Development and Deployment
- 12. #1: Microservices are complex Lower inner vs. higher outer complexity
- 23. #2: Microservices don‘t fail independently Design for failure
- 25. Your application has failed :-( What’s affected? What’s the impact? What’s the root cause?
- 26. Confidential, Dynatrace LLC What’s the impact? What’s affected? What’s the root cause?
- 28. #3: The network isn‘t reliable
- 29. Tightly coupled. Really Distribute?
- 33. Option 1a: Dynatrace OneAgent for Full-Stack Monitoring
- 34. Option 1a: Dynatrace OneAgent for Full-Stack Monitoring
- 35. Option 1b: Dynatrace OneAgent for Full-Stack Monitoring
- 36. Option 1b: Dynatrace OneAgent for Full-Stack Monitoring
- 37. Confidential, Dynatrace LLC dynatrace/oneagent Red Hat Container Certified
- 38. Option 2: Dynatrace OneAgent for PaaS Monitoring
- 39. Option 2: Dynatrace OneAgent for PaaS Monitoring oc|s2i|Dockerfile
- 40. Dynatrace with OpenShift *
- 44. Outlook?
- 46. Don’t just optimize for speed…
- 47. …instead, release fast and with certainty. Identify bad code before it gets checked in. Collect performance metrics from automated tests. Feed back metrics into Jenkins and auto-stop bad builds.
- 48. 3 Key Takeaways: AAA Auto discovery Auto baselining Auto problem analysis
Editor's Notes
- Microservices look good on paper – but what do they look like in production?
- Don’t just optimize for speed – otherwise, you will taint your production environment with features of questionable valuable.
- Instead…