Automated distributed tracing - a first class citizen of monitoring
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Automated distributed services tracing is key in microservices environments to uncover performance and functional issues. Dynatrace provides automated tracing as cloud native platform feature.
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Automated distributed tracing - a first class citizen of monitoring
- 1. Confidential, Dynatrace, LLC Automated distributed tracing – a first class citizen of monitoring Alois Mayr, Technology Lead at Dynatrace Innovation Lab, @mayralois May 23rd, 2017
- 2. Monitoring used to be about looking at dashboards
- 5. confidential A healthy environment is important … small, and bigones …
- 6. Confidential, Dynatrace, LLC Highly dynamic and distributed environments
- 7. Why automating tracing of distributed services?
- 8. Understanding deployment and microservices dependencies
- 9. Tightly coupled. Really Distribute?
- 10. confidential Analyze single requests Optimize performance of service call chains
- 11. confidential
- 12. confidential Who owns monitoring in a cloud native world?
- 13. Confidential, Dynatrace, LLC What does health mean for App teams vs. Cloud Ops Microservices Team A Microservices Team B Microservices Team X Dev Test/QA Prod Cloud Ops Team Application service insights, performance, failures, scalability, interoperability Platform health, capacity planning, Monitoring as a Service Dependencies, impacts, architecture, end-user-experience
- 14. Confidential, Dynatrace, LLC Monitoring as platform feature host Deploy one agent per host (kubectl create, bosh deploy) Auto instrumentation of containerized microservices Auto distributed transaction tracing AI based root cause analytics Cloud Ops teams own the platform
- 15. Confidential, Dynatrace, LLC Container health != service health != application health Container health Microservices health Application health IaaS health
- 16. Confidential, Dynatrace, LLC Container health != service health != application health Container health Microservices health Application health IaaS health Chaos Monkey
- 17. Confidential, Dynatrace, LLC Container health != service health != application health Container health Microservices health Application health IaaS health docker stats
- 18. Confidential, Dynatrace, LLC Container health != service health != application health Container health Microservices health Application health IaaS health
- 19. Confidential, Dynatrace, LLC Container health != service health != application health Container health Microservices health Application health IaaS health
Editor's Notes
- monitroing used to be about looking at dashboard. You developed you application, deployed itin our enviornment either on-premise in your DC or in the cloud on AWS for example. And than you watched if anything happened or if any set threshold throw an error message. But modern monitoring should go beyond that…
- and to make this a bit more intuitive, lets launch Dynatrace. In that Demo environment we are simulating to have multiple applications running, incl. customer facing ones. Have of the applications run in the DC, have of them in AWS. So what Dynatace does, it provides you an full stack overview of your environment – starting from the underlying infrastructure up to the real user monitoring. We show you all dependencies within your stack, with SmartScape we basically auto-discovery your whole environment. Now assuming you just start your cloud migration journey, having SmartScape in place makes the whole profiling and discovery phase very ease. But simple installing our agent in the environment your AS-IS landscape will be created –out of the box – within less than 5 minutes. Now you can use this information to start the actual migration process – whatever strategy you are following (lift and shift, refactoring, replatforming) and as soon as you have migrated the workload you need anyway a cloud native monitoring solution. Product demo – lets check this out in the real product: Assume we have following scenario. Have of the applications are already running on AWS, half ot them in DC. SmartScape – full stack, auto dependencies, auto discovery, Cloud Migration Use Case – how to support those
- The typical life of a container is minutes and hours and not necessarily days. Given this kind of environment, it becomes difficult to monitor the applications as the number of nodes are ever changing. As enterprises migrate applications from monolithic to microservices and use containers to support them, the number of containers supporting these microservices explode over time to 100,000s of node. A typical monitoring is unable to handle the amount of data generated by these 1000s of nodes.
- monitroing used to be about looking at dashboard. You developed you application, deployed itin our enviornment either on-premise in your DC or in the cloud on AWS for example. And than you watched if anything happened or if any set threshold throw an error message. But modern monitoring should go beyond that…
- The typical life of a container is minutes and hours and not necessarily days. Given this kind of environment, it becomes difficult to monitor the applications as the number of nodes are ever changing. As enterprises migrate applications from monolithic to microservices and use containers to support them, the number of containers supporting these microservices explode over time to 100,000s of node. A typical monitoring is unable to handle the amount of data generated by these 1000s of nodes.
- The typical life of a container is minutes and hours and not necessarily days. Given this kind of environment, it becomes difficult to monitor the applications as the number of nodes are ever changing. As enterprises migrate applications from monolithic to microservices and use containers to support them, the number of containers supporting these microservices explode over time to 100,000s of node. A typical monitoring is unable to handle the amount of data generated by these 1000s of nodes.
- The typical life of a container is minutes and hours and not necessarily days. Given this kind of environment, it becomes difficult to monitor the applications as the number of nodes are ever changing. As enterprises migrate applications from monolithic to microservices and use containers to support them, the number of containers supporting these microservices explode over time to 100,000s of node. A typical monitoring is unable to handle the amount of data generated by these 1000s of nodes.
- The typical life of a container is minutes and hours and not necessarily days. Given this kind of environment, it becomes difficult to monitor the applications as the number of nodes are ever changing. As enterprises migrate applications from monolithic to microservices and use containers to support them, the number of containers supporting these microservices explode over time to 100,000s of node. A typical monitoring is unable to handle the amount of data generated by these 1000s of nodes.
- The typical life of a container is minutes and hours and not necessarily days. Given this kind of environment, it becomes difficult to monitor the applications as the number of nodes are ever changing. As enterprises migrate applications from monolithic to microservices and use containers to support them, the number of containers supporting these microservices explode over time to 100,000s of node. A typical monitoring is unable to handle the amount of data generated by these 1000s of nodes.