Interesting example of using Jupiter Notebook to visualize performance data gathered from Solaris operating system. In this particular case (real scenario) it allowed us to confirm the issue we were suspecting was related to Solaris scheduler interfering with processor pools and Solaris zones.
I find Jupyter Notebook useful to process, analyze and visualize performance data from operating system, database or any other source. Hopefully my time allows showing some examples of it soon. For now I’m describing how to prepare environment for such analysis – and it involves docker again.
My next post about YUM on Exadata is about creation of local ULN mirror containing Exadata baseline repositories together with generic Oracle Linux channel.
I cover a case when there is no Oracle Linux machine in the environment – so such mirror needs to be created on non-Oracle machine. Specifically I show possibility to use docker image for this purpose.