The basic operator idea comes from the SRE trenches. The idea is that any time a human touches a system (besides a crisis) , then it is a bug in the system design.
So an operator is a robot that performs the normal system duties. Currently, those can be backup, auto-scaling, etc. The "instructions" to this robot are declarative. I.e. the user specifies the desired state of the world (for example, in a yaml file) , and the robot makes the actual state equal to the desired state.
The best way to learn how to create operators is to look at existing operator code.
The basic operator idea comes from the SRE trenches. The idea is that any time a human touches a system (besides a crisis) , then it is a bug in the system design.
So an operator is a robot that performs the normal system duties. Currently, those can be backup, auto-scaling, etc. The "instructions" to this robot are declarative. I.e. the user specifies the desired state of the world (for example, in a yaml file) , and the robot makes the actual state equal to the desired state.
The best way to learn how to create operators is to look at existing operator code.
My preference are knative (https://knative.dev/) and Agones (https://agones.dev/site/)
For a book I would recommend:
https://learning.oreilly.com/library/view/programming-kubern...
Later this month, I hope to release a new AutoML platform which has those robots for all stages of the ML pipeline.