How to leverage Kubernetes operators using the Operator SDK framework

Kubernetes has become an omnipresent platform to host cloud-native applications. As a rather low-level platform, it is often made developer-friendly by wrapping it into higher-level platforms, such as OpenShift (OKD), and by turning it into a managed service platform, such as APPUiO, which can be deployed to any cloud infrastructure. Application engineers interact with Kubernetes mostly by authoring appropriate deployment descriptors and by pushing their code which triggers deployments. Due to ongoing feature additions, not so much is known about useful combinations of annotations on Kubernetes deployments (and other declaratively described objects), Kubernetes operators (a kind of hooks) and custom resources definitions.
In this blog post series, we share some of the experience we have gained while researching how to trigger actions upon certain updates to the descriptors, as a precursor to dynamic and autonomous feedback loops which can self-manage application deployments.
In particular, we provide access to the adapted original examples of operators generated with the Operator SDK toolkit, which deal with Kubernetes resources by combining annotations on Kubernetes deployments and Kubernetes operators concepts. The link to our operators examples are available on Github: https://github.com/appuio/operator-sdk-examples. In further blog posts we will describe some, discussing also how they could be extended for more advanced decision making. In particular, adapting the (Go) operators to work on different environments require to modify some important go files (e.g., pkg/controller/memcached/memcached_controller.go as shown in the following Figure).

IN FURTHER BLOG POSTS WE WILL TALK ABOUT:

Section 1 – Kubernetes Operators, Operator Framework, and Operators SDK

• Here we discuss in a general setting about Operators, Operator Framework, and Operators SDK.
• Then we will discuss about the Operators SDK emerging popularity in GitHub, and in general about the “Operator SDK workflow” adopted for generating and handling operators.

Section 2 – Supported Kubernetes Operator SDK workflows

• Here we discuss about the three available alternative workflows to generate Operators provided by the last versions of Operator SDK APIs.
  
• We also discuss pros and cons of using the various operators workflows.
  

Section 3 – Examples of Supported Kubernetes Operator SDK workflows

• Here we provide examples about the three available alternative workflows to generate Operators provided by the Operator SDK APIs.
• We specifically focus on Go operators, as they are in our opinion the more stable available APIs.

Section 4 – Example(s) of Operator(s) Monitoring the Service with the usage of Prometheus (coming soon)

• Here we provide an example of an operator that communicates with Prometheus (currently used to monitor Kubernetes Clusters) for more advanced decision making (e.g., advanced monitoring of the service).

About the authors

These blog posts have been written by Dr. Josef Spillner and Dr. Sebastiano Panichella from ZHAW (Zurich University of Applied Sciences) School of Engineering. Thank you very much Josef and Sebastiano for sharing your know how with our readers!