This Python library is a high-level set of Kubernetes Helpers allowing either to manage individual standard Kubernetes controllers (Deployment, StatefulSets, etc)
or a logical set of standard Kubernetes controllers through the expecteddeploymentscales.wiremind.io CRD (for example allowing to scale down ALL Deployments of a Helm Release marked as such).
This library requires the expecteddeploymentscales.wiremind.io CRD:
helm repo add wiremind https://wiremind.github.io/wiremind-helm-charts
helm install wiremind-crds wiremind/wiremind-crds --version 0.1.0See examples of usage in Wiremind Chartreuse tool.
The expecteddeploymentscales.wiremind.io custom resource definition defines a priority field,
Allowing to control the priority of the Deployment under control for scale down order.
wiremind-kubernetes will, for each different priority found for all considered ExpectedDeploymentScale, scale down all deployments with a given priority, then wait for their pods to be actually deleted, then continue with the next priority (in descending order).
Higher priority means that the Deployment will be scaled down before others with lower priority.
KubernetesDeploymentManager.stop_pods() will use that definition.
In case of some Pods being slow to be deleted and others serving web server, this allows for less downtime for users (since we can state to delete first those slow-to-be-deleted pods, then stop the web server pods).
- The diagram has been drawn using the free online software https://draw.io, the
source code is located at
documentation/schema-stop-pods.png, feel free to correct it or make it more understandable.
KubernetesDeploymentManager.stop_pods() neutralizes HPA managing the Deployments if any, as HPAScaleToZero
(see here) may be in use (HPA may scale up the Deployment even if its replicas=0),
a more straightforward solution will be available in the future see
here. Of course KubernetesDeploymentManager.start_pods() repairs it.
It is encouraged to run this to re-scale up.
See this PR for more details.