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FerretDB Compute Resource Autoscaling

This guide will give an overview on how KubeDB Autoscaler operator autoscales the database compute resources i.e. cpu and memory using FerretdbAutoscaler crd.

Before You Begin

• You should be familiar with the following KubeDB concepts:
  • FerretDB
  • FerretDBAutoscaler
  • FerretDBOpsRequest

How Compute Autoscaling Works

The following diagram shows how KubeDB Autoscaler operator autoscales the resources of FerretDB. Open the image in a new tab to see the enlarged version.

  

The Auto Scaling process consists of the following steps:

1. At first, a user creates a FerretDB Custom Resource Object (CRO).

2. KubeDB Provisioner operator watches the FerretDB CRO.

3. When the operator finds a FerretDB CRO, it creates PetSet and related necessary stuff like secrets, services, etc.

4. Then, in order to set up autoscaling of FerretDB, the user creates a FerretDBAutoscaler CRO with desired configuration.

5. KubeDB Autoscaler operator watches the FerretDBAutoscaler CRO.

6. KubeDB Autoscaler operator generates recommendation using the modified version of kubernetes official recommender for different components of the database, as specified in the FerretDBAutoscaler CRO.

7. If the generated recommendation doesn’t match the current resources of the database, then KubeDB Autoscaler operator creates a FerretDBOpsRequest CRO to scale the ferretdb to match the recommendation generated.

8. KubeDB Ops-manager operator watches the FerretDBOpsRequest CRO.

9. Then the KubeDB Ops-manager operator will scale the ferretdb vertically as specified on the FerretDBOpsRequest CRO.

In the next docs, we are going to show a step-by-step guide on Autoscaling of FerretDB using FerretDBAutoscaler CRD.