autoscaler VS keda

We use Cluster Autoscaler to automatically adjust the number of nodes (cluster size) based on your actual usage to ensure efficiency. Additionally, we deploy Vertical and Horizontal Pod Autoscalers to scale your applications' resources as their needs change automatically.

> * Hetzner: cheap, good service, the finest pets in the world, no cattle

You can absolutely do cattle with Hetzner. They support imaging and immutable infrastructure. They don't have a native auto scaling equivalent, but if you're using Kubernetes, they have a cluster autoscaler: https://github.com/kubernetes/autoscaler/blob/master/cluster...

Autoscaling over EKS can be accomplished using either the cluster-autoscaler project or Karpenter. If you want to use Spot instances, consider using Karpenter, as it has better integrations with AWS for optimizing spot pricing and availability, minimizing interruptions, and falling back to on-demand nodes if no spot instances are available.

Autoscaling is already provided on OVH, but we don't use it for now. Autoscaler has to be manually installed on the AWS/EKS cluster.

I've implemented a couple metacontroller operators for cdktf and cdk8s, with a custom cluster autoscaler cloud provider targetting abstract cdktf modules... Don't really like the idea of Crossplane, making a set of CRD's out of every TF provider is just nuts. While managing a set of dependent cdktf workspaces (to unravel multi-stage deployments due to infamous #2430), where CRD's only contain a map of tf variables, seams reasonable.

From the description it looks like Goldilocks only provides the dashboard. The actual recommendations come from the vertical pod autoscaler: https://github.com/kubernetes/autoscaler/tree/master/vertical-pod-autoscaler

Maybe Vertical Pod Autoscaler can help you https://github.com/kubernetes/autoscaler/tree/master/vertical-pod-autoscaler

The Horizontal Pod Autoscaler and Cluster Autoscaler are the standard ways that this is approached, but of course custom solutions are possible.

First, install the AWS Spot Termination Handler to ensure you gracefully handle terminations. Along with other standard things you should install like Cluster Autoscaler to handle failures/scale outs.

Microsoft does a good job with KEDA, providing an open source autoscaling architecture that isn't tied to Azure.

https://keda.sh/ - project website

Most just utilize out of the box macro resources available in HPA.

For more advanced use cases there is keda - https://keda.sh/

Thankfully KEDA operator was already part of the cluster, and all Robin had to do was create a ScaledObject manifest targeting the Dispatch ScaleUp event, based on the rabbitmq_global_messages_received_total metric from Prometheus.

Keda

KEDA introduces event-driven scaling to Kubernetes workloads. It integrates with Kubernetes Horizontal Pod Autoscalers and can scale pods based on external metrics from services like databases and message queues (Kafka, RabbitMQ, MongoDB).

This is where KEDA comes in.

# update version 2.8.2 if required kubectl apply -f https://github.com/kedacore/keda/releases/download/v2.8.2/keda-2.8.2.yaml

This is traditionally a AWS Lambda usecase - or an OpenFaas kind of usecase. But very recently i discovered https://keda.sh/ and it seems it is specifically meant for this in a kubernetes environment.

If you deploy the application to Amazon EKS, you can also consider using KEDA to auto-scale your consumer application based on the number of messages in the MSK topic.

Maybe this https://keda.sh/