charts VS keda

I've installed Prometheus operator 0.34 (which works as expected) on cluster A (main prom)Now I want to use the federation option,I mean collect metrics from other Prometheus which is located on other K8S cluster B

I'm using the official stable/prometheus-operator chart do deploy Prometheus with helm.

Enter the default username: admin and password: prom-operator which you can find from here to access Grafana.

In this step opta generates terraform code to deploys helm chart. You can review the plan and accept it. It will take around 2-3 minutes for the plan to get applied. For specifying values to the helm chart you can modify the values inside opta-gcp/opta.yml . Refer to jenkins helm chart for more details https://github.com/helm/charts/tree/master/stable/jenkins. After opta apply finishes you will have jenkins up and running on your infra.

I do use Helm to deploy, but the trouble with using it for secrets is that it overwrites them on an upgrade.

In this section, you use Helm to deploy the Kubernetes Operator for Apache Spark from the incubator Chart repository. Helm is a package manager you can use to configure and deploy Kubernetes apps.

Redis: I have used Azure Cache for Redis, but feel free to explore other options e.g. you can install one in your Kubernetes cluster using a Helm chart).

To install Prometheus, the most convenient way is using the Prometheus Operator Helm Chart. Helm is a package manager for Kubernetes. It simplifies the installation by integrating most of the necessary configuration settings into one package. The following instructions are based on a blog post which describes the installation process specific for AKS clusters.

Maybe KEDA? https://keda.sh/

The HPA will use by default only basic metrics like CPU and memory. If you implement your own metrics, you might want to switch the autoscaling to use those metrics instead. To achieve this, the most recommended tool would be to rely on the KEDA controller. This grants you more advanced options for horizontal autoscaling. To use it, you should install it via Helm, and then you can use it by replacing the HorizontalPodAutoscaler object with their ScaledObject custom object.

New Release: KEDA 2.8.2

i highly recommend keda for this. https://keda.sh/

Quite some stuff in the new release: KEDA 2.9.0

This post demonstrates a proof-of-concept implementation that uses Kubernetes to execute code in response to an event here is API request. The workflow is powered by Keda (Kubernetes Event-driven Autoscaling) which scales out the kubernetes pods bases on incoming events such as SQS messages. After keda scaleout pods which are in pending state, Karpenter (Just-in-time Nodes for Any Kubernetes Cluster) bases on provisioners to decide scaleout more nodes

Dynamically scaling your containers is getting easier every day when using something like KEDA. I use Pulsar and there's a Pulsar scaler for it in addition to Kafka, Kinesis, Prometheus metrics, etc.

HPA is more commonly used and is the preferred option for situations with a sudden increase in resource usage. KEDA.sh can help flexibly autoscale the application based on custom metrics, like the size of a queue or number of network requests.

Practically, you'll be replacing stock k8s resources (deployments) with custom ones like Argo Rollouts with Keda autoscaling, so you have to plan the respective Gitops CD pipeline (fluxcd/argocd with some crossplane), as well.

Azure Container Apps is built upon the foundation consisting of open-source technology with CNCF projects like Kubernetes Event-Driven Autoscaling (KEDA), Distributed Application Runtime (Dapr), and Envoy running on the Azure Kubernetes Service (AKS).