charts VS volcano

I did this using the incubator/raw chart ( https://github.com/helm/charts/tree/master/incubator/raw ), by creating a k8s Job.

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).

Also, if you are creating bare pods, this sounds like batch scheduling and you should consider using Jobs instead, to have a pod controller. And then you could also consider the https://volcano.sh/ scheduler if it has a fitting scheduling plugin for your use case.

For our experiments, we will use Volcano which is a batch scheduler for Kubernetes, well-suited for scheduling Spark applications pods with a better efficiency than the default kube-scheduler. The main reason is that Volcano allows "group scheduling" or "gang scheduling": while the default scheduler of Kubernetes schedules containers one by one, Volcano ensures that a gang of related containers (here, the Spark driver and its executors) can be scheduled at the same time. If for any reason it is not possible to deploy all the containers in a gang, Volcano will not schedule that gang. This article explains in more detail the reasons for using Volcano.