volcano VS helm

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.

Update after some digging:According to Subcharts and Globals the templates are shared between charts.

But that’s not all! As we’ve decomposed, ported, and reborn our PaaS as a Kubernetes citizen; we found a need for tools to help manage all of the ephemera that comes along with building and running Kubernetes-native applications. The result has been open sourced as Helm and we’re excited to see increasing adoption and growing excitement around the project.

Helm (label: good first issue) The Kubernetes Package Manager

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.