kubegres VS charts

A good comparison with other operators here [2022]: https://blog.palark.com/cloudnativepg-and-other-kubernetes-o...

Personally I've only used Kubegres (https://www.kubegres.io/), which didn't even make the above list. It's ok for a personal project.

All k8s solutions for postgres take subtly different approaches. It seems that they've all converged on the Operator pattern. The basics are easy: run a database process which persists data to a cloud disk of your choice. The hard parts are how to update, migrate, backup, restore, monitor, failover, replicate, etc. These kubernetes "operators" promise to fulfill the role of a DBA but, just like hiring a DBA, it requires buy-in to their approach.

Reactive-Tech / Kubegres (opérateur)

I like https://www.kubegres.io, it takes a little getting used to how it works. I probably wouldn't use it for a huge database, but for things under a few TB it works great.

I don't know if you're using Kubernetes, but Kubegres is great for self hosting postgres on a cluster.

Any experience with https://www.kubegres.io/ ?

How does this compare with something like kubegres?

https://www.kubegres.io/

https://www.kubegres.io/ has worked well for me. You can use custom images, provided they follow the conventions of the official postgres image. I haven't tried a major-version upgrade yet - my impression is that it will probably be a bit painful and involve some downtime and manual ops.

Some of the more advanced operators might better handle the upgrade problem? But in general, I've been happy with Kubegres for a ~10GB database with low traffic.

There are some great operators out there that make deployment and management easy. I use kubegres managed with pgadmin and it has been working well for me.

reactive-tech/kubegres (operator)

Both of these examples seem pretty obvious and something you wouldn’t mess up, but as your chart grows, so does your values.yaml file. A great example is the Redis chart by Bitnami. I encourage you to scroll through its values file. See you in a minute!

We will write a Terraform module that will take a list of configurations for each required RabbitMQ instance. Luckily for us, we don't have to write the Kubernetes yaml configurations since the helm charts by Bitnami does a great job of doing all the things we discussed above. All we need to do is leverage Terraform Helm Provider and deploy the chart with the required values for our use case.

💡  The full details of helm charts can be referenced in their associated GitHub Repository.

I have a configmap with the ndjson set up under data:, similar to https://github.com/bitnami/charts/issues/6159 and it's subsequent answer.

This way, you can easily deploy any Helm charts from this public repo - https://github.com/bitnami/charts/tree/main/bitnami in just minutes.

The Bitnami Github Pages themselves usually cover everything you need to know. Configure a values.yaml file, or modify that to your liking, and you run helm install, as written in their docs.

The actual reason that our pods are not coming up is found when we review the helm installation that we are trying to run. If you check the dependencies in the GitHub repository (https://github.com/bitnami/charts/blob/main/bitnami/drupal/values.yaml) you find out that persistent storage is enabled by default and set to 8Gi. Also, the helm package uses MariaDB and the database size is specified to a default of 8Gi, thus setting the minimum storage for this installation to be 16Gi.

We're using https://github.com/bitnami/charts/tree/main/bitnami/spark, but I have heard good things about https://github.com/GoogleCloudPlatform/spark-on-k8s-operator as well. Hudi should not need any long running deployments as per the docs https://hudi.apache.org/docs/0.5.1/deployment/#deploying

Bitnami has its own scaffolding published at https://github.com/bitnami/charts/tree/main/template