Creating a Helm Repo with Github

Next step in learning helm is being able to take an existing helm package and put it in your own repo.

There are ways to do this with github pages. I don’t really want mess withthat right now, how can I use a Github repo to host my changes to the deployment?

For installing helm and an additional demo please see part 1 of this series.

https://blog.2vcps.io/2018/03/27/getting-started-with-helm-for-k8s/

Continue reading “Creating a Helm Repo with Github”

Getting Started with Helm for K8s

Over the last few weeks I was setting up Kubernetes in the lab. One thing I quickly learned was managing and editing yaml files for deployments, services and persistent volume claims became confusing and hard. Even when I had things commited in github sometimes I would make edits then not push them then rebuild my K8s cluster.

The last straw was when 2 of our Pure developers said that editing yaml in vi wasn’t very cool and to start using helm.

Needless to say that was good advice. I still have to remember to push my repos to github. Now my demostration applications are more “cloud native”. I can create and edit them in one environment and use helm install in another and have it just work.

Continue reading “Getting Started with Helm for K8s”

Using Snapshots with the Pure Storage Plugin for Kubernetes

One request from customers is not only provision persistent storage for Kubernetes but also integrate into workflows that may need to snap and copy the data for different environments. Much like we do this with powershell or python for SQL and Oracle environments to accelerate development or QA. Pure has enabled snapshots using the Pure Provisioner as part of our Kubernetes Plugin.

In this demo I am showing how I can take a users data directory for JupyterHub and clone it for another user to take advantage of all the benefits of Pure’s snapshots and clones. You instantly get access to a copy of the dataset. The dataset doesn’t take up room on the backend storage. Only globally unique changes will grow the volume. In this use case the Data Science team will see increases in productivity as they are not waiting for data to download from the cloud or copy from another place on the array.

The command to run the snap using kubectl is below:

kubectl exec <pure provisioner pod name> -- snapshot create -n <namespace> <pvc-claim-name>

Kubernetes and the Pure Storage FlexVolume Plugin

First, if you are using Pure Storage and Kubernetes make life easier and take a look at our plugin. Now version 1.2.2 and GA.

https://hub.docker.com/r/purestorage/k8s/

Make sure the follow the directions on the page to pull and install the plugin. If you are using Openshift pay special attention to the Readme. I will post more on this in the near future.

Cockroach DB as our Persistent Database

I want to simulate a very easy database that I can easily use in a container. That is also not the same old. I built a Go app that will write to a database over and over to kind of demonstrate the inner workings of the plugin but not necessarily supply a performance test.

To learn more about the steps I use in the video to deploy and manage CRDB in K8s please check out this link. https://www.cockroachlabs.com/docs/stable/orchestrate-cockroachdb-with-kubernetes.html

With that said, please check out how to deploy and scale a database with a persistent data platform from a Pure FlashArray. Watch this in Full screen to make the CLI commands easier to see.

What you are seeing in the video:

  1. Deploy the initial 3 pods with volumes automatically created and connected on the Pure FA.
  2. Initialize the cluster.
  3. Fail a node and watch K8s redeploy a new container and re-attach the data volume.
  4. Run a load generation application as a K8s Job.
  5. Scale the DB cluster out to 8 nodes.

What is next?

This is a really easy and quick demo but it show the ease of using the Pure Plugin to manage the persistent data, making sure you do not lose data in the event of app crashes. Also easily scaling. This can all be done via policy and the deployment can be made even easier using Helm. In a future post we will see how we can take advantage of these methods and keep the same highly available, high performance and very easy to use persistent data platform for your application.

Four Resources that Got Me Started with Kuberenetes

In the last post I mentioned there are resources that have already gone through that do a better job than me in helping you understand containers and Kubernetes.
So if you are a virtualization admin like me and want to make 2018 the year you know enough to be dangerous I suggest the following resources.

  1. Do Nigel Poulton’s Docker Deep Dive. A foundational understanding to containers will help the orchestration parts make sense.https://app.pluralsight.com/library/
  2. Read Nigel’s The Kubernetes Book
  3. Do Kubernetes the Hard Way. Once you see this the options that make K8s easier will seem a lot cooler and you will understand what they do in the background.
  4. Go and Play with Docker and Kubernetes. Free sandboxes for you to try out.
    https://labs.play-with-docker.com/
    https://labs.play-with-k8s.com/

Start thinking: Does this app need a VM or a container? Once you are asking the question you will begin to think critically about the choices.

I am not sure we all need to move 100% off of VM’s today. Starting to ask the questions will help prepare us to provide these services to our customers when the workloads and workflows that require them to arise.