Kubernetes Best Practices

As a Solution Architect here at Rafay Systems, I get to work with customers and prospects across a wide spectrum of industries. Some of these customers are very early in their Kubernetes journey, while others have been actively using K8s for a very long time. It’s become clear to me that, generally speaking, the industry is still in a very early phase of Kubernetes adoption. Right now it seems that most people are learning, experimenting and testing with Kubernetes deployments and production deployments.

What makes my job so enjoyable is that I get to work with prospects involved in very diverse and interesting use cases. I also field questions that range from very broad (How do I go about bringing-up up my first cluster?, How do I go about deploying multiple clusters across different locations that are both on-premises and in public clouds?) to very specific (How do I synchronize my data and metadata between clusters in Amazon EKS and on-premises? How do I automate the provisioning of persistent storage across my clusters?)

One of my takeaways from educating and mentoring customers is there is a steep learning curve when it comes to Kubernetes. Nearly every customer I speak and meet with asks me about best practices and whether Rafay has a set of “Kubernetes Best Practices” recommendations.

I brought this question back to the engineering and product teams at Rafay and we have assembled such a list with our recommendations. This list is organized by category and covers topics such as:

• Git/Source Code
• CI System
• Cluster Level
• Workload Level
• … and more.

Within each of the main categories, we have further detailed specific areas, descriptions and the recommended best practices.

 

As yet another way to give back to the Kubernetes community, Rafay would like to share these Best Practices recommendations here. Please use your business or organization email when asked.

As always, we welcome your inputs, thoughts and ideas. Feel free to get in touch via email.