Three common Kubernetes challenges — and how to solve them

Kubernetes has a pretty fearsome reputation for complexity in its own right (as we’ve discussed before). Learning it for the first time and standing up your first cluster, deploying your first application stack… it can be painful. But as any seasoned operator will tell you, it’s when you expand into running Kubernetes in production at scale that you come across the real pain! Let’s delve into  three of the most common "growing pains" that we’ve seen in the field: - **Developer productivity** - **Multicluster headaches** - **The edge learning curve** We’ll not only explore the pain, but show you some ways to sidestep these pitfalls. ## Pain 1: Developer Productivity ... Despite the popularity of the term “DevOps,” most developers don’t have the skill set to be cloud native infrastructure or Kubernetes experts. They would much rather be coding features than managing infrastructure (as we have explored in this blog post). Developers just want to consume infrastructure elements such as Kubernetes clusters, and they have little tolerance for delays and hurdles in their way. Unfortunately, it’s not always easy to give them what they want. ... Firing up a new cluster takes work, costs money, and even if you have the capacity to jump right on the request, it also takes time. Which means your developers are kept waiting. … ## Pain 2: Multicluster Headaches Everyone starts with one Kubernetes cluster. But few teams today stay that way. This number quickly grows to three when you split development, staging and production environment clusters. And from there? Well, our research found that already half of those using Kubernetes in production have more than 10 clusters. Eighty percent expect to increase the number or size of their clusters in the next year. … That “future state” description should cover the entire cluster, from its infrastructure to the application workloads that run on top. ... From the data center and cloud, you might start looking even further afield: to the edge. Organizations are increasingly adopting edge computing to put applications right where they add value: in restaurants, factories and other remote locations. But edge presents unique challenges. The hardware is often low power: Your clusters might be single-node devices. The connectivity to the site may be low bandwidth or intermittent, making remote management difficult. There’s a whole new frontier of security to consider, protecting against hardware tampering. And the killer: When we’re talking about restaurant chains or industrial buildings, compute might need to be deployed to hundreds or thousands of sites. There won’t be a Kubernetes expert at each site — or even a regular IT guy — to help onboard new devices or fix any configuration issues locally. These are big challenges, but there are solutions to help you.