The challenge with Kubernetes (K8s) was its complexity and lack of in-house skills to provision and troubleshoot. Kubernetes Cloud or on-premises cluster. Adoption of K8 continues to grow, but many developers and operations teams are still new to the technology and need to learn new terminology, workflows, tools, and more.
The K8s discrete parts require a wide level of expertise just for the setup process. Consider that launching a K8s cluster requires knowledge and configuration of multiple components, from pods to services, as well as resources such as etcd, API server, kubelet, kube-proxy, etc.
Then you also have to tackle scheduling, scaling and networking. One mistake can quickly lead to a myriad of scalability, reliability, and even security issues.
Moreover, the ecosystem itself is always growing and evolving rapidly. Tools and add-ons can be voluminous for beginners and difficult to keep up with. Formal education and training in it is not necessarily part of every developer’s academic or professional background.
Also, don’t forget that this technology has many moving parts and complex interactions that can make troubleshooting difficult and time consuming in the event of a failure. Diagnosing the cause of failure requires deep technical knowledge and expertise, which often resides in the minds of a few experienced engineers.
Let’s dig deeper and explore new and innovative ways to help overcome glare problems. skill gap problem.
Improve your team’s skills with ChatGPT assistance
Kubernetes lacks a one-size-fits-all approach, making it difficult to learn and use effectively. K8s are highly customizable and can be configured in many different ways, depending on the specific needs of your application and infrastructure. Teams lack contextual understanding and visibility of the architecture, so it’s often difficult to apply what they’ve learned from documentation (there’s a lot) and training to existing environments.
What is your current architecture like? Which pods are tied to specific namespaces? What about node health? You need to context switch between Terraform configuration files, monitoring tools.
What if you could ask ChatGPT these questions?
Let’s see a usage example prompt operation Leverage ChatGPT to understand all deployments in your cluster. PromptOps provides Free Kubernetes consulting tools This allows users to ask questions and get immediate support in the form of BASH scripts, documentation references, and other helpful resources.
By providing PromptOps with infrastructure fragmented data from various sources such as Confluence, Notion and Terraform configuration files, it is hoped that PromptOps will quickly aggregate all the information and help visualize the architecture. increase. Instead of manually running kubectl commands to check for deployments, I prompted her PromptOps through chat to explain what deployments were in the cluster.
The PromptOps response is:
Prompt Operations > To answer your question, I suggest running this script.
PromptOps provided a script to run with a reference to a Notion page containing information about the cluster.
After running the script, PromptOps provided the output of the deployment in the cluster.
PromptOps also provides a visual graph of all deployments in the cluster, making learning much easier.
Responses from PromptOps simplify Kubernetes infrastructure management and ensure everyone on your DevOps team understands current architecture and adapts to change.
Save time with automated incident investigation
Take a look at Troubleshooting Production Issues to see how PromptOps can help you identify the root cause of Kubernetes failures. Kubernetes metrics Logs provide key insights across all levels from cluster to container. They provide information about resource utilization, pod and node status, cluster autoscaler metrics, network errors, container runtimes, and more.
But the problem with all these metrics is that they are not immediately useful. Log streams or metric graphs don’t provide the context or direction you need. There are tools that can help, but the need to get context from the entire tech stack just to see what actually happened and who did what when something went wrong complicates things even more. often
and Ctrl stack, which incorporates PromptOps to analyze causality graphs, Kubernetes events, metrics, and logs to reduce manual troubleshooting challenges. The goal is to help DevOps teams quickly understand Kubernetes issues by using PromptOps to generate a root cause analysis summary that explains the cause. In this example, the causal graph of the root cause analysis dashboard within CtrlStack is populated into his PromptOps for troubleshooting and resolution as described below.
Here is an example of a casual chart from the Root Cause Analysis dashboard.
This prompted us to ask PromptOps to explain the graph. Below is the PromptOps response.
Prompt Operations > The chart description is as follows:
- This graph visualizes the root cause of Kubernetes pod failures. This error is caused by pod image changes similar to Kubernetes changes.
pod_errormetric. This error is also analogous to an inflection point:kubernetes_uptimeandkubernetes_pod_mem_usageAffects metrics, and topology changes on node 1.
- The graph shows the root cause of the problem.
kubernetes_pod_errorchange. I have three insights. The first is that the image has changed. Second, we found an inflection point in our metrics.kubernetes_uptimeandkubernetes_pod_mem_usage. Third, there is a topology change on node1.
Responses from PromptOps provide useful correlations of information across uptime and memory usage metrics and related topology changes. These insights include checking Kubernetes change events, metrics, resource usage, and topology changes. Based on this automated incident investigation, developers and operators should have sufficient context to quickly determine next steps to resolve the issue.
Close the skills gap with ChatGPT and change intelligence
Based on the examples provided, it is clear that ChatGPT can provide valuable assistance in closing the Kubernetes skills gap. ChatGPT provides quick insight and clear explanations for DevOps teams to troubleshoot and fix production issues. This helps developers new to Kubernetes as well as junior operators to learn the technology and solve common problems on their own.
ChatGPT responses can help you understand problems quickly, but different problems in Kubernetes deployments require specific contextual information. That’s where Change Intelligence comes in. The Change Intelligence Platform provides causal graphs that connect resource capacity, infrastructure changes, configuration changes, metric history graphs, and event timelines to optimize your path to root cause analysis.
A ChatGPT-based approach to learning Kubernetes has the potential to significantly improve DevOps productivity while eliminating cognitive overload. Combining ChatGPT with change intelligence will help the team improve his Kubernetes skills and improve observability.
