Introduction
K8sGPT is a tool that scans Kubernetes cluster diagnostics and rates errors in plain language. It analyzes the cluster, searches for issues, and suggests artificial intelligence-based fixes. Numerous issues are visible throughout the cluster, including pod, service, and node issues. K8sGPT has several built-in analyzers that examine these issues. Both optional and default analyzers are available. PodAnalyzer, pvcAnalyzer, rsAnalyzer, service analyzer, event analyzer, statefulset analyzer, ingress analyzer, cronjob analyzer, and node analyzer are the analyzers that are enabled by default. The HPA Analyzer, pdb Analyzer, and Network Policy Analyzer are optional analyzers. Additionally, we can build unique analyzers if needed. Using the k8sgpt analyse command, we can analyze the cluster and find faults in plain English. This will reduce the time required to resolve the issue because it offers suggestions to troubleshoot the issue.
Quick Start
Firstly, install k8sgpt in your environment by referring to the official k8sGPT documentation. Use minikube or microk8s clusters for creating a Kubernetes environment.
K8sGPT uses an AI tool as a backend. The default backend is OpenAI, but we can use other backends like Azure OpenAI, LocalAI, or Noopai. So first, we need to generate an API key to authenticate k8sGPT with OpenAI. We can generate API keys by going to this site: Here’s the link you provided with the requested styling: https://platform.openai.com/account/api-keys or by using the k8sgpt generate command, which will open a link in your default browser where we can generate the API key.
We can analyze the cluster by using the k8sgpt-analyse command. Here I have made some errors, like deleting the persistent volume in the first case, creating a persistent volume claim with the storage class manual in the second case, and changing the label of the service in the third case. Now, using the k8sgpt analyse command, we can see that these errors have been detected
There are K8sgpt filter commands that are used to manage which resources to analyze. We can add and remove filters.
We can add multiple filters by separating them with commas, like
k8sgpt filters add Pod, Service
We can filter the resource by namespace by using a command like
k8sgpt analyse –namespace=default. We can also combine multiple filters, like
k8sgpt analyse -filter=Pod -namespace=default to analyze only the filtered resource
How k8sGPT works
K8sGPT uses OpenAI’s GPT language model, which helps K8sGPT understand the k8s configuration files, detect issues, and give suggestions to fix them. It has many analyzers to analyze clusters. Some of the analyzers are enabled by default, and others can be enabled by us. We can also create custom analyzers if we need them. For security, data anonymization can be done by adding the –anonymize flag in the k8sGPT command, so data is anonymized before it is sent to OpenAI. Here, information like object names, labels, and other sensitive information is masked before being sent to OpenAI. It is replaced by a key, and this key is used to de-anonymize the data when it is returned to the user. We can integrate with other tools like Trivy, a cloud-native security scanner, to scan the cluster to identify any vulnerability issues. K8sGPT integrates with monitoring tools like Grafana and Prometheus to access a rich set of metrics that can enrich its analysis. This will lead to more accurate diagnoses of the root causes of problems.
You can list all the available integrations, activate Trivy integration, or deactivate Trivy integration.
Once the integration is activated, we can use the Vulnerability Reports filter, which is enabled in the k8sGPT filter list. To use the vulnerability report filter, use the below command.
k8sgpt analyse –filter = VulnerabilityReport
Conclusion
Based on OpenAI’s GPT-3 language model, K8sGPT is a tool that assists you in identifying and prioritizing problems with your Kubernetes clusters.
You may easily scan your clusters, find problems, and receive recommendations on how to fix them by installing and configuring K8sGPT. Its integrated filters and analyzers also make it simple to alter the analysis to meet your unique requirements. You can enhance your Kubernetes monitoring and troubleshooting operations and obtain insights into your Kubernetes clusters with the aid of K8sGPT!
