Kubernetes (commonly stylized as k8s) is an open-source container-orchestration system for automating application deployment, scaling, and management. It aims to provide a platform for automating deployment, scaling, and operations of application containers across clusters of hosts. It works with a range of container tools, including Docker.
So if you are following from the previous post or dropping in now, let's segway to the world of eBPF applications and take a look at Parca, which builds on our brief investigation of performance bottlenecks using eBPF, but puts a killer app on top of your cluster to monitor all your iris workloads, continually, cluster wide!
Continous Profiling with Parca, IRIS Workloads Cluster Wide
This article is a continuation of Deploying InterSystems IRIS solution on GKE Using GitHub Actions, in which, with the help of GitHub Actions pipeline, our zpm-registry was deployed in a Google Kubernetes cluster created by Terraform. In order not to repeat, we’ll take as a starting point that:
In our previous article, we have explored the most common Kubernetes components:
We started with the pods and the services we needed to communicate with each other.
Then, we examined the Ingress component used to Route traffic into the cluster.
We also skimmed through an external configuration using ConfigMaps and Secrets.
Afterward, we analyzed Data persistence with the help of Volumes.
Finally, we took a quick look at pod blueprints with such replicating mechanisms as Deployments and StatefulSets (the latter is employed specifically for such stateful applications as databases).
In this article, we will explore Kubernetes architecture and configuration.
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