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zsoltkacsandi | 2 months ago
Unpopular opinion, but the source of most of the problems I've seen with infrastructures using Kubernetes came from exactly this kind of approach.
Problems usually come when we use tools to solve things that they weren't made for. That is why - in my opinion - it is super important to treat a container orchestrator a container orchestrator.
antonvs|2 months ago
> it is super important to treat a container orchestrator a container orchestrator.
Which products do you think are only “container orchestrators”? Even Docker Compose is designed to achieve a desired state from a declarative infrastructure definition.
zsoltkacsandi|2 months ago
The way how something describes the desired state (declaratively for example) has nothing to do with if it is a container orchestrator or not.
If you open the Kubernetes website, do you know what is the first thing you will see? "Production-Grade Container Orchestration". Even according to their own docs, Kubernetes is a container orchestrator.
NewJazz|2 months ago
zsoltkacsandi|2 months ago
k8ssskhltl|2 months ago
zsoltkacsandi|2 months ago
Yes, and 99% of the companies do this. It is quite common to use Terraform/AWS CDK/Pulumi/etc to provision the infrastructure, and ArgoCD/Helm/etc to manage the resources on Kubernetes. There is nothing wrong with it.
szundi|2 months ago
zsoltkacsandi|2 months ago
> the source of most of the problems I've seen with infrastructures using Kubernetes came from exactly this kind of approach
But some more concrete stories:
Once, while I was on call, I got paged because a Kubernetes node was running out of disk space. The root cause was the logging pipeline. Normally, debugging a "no space left on device" issue in a logging pipeline is fairly straightforward, if the tools are used as intended. This time, they weren't.
The entire pipeline was managed by a custom-built logging operator, designed to let teams describe logging pipelines declaratively. The problem? The resource definitions alone were around 20,000 lines of YAML. In the middle of the night, I had to reverse-engineer how the operator translated that declarative configuration into an actual pipeline. It took three days and multiple SREs to fully understand and fix the issue. Without such a declarative magic it takes usually 1 hour to solve such an issue.
Another example: external-dns. It's commonly used to manage DNS declaratively in Kubernetes. We had multiple clusters using Route 53 in the same AWS account. Route 53 has a global API request limit per account. When two or more clusters tried to reconcile DNS records at the same time, one would hit the quota. The others would partially fail, drift out of sync, and trigger retries - creating one of the messiest cross-cluster race conditions I've ever dealt with.
And I have plenty more stories like these.