helm-charts VS helm

The example https://github.com/DataDog/helm-charts/blob/main/charts/datadog/values.yaml they provide list of all variables supported by the agent so should be pretty straight forward to copy paste the stuff that you need into your own values.yaml

Not to mention that, by default, they bill by the whole calendar month for each infrastructure and APM host. Scale your Kubernetes cluster up and then back down? That'll be an extra $18 + $36 per additional node (not $15 + $31 – that's the contract pricing, not the on-demand pricing), even if they were only online for a few days – even if they were only online for thirty seconds. Swap out a node? By default they bill by unique instances, not by number of instances, so they'll bill you for that, too.

If you ask them about it, they'll “happily” put you onto hourly on-demand billing (which seems to fix the unique vs. count thing, too), which is more expensive if you let something run on-demand for a whole month... but isn't the point of an on-demand service that you're not running it for a whole billing period?

Not to mention that their agent logs fairly noisily, and of course its logs count toward your quota! I upgraded a cluster without also upgrading the agent, and didn't notice for about a week that each agent was happily spamming away about some long-deprecated Kubernetes API no longer being available[0]. At $2.55/million log lines and fewer than a million lines logged, this was not a costly mistake, but it's the principle of the thing. Why should an incompatibility in their agent (which their dashboard could specifically alert about, but doesn't!) cost me money?

[0] https://github.com/DataDog/helm-charts/issues/620#issuecomme...

After that, we have to edit values.yaml file, which will be used as a configuration file used by datadog agent running in the cluster. The fields I overrided(edited) are as below.

These are the commands to install the Datadog agent in your cluster using Helm v3 with the default values. Make sure to copy your API key from Datadog dashboard in the install command.

Applying Kubernetes manifests individually is problematic because files can get overlooked. Packaging your applications as Helm charts lets you version your manifests and easily repeat deployments into different environments. Helm tracks the state of each deployment as a "release" in your cluster.

helm

Explanation here: https://github.com/helm/helm/issues/12681#issuecomment-19593...

Looks like it's a bug in Helm, but actually isn't Helm's fault, the issue was introduced by Fedora Linux.

Helm (Get from here https://helm.sh/)

It’s also well understood that having a k8s cluster is not enough to make developers able to host their services - you need a devops team to work with them, using tools like delivery pipelines, Helm, kustomize, infra as code, service mesh, ingress, secrets management, key management - the list goes on! Developer Portals like Backstage, Port and Cortex have started to emerge to help manage some of this complexity.

Kubernetes orchestrates deployments and manages resources through yaml configuration files. While Kubernetes supports a wide array of resources and configurations, our aim in this tutorial is to maintain simplicity. For the sake of clarity and ease of understanding, we will use yaml configurations with hardcoded values. This method simplifies the learning process but isn’t ideal for production environments due to the need for manual updates with each new deployment. Although there are methods to streamline and automate this process, such as using Helm charts or bash scripts, we’ll not delve into those techniques to keep the tutorial manageable and avoid fatigue — you might be quite tired by that point!

Helm is a package manager that automates Kubernetes applications' creation, packaging, configuration, and deployment by combining your configuration files into a single reusable package. This eliminates the requirement to create the mentioned Kubernetes resources by ourselves since they have been implemented within the Helm chart. All we need to do is configure it as needed to match our requirements. From the public Helm chart repository, we can get the charts for common software packages like Consul, Jenkins SonarQube, etc. We can also create our own Helm charts for our custom applications so that we don’t need to repeat ourselves and simplify deployments.

We can search for charts https://helm.sh/ . Charts can be pulled(downloaded) and optionally unpacked(untar).

Generally I felt as if I was diving in the deepest of waters without the correct equipement and that was horrifying. Unfortunately to me, I had to dive even deeper before getting equiped with tools like ArgoCD, and k8slens. I had to start working with... HELM.

Within the architecture of Cyclops, a central component is the Helm engine. Helm is very popular within the Kubernetes community; chances are you have already run into it. The popularity of Helm plays to Cyclops's strength because of its straightforward integration.