Pod scheduling is an internal process that determines placement of new pods onto nodes within the cluster. It is probably one of the most important tasks for a Day-2 scenario and should be considered at a very early stage for a new cluster. OpenShift/Kubernetes is already shipped with a default scheduler which schedules pods as they get created accross the cluster, without any manual steps.
However, there are scenarios where a more advanced approach is required, like for example using a specifc group of nodes for dedicated workload or make sure that certain applications do not run on the same nodes. Kubernetes provides different options:
Controlling placement with node selectors
Controlling placement with pod/node affinity/anti-affinity rules
Controlling placement with taints and tolerations
Controlling placement with topology spread constraints
This series will try to go into the detail of the different options and explains in simple examples how to work with pod placement rules.
It is not a replacement for any official documentation, so always check out Kubernetes and or OpenShift documentations.
The following prerequisites are used for all examples.
Let’s image that our cluster (OpenShift 4) has 4 compute nodes
oc get node --selector='node-role.kubernetes.io/worker'
NAME STATUS ROLES AGE VERSION
compute-0 Ready worker 7h1m v1.19.0+d59ce34
compute-1 Ready worker 7h1m v1.19.0+d59ce34
compute-2 Ready worker 7h1m v1.19.0+d59ce34
compute-3 Ready worker 7h1m v1.19.0+d59ce34
An example application (from the catalog Django + Postgres) has been deployed in the namespace podtesting. It contains by default 1 pod for a PostGresql database and one pod for a frontend web application.
Lately I came across several issues where a given Helm Chart must be modified after it has been rendered by Argo CD. Argo CD does a helm template to render a Chart. Sometimes, especially when you work with Subcharts or when a specific setting is not yet supported by the Chart, you need to modify it later … you need to post-render the Chart.
In this very short article, I would like to demonstrate this on a real-live example I had to do. I would like to inject annotations to a Route objects, so that the certificate can be injected. This is done by the cert-utils operator. For the post-rendering the Argo CD repo pod will be extended with a sidecar container, that is watching for the repos and patches them if required.
The article SSL Certificate Management for OpenShift on AWS explains how to use the Cert-Manager Operator to request and install a new SSL Certificate. This time, I would like to leverage the GitOps approach using the Helm Chart cert-manager I have prepared to deploy the Operator and order new Certificates.
I will use an ACME Letsencrypt issuer with a DNS challenge. My domain is hosted at AWS Route 53.
However, any other integration can be easily used.
During a GitOps journey at one point, the question arises, how to update a cluster? Nowadays it is very easy to update a cluster using CLI or WebUI, so why bother with GitOps in that case? The reason is simple: Using GitOps you can be sure that all clusters are updated to the correct, required version and the version of each cluster is also managed in Git.
All you need is the channel you want to use and the desired cluster version. Optionally, you can define the exact image SHA. This might be required when you are operating in a restricted environment.
Argo CD or OpenShift GitOps uses Applications or ApplicationSets to define the relationship between a source (Git) and a cluster. Typically, this is a 1:1 link, which means one Application is using one source to compare the cluster status. This can be a limitation. For example, if you are working with Helm Charts and a Helm repository, you do not want to re-build (or re-release) the whole chart just because you made a small change in the values file that is packaged into the repository. You want to separate the configuration of the chart with the Helm package.
The most common scenarios for multiple sources are (see: Argo CD documentation):
Your organization wants to use an external/public Helm chart
You want to override the Helm values with your own local values
You don’t want to clone the Helm chart locally as well because that would lead to duplication and you would need to monitor it manually for upstream changes.
This small article describes three different ways with a working example and tries to cover the advantages and disadvantages of each of them. They might be opinionated but some of them proved to be easier to use and manage.
OpenShift Logging is one of the more complex things to install and configure on an OpenShift cluster. Not because the service or Operators are so complex to understand, but because of the dependencies logging has. Besides the logging operator itself, the Loki operator is required, the Loki operator requires access to an object storage, that might be configured or is already available.
In this article, I would like to demonstrate the configuration of the full stack using an object storage from OpenShift Data Foundation. This means:
Installing the logging operator into the namespace openshift-logging
Installing the Loki operator into the namespace openshift-operators-redhat
Creating a new BackingStore and BucketClass
Generating the Secret for Loki to authenticate against the object storage
Configuring the LokiStack resource
Configuring the ClusterLogging resource
All steps will be done automatically. In case you have S3 storage available, or you are not using OpenShift Data Foundation, the setup will be a bit different. For example, you do not need to create a BackingStore or the Loki authentication Secret.
Thomas Jungbauer
( Lastmod: 2024-05-08 )
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2 min read