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.

In this article, we will cover below topics:

• What is Kubernetes?
• Main Kubernetes (K8s) Components

What is Kubernetes?

Kubernetes is an open-source container orchestration framework developed by Google. In essence, it controls container speed and helps you manage applications consisting of multiple containers. Additionally, it allows you to operate them in different environments, e.g., physical machines, virtual machines, Cloud environments, or even hybrid deployment environments.

In case you're planning on deploying IRIS For Health, or any of our containerized products, via the IKO on OpenShift, I wanted to share some of the hurdles we had to overcome.

As with any IKO based installation, we first need to deploy the IKO itself. However we were getting this error:

The IKO allows for sidecars. The idea behind them is to have direct access to a specific instance of IRIS. If we have mirrored data nodes, the web gateway will (correctly) only give us access to the primary node. But perhaps we need access to a specific instance. The sidecar is the solution.

Building on the example from the previous article, we introduce the sidecar by using a mirrored data node and of course arbiter.

We now get to make use of the IKO.

Below we define the environment we will be creating via a Custom Resource Definition (CRD). It lets us define something outside the realm of what the Kubernetes standard knows (this is objects such as your pods, services, persistent volumes (and claims), configmaps, secrets, and lots more). We are building a new kind of object, an IrisCluster object.

K9s is a terminal-based UI (aka kubectl clown suit), to manage Kubernetes clusters that drastically simplifies navigating, observing, and managing your applications in K8s, including Custom Resources like the InterSystems Kubernetes Operator (IKO) and ArgoCD Applications. If you are about to take your CKD, CKA, or CKS, leave k9s well enough alone for awhile as the abstraction to kubectl will become the standard for navigating the cluster and you will undoubtedly become estranged to the extended flags of kubectl and bomb the exam.

In this article, we’ll build a highly available IRIS configuration using Kubernetes Deployments with distributed persistent storage instead of the “traditional” IRIS mirror pair. This deployment would be able to tolerate infrastructure-related failures, such as node, storage and Availability Zone failures. The described approach greatly reduces the complexity of the deployment at the expense of slightly extended RTO.

IRIS configurations and user accounts contain various data elements that need to be tracked, and many people struggle to copy or sync those system configurations and user accounts between IRIS instances. So how can this process be simplified?

In software engineering, CI/CD or CICD is the set of combined practices of continuous integration (CI) and (more often) continuous delivery or (less often) continuous deployment (CD). Can CI/CD eliminate all our struggles?

Want a commercial grade FHIR® Implementation included in your micro service ecosystem and barely have enough time to fill out your Health plan elections?

In this article, we’ll look at one of the ways to monitor the InterSystems IRIS data platform (IRIS) deployed in the Google Kubernetes Engine (GKE). The GKE integrates easily with Cloud Monitoring, simplifying our task. As a bonus, the article shows how to display metrics from Cloud Monitoring in Grafana.

Note that the Google Cloud Platform used in this article is not free (price list), but you can leverage a free tier. This article assumes that you already have a project in the Google Cloud Platform (referred to as <your_project_id>) and have permission to use it.

In the previous article, we looked at one way to create a custom operator that manages the IRIS instance state. This time, we’re going to take a look at a ready-to-go operator, InterSystems Kubernetes Operator (IKO). Official documentation will help us navigate the deployment steps.

Imagine you want to see what InterSystems can give you in terms of data analytics. You studied the theory and now you want some practice. Fortunately, InterSystems provides a project that contains some good examples: Samples BI. Start with the README file, skipping anything associated with Docker, and go straight to the step-by-step installation. Launch a virtual instance, install IRIS there, follow the instructions for installing Samples BI, and then impress the boss with beautiful charts and tables. So far so good.

Inevitably, though, you’ll need to make changes.

Last time we deployed a simple IRIS application to the Google Cloud. Now we’re going to deploy the same project to Amazon Web Services using its Elastic Kubernetes Service (EKS).

We assume you’ve already forked the IRIS project to your own private repository. It’s called <username>/my-objectscript-rest-docker-template in this article. <root_repo_dir> is its root directory.

Before getting started, install the AWS command-line interface and, for Kubernetes cluster creation, eksctl, a simple CLI utility. For AWS you can try to use aws2, but you’ll need to set aws2 usage in kube config file as described here.