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:

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Windows Subsystem for Linux (WSL) is a feature of Windows that allows you to run a Linux environment on your Windows machine, without the need for a separate virtual machine or dual booting.

WSL is designed to provide a seamless and productive experience for developers who want to use both Windows and Linux at the same time**.

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Most transactional applications have a 70:30 RW profile. However, some special cases have extremely high write IO profiles.

I ran storage IO tests in the ap-southeast-2 (Sydney) AWS region to simulate IRIS database IO patterns and throughput similar to a very high write rate application.

The test aimed to determine whether the EC2 instance types and EBS volume types available in the AWS Australian regions will support the high IO rates and throughput required.

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In our team, there are several developers working in parallel on different projects. To ensure this distributed collaboration and high-quality code reviews, we rely on version control with Git. Our challenge is to harmonize the unique characteristics of InterSystems products and the possibilities of Git and Docker.

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Some Usage cases

1. A deployment may consist of two high availability instances and two disaster recovery instances in a different data center.

The corresponding UAT environment could replicate this giving a total of 8 instances. How do you confirm CPF and Scheduled task alignment across ALL instances.

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Article
· Jul 21, 2022 11m read
ECP With Docker

Hi community,

This is the third article in the series about initializing IRIS instances with Docker. This time, we will focus on Enterprise Cache Protocol (ECP).

In a very simplified way, ECP allows configuring some IRIS instances as application servers and others as data servers. Detailed technical information can be found in the official documentation.

This article aims to describe:

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Hi Developers!

This is yet another short post that is intended to simplify developers' life. Now we'll talk about how to make GitHub run unit tests with every push to the repository by adding just one file to the repo. For free. On Github Cloud. Sounds great, isn't it?

It is possible and very easy to do. Credit goes to @Dmitry Maslennikov (and his repo), ZPM Package Manager, and GitHub Actions. Let's see how this all works!

Something for Nothing by Robert Sheckley - YouTube

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Adding VSCode into your IRIS container

One of the easiest ways to setup repeatable development environments is to spin up containers for them. I find that when iterating quickly, it was very convenient to host a vscode instance within my development container. Thus, I have created a quick container script to add a browser-based vscode into an IRIS container. This should work for most 2021.1+ containers. My code repository can be found here

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We’ve already considered how to run an IRIS-based application in GCP Kubernetes in Deploying InterSystems IRIS Solution into GCP Kubernetes Cluster GKE Using CircleCI. Additionally, we’ve seen how to run an IRIS-based application in AWS Kubernetes in Deploying a Simple IRIS-Based Web Application Using Amazon EKS. Now, let’s look at how to deploy an application to the Azure Kubernetes Service (AKS).

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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.

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Some changes in IRIS configuration require a restart of IRIS.
This is no big issue as long as I have access to the server command line with sufficient privileges.

In a container, this is not always given.
Stopping IRIS from the terminal/session prompt is no problem.
But the restart after is.

Note1: container start-stop is no option as it might be removed by option --rm in docker run
Note2: the target is linux (manly in docker). Windows is excluded

GitHub

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Introduction
Several resources tell us how to run IRIS in a Kubernetes cluster, such as Deploying an InterSystems IRIS Solution on EKS using GitHub Actions and Deploying InterSystems IRIS solution on GKE Using GitHub Actions. These methods work but they require that you create Kubernetes manifests and Helm charts, which might be rather time-consuming.
To simplify IRIS deployment, InterSystems developed an amazing tool called InterSystems Kubernetes Operator (IKO). A number of official resources explain IKO usage in details, such as New Video: Intersystems IRIS Kubernetes Operator and InterSystems Kubernetes Operator.

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As we all well know, InterSystems IRIS has an extensive range of tools for improving the scalability of application systems. In particular, much has been done to facilitate the parallel processing of data, including the use of parallelism in SQL query processing and the most attention-grabbing feature of IRIS: sharding. However, many mature developments that started back in Caché and have been carried over into IRIS actively use the multi-model features of this DBMS, which are understood as allowing the coexistence of different data models within a single database. For example, the HIS qMS database contains both semantic relational (electronic medical records) as well as traditional relational (interaction with PACS) and hierarchical data models (laboratory data and integration with other systems). Most of the listed models are implemented using SP.ARM's qWORD tool (a mini-DBMS that is based on direct access to globals). Therefore, unfortunately, it is not possible to use the new capabilities of parallel query processing for scaling, since these queries do not use IRIS SQL access.

Meanwhile, as the size of the database grows, most of the problems inherent to large relational databases become right for non-relational ones. So, this is a major reason why we are interested in parallel data processing as one of the tools that can be used for scaling.

In this article, I would like to discuss those aspects of parallel data processing that I have been dealing with over the years when solving tasks that are rarely mentioned in discussions of Big Data. I am going to be focusing on the technological transformation of databases, or, rather, technologies for transforming databases.

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Hi All,

With this article, I would like to show you how easily and dynamically System Alerting and Monitoring (or SAM for short) can be configured. The use case could be that of a fast and agile CI/CD provisioning pipeline where you want to run your unit-tests but also stress-tests and you would want to quickly be able to see if those tests are successful or how they are stressing the systems and your application (the InterSystems IRIS backend SAM API is extendable for your APM implementation).

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A lot of developers like to work with Studio and have been looking into source code version control such as GIT or into enabling modern development workflows like CICD or DevOps processes.

This article describe an elementary solution to get you started in CICD and DevOps, even if you are not yet ready to move to Atelier or forth coming VS Code approach which enable client side source code version control.

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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:

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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.

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In an earlier article (hope, you’ve read it), we took a look at the CircleCI deployment system, which integrates perfectly with GitHub. Why then would we want to look any further? Well, GitHub has its own CI/CD platform called GitHub Actions, which is worth exploring. With GitHub Actions, you don’t need to rely on some external, albeit cool, service.

In this article we’re going to try using GitHub Actions to deploy the server part of InterSystems Package Manager, ZPM-registry, on Google Kubernetes Engine (GKE).

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Last time we launched an IRIS application in the Google Cloud using its GKE service.

And, although creating a cluster manually (or through gcloud) is easy, the modern Infrastructure-as-Code (IaC) approach advises that the description of the Kubernetes cluster should be stored in the repository as code as well. How to write this code is determined by the tool that’s used for IaC.

In the case of Google Cloud, there are several options, among them Deployment Manager and Terraform. Opinions are divided as to which is better: if you want to learn more, read this Reddit thread Opinions on Terraform vs. Deployment Manager? and the Medium article Comparing GCP Deployment Manager and Terraform.

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