Hi Developers,

Writing a script for the application deployment can be very interesting to ensure rapid deployment without forgetting anything.
config-api is a library to help developers to write configuration scripts based on a JSON document.

Implemented features :

*** archived ***

The question has come up several times and I saw mixed answers and no quick example

My personal preference is using CPIPE device as you get back exactly the output you will get at the command line interface of your OS .
The tricky thing is to stop reading in time.
The example just displays what you normally see in your console.
it becomes useful if you look for things that you can't get from any $system.whatever()

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.

If a picture is worth a thousand words, what's a video worth? Certainly more than typing a post.

Please check out my "Coding talks" on InterSystems Developers YouTube:

1. Analysing InterSystems IRIS System Performance with Yape. Part 1: Installing Yape

https://www.youtube.com/embed/3KClL5zT6MY
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Running Yape in a container.

2. Yape Container SQLite iostat InterSystems

https://www.youtube.com/embed/cuMLSO9NQCM
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Extracting and plotting pButtons data including timeframes and iostat.

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.

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.

All source code to the article is available at: https://github.com/antonum/ha-iris-k8s

REST API for Security Package

Hi community,

In this article, we will learn how to set up a REST API for the IRIS Security Package. We will be able to create users, roles, add applications, etc... by simple HTTP requests as well as generate a client application in ObjectScript.

Requirements

We need :

I'm looking for a way to have an IRIS db distributed over several (hw) drives.
Without touching the internal data structures (e.g. mapping) !
Are there any options in file systems to achieve this "splitting" or "appending" ?

Historic background:

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.

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).

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:

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: