This is a rather personal view of the history before Caché.
It is in no sense meant to compete with the excellent books from Mike Kadow discussed in an earlier article.
We have different histories and so this is meant to create a different perspective of the past.

Hi community,

The aim of this article is to explain how to create messaging between IRIS and Microsoft Teams.

In my company, we wanted to monitor error messages, and we used the Ens.Alert class to redirect those error messages through a Business Operation that sent an email.
The problem was that we sent those error messages to a support account where there were many emails. We wanted something specific for a specific team.

So we investigated how to make these messages reach the development team directly and they could have, in real time, a notification of an error in our production.
In our company we use Microsoft Teams as a corporate tool, so we asked ourselves: How could we make these messages reach the IRIS development team?

This post will show you an approach to size shared memory requirements for database applications running on InterSystems data platforms including global and routine buffers, gmheap, and locksize as well as some performance tips you should consider when configuring servers and when virtualizing Caché applications. As ever when I talk about Caché I mean all the data platform (Ensemble, HealthShare, iKnow and Caché).

A list of other posts in this series is here

We all know that having a set of proper test data before deploying an application to production is crucial for ensuring its reliability and performance. It allows to simulate real-world scenarios and identify potential issues or bugs before they impact end-users. Moreover, testing with representative data sets allows to optimize performance, identify bottlenecks, and fine-tune algorithms or processes as needed. Ultimately, having a comprehensive set of test data helps to deliver a higher quality product, reducing the likelihood of post-production issues and enhancing the overall user experience.

In this article, let's look at how one can use generative AI, namely Gemini by Google, to generate (hopefully) meaningful data for the properties of multiple objects. To do this, I will use the RESTful service to generate data in a JSON format and then use the received data to create objects.

The invention and popularization of Large Language Models (such as OpenAI's GPT-4) has launched a wave of innovative solutions that can leverage large volumes of unstructured data that was impractical or even impossible to process manually until recently.

Recently I was fielding some questions that someone had about some bugs that crop up on Docker for Mac, and it reminded me of what Shakespeare wrote in his famous tragedy about large-scale software orchestration, Kubelet: the Prince of Benchmark.

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.

Your application is deployed and everything is running fine. Great, hi-five! Then out of the blue the phone starts to ring off the hook – it’s users complaining that the application is sometimes ‘slow’. But what does that mean? Sometimes? What tools do you have and what statistics should you be looking at to find and resolve this slowness? Is your system infrastructure up to the task of the user load? What infrastructure design questions should you have asked before you went into production? How can you capacity plan for new hardware with confidence and without over-spec'ing? How can you stop the phone ringing? How could you have stopped it ringing in the first place?

I'm pleased to announce a milestone in the lifecycle of the ObjectScript package manager, ZPM. The package manager has offered developers the ability to neatly package up ObjectScript code and deployment configuration settings and version information in a convenient way. Over the last few years, it has evolved greatly into an integral part of many development workflows.

ObjectScript has at least three ways of handling errors (status codes, exceptions, SQLCODE, etc.). Most of the system code uses statuses but exceptions are easier to handle for a number of reasons. Working with legacy code you spend some time translating between the different techniques. I use these snippets a lot for reference. Hopefully they're useful to others as well.

My major interest is Working with Globals in Embedded Python.
So I checked the available official documentation.

#1 Introduction to Globals
an attempt of a generic description of what a global is. Pointing to

As a developer, you have probably spent at least some time writing repetetive code. You may have even found yourself wishing you could generate the code programmatically. If this sounds familiar, this article is for you!

We'll start with an example. Note: the following examples use the %DynamicObject interface, which requires Caché 2016.2 or later. If you are unfamiliar with this class, check out the documentation here: Using JSON in Caché. It's really cool!

The Lo-Code Challenge

Imagine the scene. You are working happily at Widgets Direct, the internet's premier retailer of Widgets and Widget Accessories. Your boss has some devastating news, some customers might not be fully happy with their widgets, and we need a helpdesk application to track these complaints. To makes things interesting, he wants this with a very small code footprint and challenges you to deliver an application in less than 150 lines of code using InterSystems IRIS. Is this even possible?