The IKO documentation is robust. A single web page, that consists of about 50 actual pages of documentation. For beginners that can be a bit overwhelming. As the saying goes: how do you eat an elephant? One bite at a time. Let's start with the first bite: helm.

What is Helm?

Helm is to Kubernetes what the InterSystems Package Manager (IPM, formerly ObjectScript Package Manager - ZPM) is to IRIS.

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.

InterSystems FAQ rubric

The meaning of each timeout value is as follows.

1. [Server response timeout]

If IRIS/Caché processing (routine or query execution) does not finish within this set time, the browser will return an error.

For example, if this value is 60 seconds and it takes 90 seconds to execute a routine/method/query, an error will occur.

2. [Queued request timeout]

Recently @Anastasia Dyubaylo published a post (this one) showing a new IntegratedML functionality for time series predictions that @Thomas Dyar already presented to us at the Global Summit 2023 so, let's go to set up a small workshop to test it!

In a customer project I was asked how you can keep track of database changes: Who changed what at which date and time. Goal was to track insert, update and delete for both SQL and object access.

This is the table that I created to keep the Change Log:

IRIS 2023.3.0 ASQ

Abstract Set Query, ASQ, is a super set of JSON Path Language, JPL, which was first introduced by the 2016 ISO SQL Standard. JPL is only defined within the context of SQL. In 2023.3, we introduced classes that implement ASQ. It works on any implementation of %Library.AbstractSet. The complete syntax for ASQ will be documented later. This is just a quick introduction to a few of its features. This implementation comes with a nice utility as well. You will know it when you see it!

Following 2 local Webinars we had focused on VS Code ["Intro" and "Beyond Basics"; in Hebrew], I prepared for the participants some related links of relevant resources which we sent as a follow-up. Sharing them here as well for the benefit of the Community.
You are all of course welcome to add more useful resources.

Hi, Community!

Since this article is an overview of Flask Login, let's begin with Flask Introduction!

What is Flask?

In the realm of web development, Python has emerged as a formidable force, offering its versatility and robustness to create dynamic and scalable applications. For that reason, tools and services compatible with this language are in demand these days. Flask is a lightweight and easy-to-use web framework for Python. It stands out as a lightweight and user-friendly option. Its simplicity and flexibility have made it a popular choice for developers, particularly for creating smaller-scale applications. It is based on the Werkzeug toolkit and provides a simple but powerful API for building web applications.
Unlike its full-stack counterparts, Flask provides a core set of features, focusing on URL routing, template rendering, and request handling. This minimalist approach makes Flask lightweight and easy to learn, allowing developers to build web applications quickly and without the burden of unnecessary complexity.

We return with our example of using the FHIR Adapter, in this article we are going to review how we can configure it in our IRIS instances and what the result of the installation is.

The steps taken to configure the project are the same as indicated in the official documentation, you can review them directly here. Well, let's get to work!

In recent versions of IRIS, a powerful new data loading command has been introduced to SQL: LOAD DATA. This feature has been highly optimized to import data into IRIS extremely fast, allowing hundreds of gigabytes of data to be inserted in seconds instead of hours or days.

This is a very exciting improvement. However, a big problem in the data loading experience still exists. Namely, the time and hassle it takes to:

Artificial intelligence is not limited only to generating images through text with instructions or creating narratives with simple directions.

You can also make variations of a picture or include a special background to an already existing one.

Additionally, you can obtain the transcription of audio regardless of its language and the speed of the speaker.

So, let's analyze how the file management works.

I recently had the need to monitor from HealthConnect the records present in a NoSQL database in the Cloud, more specifically Cloud Firestore, deployed in Firebase. With a quick glance I could see how easy it would be to create an ad-hoc Adapter to make the connection taking advantage of the capabilities of Embedded Python, so I got to work.

One of the great features in InterSystems IRIS is Monitoring InterSystems IRIS using REST API. This enables every InterSystems HealthShare instance with the ability to use a REST interface to provide statistics about the InterSystems HealthShare instance. This feature includes information about the In