Over the years, I’ve noticed that certain SQL questions come up repeatedly on the InterSystems Developer Community, especially about using the LIKE predicate in different contexts. Common variations include:

• using special characters in queries
• using variables in queries

and many more derivatives. So, I decided to write an article that focuses on how LIKE works in InterSystems IRIS SQL, especially when used with variables in Embedded SQL, Dynamic SQL, and Class Queries, while touching on pattern escaping and special character searches.

First of all, I'd like to mention that InterSystems IRIS SQL offers most of the capabilities available in other relational DBMS that implement a later version of the SQL standard. But at the same time, it's important to mention that apart from relational access, in IRIS you can also use other models to get the same data, for example, object or document models.

On this note, let's look at the LIKE predicate and how this tool is used in SQL for pattern matching.

One good thing about COVID-19 is that it taught people that online work and study are possible. I remember a week before the lockdown started, everyone said that working online was impossible, not secure, etc. And what do you know, it all became possible and "secure" in the blink of an eye. As with everything, it depends on whether a person is capable of organizing his or her day and their work or study attitude. Thus, I became a great believer in online education. As an associate professor who teaches databases, I find it beneficial to my schedule and students. One thing that studying online brings is flexibility. Another one is accepting third-party courses to grant credits to my students 😉

Considering that I'm teaching the basics of IRIS, I thought a new course, Getting Started with InterSystems IRIS for Coders, could be a relevant addition to my course. Therefore, I decided to follow the new Learning Path and see how it corresponds to my Syllabus and how many ECTS credits I can give for finishing it.

Columnar storage is one of the newer offers provided by InterSystems IRIS. Unlike traditional row-based storage, it optimizes query processing by storing data in columns rather than rows, enabling faster access and retrieval of relevant information.

A couple of articles have been written on when it should be used to give a system the biggest boost, how to create tables like that using SQL

CREATE TABLE table (column1 type1, column2 type2, column3 type3) WITH STORAGETYPE = COLUMNAR  -- ex 1
CREATE TABLE table (column1 type1, column2 type2, column3 type3 WITH STORAGETYPE = COLUMNAR)  -- ex 2

and even the performance tests.

As we all know, InterSystems IRIS is a multi-model DBMS and it gives seamless access to the same data using relational and object access. So the former is covered in other articles, but what about the latter?

Recently, the question came up while discussing the access to the data stored in IRIS from different languages with my students if it was possible to initiate the connection and get data from Cloud solution (InterSystems IRIS CloudSQL) from Microsoft Excel, not the other way around. Considering the many varied ways one can get data in Excel (import data from external sources, connecting to databases using ODBC drivers, using power queries and web queries etc.) the obvious choice was to try ODBC driver. The only task left was to try to connect to the database in the cloud using the ODBC driver.

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.

Hello,

One of my students experiences a problem these last couple of days with downloading IRIS for MacOS. She gets this error message:

Where/how can she download it?

Saw the other day an article with the usage of the %ZEN package when working with JSON and decided to write an article describing a more modern approach. At some recent point, there was a big switch from using %ZEN.Auxiliary.* to dedicated JSON classes. This allowed to work with JSONs more organically.

Thus, at this point there are basically 3 main classes to work with JSON:

• %Library.DynamicObject - provides a simple and efficient way to encapsulate and work with standard JSON documents. Also, there is a possibility instead of writing the usual code for creating an instance of a class like

set obj = ##class(%Library.DynamicObject).%New()

it is possible to use the following syntax

set obj = {}

• %Library.DynamicArray - provides a simple yet efficient way to encapsulate and work with standard JSON arrays. With arrays you can use the same approach as with objects, meaning that yu can either create an instance of the class

set array = ##class(%DynamicArray).%New()

set array = []

In this tutorial, I’d like to talk about Class Queries. To be more precise, about the Queries based on user-written code:

Because I had no idea how to build an integration solution for HL7 and didn't know where to start, I decided to follow the course Building Basic HL7 Integrations with InterSystems on Learning portal to get at least the idea of where to begin. After I studied all of it, I decided it might be a good idea to share my thoughts and reflections about it with everyone.