When we need to integrate Caché/IRIS with other relational databases, one common question arises: “How do I set up the JDBC connection?”.
The official documentation doesn’t always provide a straightforward step-by-step guide, which can be frustrating, especially for beginners.

In this article, I’ll walk you through the entire process of configuring a JDBC connection with MySQL, from downloading the connector to linking tables in Caché/IRIS.

Note: JDBC connections in Caché/IRIS have some limitations, especially regarding caching and the use of ResultSet

As mentioned in the previous article, it is possible to configure the way you wish your collections to be stored and queried in SQL.

To set this up, you will need to use the following field parameters and settings:

In InterSystems IRIS, when you compile a persistent class, you automatically get a SQL table. Sometimes, there are situations that require having a slightly (or not so slightly) different names/options in SQL compared to Object model. Here are some of the settings that you can change to make it happen.

Class-level settings 

They define the "Face" of your class when it appears as a table in the SQL catalog.

SqlTableName allows you to change the name of SQL table that holds objects from your class. This is especially useful, when your class name uses reserved SQL words in a name, e.g. Group.

I joined InterSystems less than a year ago. Diving into ObjectScript and IRIS was exciting, but also full of small surprises that tripped me up at the beginning. In this article I collect the most common mistakes I, and many new colleagues, make, explain why they happen, and show concrete examples and practical fixes. My goal is to help other new developers save time and avoid the same bumps in the road.

1. Getting lost among system classes and where to start

The issue: ObjectScript/IRIS ships with many system classes and packages (%Library, %SYS, %Persistent, %SQL, etc.).

Ever since I started using IRIS, I have wondered if we could create agents on IRIS. It seemed obvious: we have an Interoperability GUI that can trace messages, we have an underlying object database that can store SQL, Vectors and even Base64 images. We currently have a Python SDK that allows us to interface with the platform using Python, but not particularly optimized for developing agentic workflows. This was my attempt to create a Python SDK that can leverage several parts of IRIS to support development of agentic systems.

We were testing upgrades on our internal systems recently, and were initially surprised to see a change in some instances of SQL behavior. On further investigation, the changes make a lot of sense, and are easy to adapt to. But, to hopefully save you some time and provide additional information in case this is something you too find curious or interesting, we thought we would share our understanding of the changes and how we got around it to preserve previous behavior where desired.

Starting in IRIS version 2024.2.

This is the second part of an article pair where I walk you through:

• Part I - Intro and Quick Tour (the previous article)
  • What is it?
  • Spinning up an InterSystems IRIS Cloud Document deployment
  • Taking a quick tour of the service via the service UI
• Part II - Sample (Dockerized) Java App (this article)
  • Grabbing the connection details and TLS certificate
  • Reviewing a simple Java sample that creates a collection, inserts documents, and queries them
  • Setting up and running the Java (Dockerized) end‑to‑end sample

As mentioned the goal is to give you a smooth “first run” experience.

Customer support questions span structured data (orders, products 🗃️), unstructured knowledge (docs/FAQs 📚), and live systems (shipping updates 🚚). In this post we’ll ship a compact AI agent that handles all three—using:

• 🧠 Python + smolagents to orchestrate the agent’s “brain”
• 🧰 InterSystems IRIS for SQL, Vector Search (RAG), and Interoperability (a mock shipping status API)

⚡ TL;DR (snack-sized)

• Build a working AI Customer Support Agent with Python + smolagents orchestrating tools on InterSystems IRIS (SQL, Vector Search/RAG, Interoperability for a mock shipping API).

While starting with Intersystems IRIS or Cache, developers often encounter three core concepts: Dynamic Objects, Globals & Relational Table. Each has its role in building scalable and maintainable solutions. In this article, we'll walk through practical code examples, highlight best practices, and show how these concepts tie together. 

1. Working with Dynamic Objects:

Dynamic objects (%DynamicObject and %DynamicArray) allow developers to manipulate JSON-like structures directly in Objectscript. They are especially useful for modern applications that need to parse, transform or generate JSON.

If you already know Java (or .Net) and perhaps also have used other document databases (or looking for one), but you are new to the InterSystems world, this post should help you.

InterSystems IRIS Cloud Document is a fully managed document database that lets you store JSON documents and query them with familiar SQL syntax, delivered as a cloud service managed by InterSystems.

In this article pair I’ll walk you through:

• Part I - Intro and Quick Tour (this article)
  • What is it?
  • Spinning up an InterSystems IRIS Cloud Document deployment
  • Taking a quick tour of the service via the service UI
• Part II - Sample (Dockerized) Java App (the next article)
  • Grabbing the connection details and TLS certificate
  • Reviewing a simple Java sample that creates a collection, inserts documents, and queries them
  • Setting up and running the Java (Dockerized) end‑to‑end sample

The goal is to give you a smooth “first run” experience.

Migrating from Oracle, MSSQL, or other purely relational database systems to a multimodel InterSystems IRIS is a strategic decision that requires careful planning and execution. While this transition offers significant benefits, including enhanced performance, scalability, and support for modern architectures, it also comes with challenges. In this article I will highlight some of the considerations connected to coding to ensure a successful migration. I will leave everything connected to an actual migration of structures and data outside the scope of this article.

First, when you're considering migrating to a different database system, you need to understand your business logic, whether it's on the side of the application (application server) or the database server. Basically, where do you have your SQL statements that you will need to potentially rewrite? 

In my previous article, Using LIKE with Variables and Patterns in SQL, we explored how the LIKE predicate behaves in different scenarios, from Embedded SQL to Dynamic SQL, and what happens to performance when wildcards and variables come into play. That piece was about getting comfortable writing a working LIKE query. But writing SQL that works is only the starting point. To build applications that are reliable, scalable, and secure, you need to understand the best practices that underpin all SQL, including queries that use LIKE.

This article takes the next step. We’ll look at a few key points to help strengthen your SQL code, avoid common pitfalls, and make sure your SELECT statements run not just correctly, but also efficiently and safely. I'll use SELECT statements with LIKE predicate as an example along the way, showing how these broader principles directly affect your queries and their results.

*This is what Gemini came up with for this article, kinda cute.

Are you familiar with SQL databases, but not familiar with IRIS?  Then read on...

About a year ago I joined InterSystems, and that is how IRIS got on my radar.  I've been using databases for over 40 years—much of that time for database vendors—and assumed IRIS would be largely the same as the other databases I knew.

Apache Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.

And now it is possible to use with InterSystems IRIS as well.

An online demo is available and it uses IRIS Cloud SQL as a data source.

Apache Superset provides a bunch of examples, which were successfully loaded to IRIS without any issues, and displayed on example dashboards.