InterSystems Developer Community is a community of 25,835 amazing developers
We're a place where InterSystems IRIS programmers learn and share, stay up-to-date, grow together and have fun!
Learn more

The "Ask Developer Community AI" tool is an excellent resource for studying for the certification. I asked it about each topic that will be covered in the test and the results are below.
Note: I classified each answer by the assertiveness that I consider as good, average and bad.

Note 2: The article has 4 parts, each one for an exam area.

7 0
3 367
Article
· Feb 26, 2025 6m read
The Case for IRIS and JavaScript

Introduction

My guess is that most IRIS developers create their applications using its native ObjectScript language or, if using an external language, then most likely using either Java, Python or perhaps C++.

I suspect that only a minority have considered using JavaScript as their language of choice, which, if true, is a great shame, because, In my opinion and experience, JavaScript is the closest equivalent to ObjectScript in terms of its ability to integrate with the IRIS's underlying multi-dimensional database.

15 1
2 346
Article
· Jun 18, 2025 2m read
Options for Python Devs + Poll!

I am writing this post primarily to gather an informal consensus on how developers are using Python in conjunction with IRIS, so please respond to the poll at the end of this article! In the body of the article, I'll give some background on each choice provided, as well as the advantages for each, but feel free to skim over it and just respond to the poll.

4 5
2 251

Hi Community,

We’re excited to share a brand-new Instruqt tutorial:

🧑‍🏫 RAG using InterSystems IRIS Vector Search

This hands-on lab walks you through building a Retrieval Augmented Generation (RAG) AI chatbot powered by InterSystems IRIS Vector Search. You’ll see how vector search can be leveraged to deliver up-to-date and accurate responses, combining the strengths of IRIS with generative AI.

✨ Why try it?

10 7
3 209

Introduction

Database performance has become a critical success factor in a modern application environment. Therefore identifying and optimizing the most resource-intensive SQL queries is essential for guaranteeing a smooth user experience and maintaining application stability.

This article will explore a quick approach to analyzing SQL query execution statistics on an InterSystems IRIS instance to identify areas for optimization within a macro-application.

Rather than focusing on real-time monitoring, we will set up a system that collects and analyzes statistics pre-calculated by IRIS once an hour. This approach, while not enabling instantaneous monitoring, offers an excellent compromise between the wealth of data available and the simplicity of implementation.

We will use Grafana for data visualization and analysis, InfluxDB for time series storage, and Telegraf for metrics collection. These tools, recognized for their power and flexibility, will allow us to obtain a clear and exploitable view.

More specifically, we will detail the configuration of Telegraf to retrieve statistics. We will also set up the integration with InfluxDB for data storage and analysis, and create customized dashboards in Grafana. This will help us quickly identify queries requiring special attention.

To facilitate the orchestration and deployment of these various components, we will employ Docker.

logos.png

Open Exchange app
6 0
4 349

It's been a while since the new UI for Productions and DTL was published as a preview and I would like to know your opinions about it.

WARNING: This is a personal opinion, totally personal and not related with InterSystems Corporation.

I'm going to start with the Interoperabilty screen:

Production screen:

The style is sober and without frills, following the line of cloud services design, I like it.

3 6
0 340

My usecase is sorting and removing duplicates and getting count from a file that has json messages as a individual rows.

I am currently planning to use pandas for this purpose as its really fast. Below are the steps i am following

1) call a python function (called function) from IRIS classmethod(calling function)

2) the call python function will read the json file in a dataframe

3) perform sorting, dup removal, count in the dataframe

4) convert the dataframe into iris stream

5) return back the stream to iris calling function class method

0 6
0 340

When working with InterSystems IRIS, database developers and architects often face a critical decision: whether to use Dynamic SQL or Embedded SQL for querying and updating data. Both methods have their unique strengths and use cases, but understanding their performance implications is essential to making the right choice. Response time, a key metric in evaluating application performance, can vary significantly depending on the SQL approach used. Dynamic SQL offers flexibility, as queries can be constructed and executed at runtime, making it ideal for scenarios with unpredictable or highly variable query needs. Conversely, Embedded SQL emphasizes stability and efficiency by integrating SQL code directly into application logic, offering optimized response times for predefined query patterns.

In this article, I will explore the response times when using these two types of SQL and how they depend on different class structures and usage of parameters. So to do this, I'm going to use the following classes from the diagram:

10 3
2 285

The most upvoted idea on the InterSystems Ideas Portal—garnering 74 votes—requests a lightweight version of IRIS. While the platform has grown into a powerful data engine, many projects require only its SQL database capabilities. This article demonstrates how to build an unofficial, compact IRIS Community Edition image focused solely on core database functionality, reducing the image size by over 80%.

⚠️ Disclaimer

This project produces an unofficial, experimental image of InterSystems IRIS Community Edition.

  • Not supported or endorsed by InterSystems.
  • Use at your own risk. The modifications remove core platform features and may break compatibility with tools, APIs, and expected behaviors.
  • No warranties or guarantees apply, including fitness for production use.
  • Intended only for educational and experimental purposes by advanced users.

Why a Lightweight IRIS?

While IRIS today includes rich functionality—interoperability, analytics, machine learning, system management, etc.—many projects only require its core SQL capabilities. The official Community Edition Docker image is approximately:

  • Disk usage: 3.5–3.8 GB
  • Compressed size: ~1.1 GB

IRIS Light reduces that to:

  • Disk usage: ~575–583 MB
  • Compressed size: ~144–148 MB

This makes it suitable for:

  • Microservice or containerized SQL use
  • CI pipelines with faster startup and pull
  • Horizontal scaling where full features are unnecessary

Open Exchange app
10 7
0 204

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. However I was surprised to find that IRIS is in several ways quite unlike other databases, often much better. With this, my first article in the Dev Community, I'll give a high-level overview of IRIS for people that are already familiar with the other databases such as Oracle, SQL Server, Snowflake, PostgeSQL, etc. I hope I can make things clearer and simpler for you and save you some time getting started.

9 1
1 323

My main goal of this article was to prove the use of InterSystems IRIS for Health for REST FHIR interoperability between multiple applications. In this use case, some initiating application makes a REST call to IRIS for Health (which is merely a passthrough for REST calls) to retrieve FHIR data from an Oracle Health R4 FHIR repository. Ideally, it simplifies the syntax for calling the Oracle Health APIs.

9 0
2 341

One of the challenges of creating a DICOM message is how to implement putting data in the correct place. Part of it is by inserting the data in the specific DICOM tags, while the other is to insert binary data such as a picture - In this article I will explain both.

To create a DICOM message, you can either use the EnsLib.DICOM.File class (to create a DICOM file) or the EnsLib.DICOM.Document class (to create a message that can be sent to PACS directly). In either case, the SetValueAt method will allow you to add your data to the DICOM tags.

Open Exchange app
10 0
0 335

Hi colleagues!

Often, while developing a frontend app or any other communication vs REST API, it is worth having a Swagger UI - a test UI for the REST API that follows Open API 2.0 spec. Usually, it is quite a handful as it lets have quick manual tests vs REST API and its responses and the data inside.

Recently I've introduced the Swagger support to the InterSystems IRIS FHIR template for FHIR R4 API:

How to get it working.

Open Exchange app
11 0
3 331

Dear Community,

As the 🎄 Festive Season 🎄 approaches, we’re excited to send our warmest wishes your way. May your holidays be filled with the joy of 🧑‍💻 learning, 🫂 connecting with fellow developers, and the thrill of new ideas and challenges waiting in the year ahead!

Looking back on 2025, we’re delighted to celebrate another year of remarkable achievements together with YOU, our incredible members:

14 7
1 189

Hi Community,

In this article, I will introduce my application iris-AgenticAI .

The rise of agentic AI marks a transformative leap in how artificial intelligence interacts with the world—moving beyond static responses to dynamic, goal-driven problem-solving. Powered by OpenAI’s Agentic SDK , The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, Swarm.
This application showcases the next generation of autonomous AI systems capable of reasoning, collaborating, and executing complex tasks with human-like adaptability.

Application Features

  • Agent Loop 🔄 A built-in loop that autonomously manages tool execution, sends results back to the LLM, and iterates until task completion.
  • Python-First 🐍 Leverage native Python syntax (decorators, generators, etc.) to orchestrate and chain agents without external DSLs.
  • Handoffs 🤝 Seamlessly coordinate multi-agent workflows by delegating tasks between specialized agents.
  • Function Tools ⚒️ Decorate any Python function with @tool to instantly integrate it into the agent’s toolkit.
  • Vector Search (RAG) 🧠 Native integration of vector store (IRIS) for RAG retrieval.
  • Tracing 🔍 Built-in tracing to visualize, debug, and monitor agent workflows in real time (think LangSmith alternatives).
  • MCP Servers 🌐 Support for Model Context Protocol (MCP) via stdio and HTTP, enabling cross-process agent communication.
  • Chainlit UI 🖥️ Integrated Chainlit framework for building interactive chat interfaces with minimal code.
  • Stateful Memory 🧠 Preserve chat history, context, and agent state across sessions for continuity and long-running tasks.

Open Exchange app
6 0
0 328

gj :: configExplorer is a new VS Code extension integrating with Server Manager and leveraging Structurizr to produce configuration diagrams of your servers.

Here's a short introductory video.

https://www.youtube.com/embed/WHkoZsg6P-A
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

Open Exchange app
3 7
0 184