#InterSystems IRIS

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InterSystems IRIS is a Complete Data Platform
InterSystems IRIS gives you everything you need to capture, share, understand, and act upon your organization’s most valuable asset – your data.
As a complete platform, InterSystems IRIS eliminates the need to integrate multiple development technologies. Applications require less code, fewer system resources, and less maintenance.

Hi community!

I am excited to say that since the beginning of this year we have published many of the client SDKs for InterSystems IRIS, InterSystems IRIS for Health and Health Connect to the corresponding external repositories (Maven, NuGet, npm and PyPI). This provides many benefits to you such as:

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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

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Apache Airflow is the leading open-source platform to programmatically author, schedule, and monitor data pipelines and workflows using Python. Workflows are defined as code (DAGs), making them version-controlled, testable, and reusable. With a rich UI, 100+ built-in operators, dynamic task generation, and native support for cloud providers, Airflow powers ETL/ELT, ML pipelines, and batch jobs at companies like Airbnb, Netflix, and Spotify.

Airflow Application Layout

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Over the past several years, the InterSystems Developer Community has accumulated more than 1,000 open-source projects. Many of them serve as examples and learning materials — but a significant number have become useful tools, libraries, integrations, and real-world components used in production.

Some of these projects are mine, and like many community developers, I’ve seen the same recurring problem:

  • It’s easy to create an open-source project.
  • It’s hard to maintain, support, and develop it sustainably — especially without funding.

Writing code is one thing.
Supporting it for years, keeping up with new IRIS versions, building CI pipelines, writing documentation, fixing issues, reviewing PRs — all of this demands both time and motivation, and the biggest motivator is often financial support.

This is not a new challenge.
The global open-source world has faced this for decades and has developed various models to support OSS ecosystems.

I believe it’s time for the InterSystems community to start a discussion about adopting a similar model.

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Modern SQL engines are enormously complex pieces of software. Even when they appear stable and mature, subtle bugs can hide in their optimizers, type systems, predicate evaluation, or execution layers. These bugs rarely announce themselves loudly. Instead, they quietly produce incorrect results, behave inconsistently, or fail abruptly under specific combinations of SQL constructs.

This is precisely why tools like SQLancer exist. SQLancer automatically generates SQL queries and uses logical “oracles” to detect when a database behaves incorrectly. It has revealed hundreds of real bugs in widely used systems such as MySQL, PostgreSQL, SQLite, and DuckDB.

With this in mind, I attempted to bring SQLancer to InterSystems IRIS, starting with the NOREC oracle — a powerful method for detecting optimizer correctness issues. The journey, however, uncovered not just potential SQL correctness problems, but also a surprising number of driver-level and server-level failures that prevented SQLancer from running at full strength.

This article summarizes why SQLancer is important, how the NOREC oracle works, and what unexpected findings appeared while testing IRIS.

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The 2025.3 release of InterSystems IRIS® data platform, InterSystems IRIS® for Health, and HealthShare® Health Connect is now Generally Available (GA). This is a Continuous Delivery (CD) release.

Release Highlights:

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Modern data architectures utilize real-time data capture, transformation, movement, and loading solutions to build data lakes, analytical warehouses, and big data repositories. It enables the analysis of data from various sources without impacting the operations that use them. To achieve this, establishing a continuous, scalable, elastic, and robust data flow is essential. The most prevalent method for that is through the CDC (Change Data Capture) technique. CDC monitors for small data set production, automatically captures this data, and delivers it to one or more recipients, including analytical data repositories. The major benefit is the elimination of the D+1 delay in analysis, as data is detected at the source as soon as it is produced, and later is replicated to the destination.

This article will demonstrate the two most common data sources for CDC scenarios, both as a source and a destination. For the data source (origin), we will explore the CDC in SQL databases and CSV files. For the data destination, we will use a columnar database (a typical high-performance analytical database scenario) and a Kafka topic (a standard approach for streaming data to the cloud and/or to multiple real-time data consumers).

Overview

This article will provide a sample for the following interoperability scenario:

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In the previous article, we talked about ODBC and connecting from C#. And now, let's look at JDBC and Java. The InterSystems JDBC driver is the recommended, high-performance way to integrate your Java applications.

Here is a step-by-step guide to getting your Java application connected to an IRIS instance using the JDBC driver.

Step 1: Obtain and Include the InterSystems IRIS JDBC Driver

Unlike ODBC drivers, which are often installed system-wide, JDBC drivers are typically distributed as JAR files that must be included in your Java project's classpath.

If InterSystems IRIS is installed on your local machine or another you have access to, you can find the file in install-dir/dev/java/lib/ or similar, where install-dir is the installation directory for the instance. Conversely, you can download the jar file from Driver packages page.

Or as suggested by @Dmitry Maslennikov in the comments, use the maven central repository for Maven:

<dependency>
    <groupId>com.intersystems</groupId>
    <artifactId>intersystems-jdbc</artifactId>
    <version>3.10.5</version>
</dependency>

or for Gradle:

implementation("com.intersystems:intersystems-jdbc:3.10.5")

Include the jar file in Project:

  • Maven/Gradle: If you use a build tool, the simplest method is to add the InterSystems JDBC driver as a dependency in your pom.xml or build.gradle file. This automatically downloads and manages the JAR.
  • Manual: For simple projects, you must place the JAR file in a project directory (e.g., /lib) and explicitly add it to your classpath when compiling and running.

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We are a group of interface analysts in a healthcare setting, running IRIS for Health 2024, having upgraded over time from an old ENSEMBLE environment. We have been working on a wish list of development goals, but are having trouble finding the correct, best practice method/path for getting to what we envision.

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In some cases, it's necessary to manipulate data from one namespace to another. For example, a routine in the "N1" namespace needs data from the "N2" namespace. In legacy systems (using only globals), it's common to make the global universal, but what about persistent classes? Is this type of globalization also possible?

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Hello

I'm using from visual code. My routines are server-side files. If I try to search patterns in files (e.g find all routines containing the string "version=3") , the search runs but didn't find anything. If I do the same search from the management portal, it finds all routines including this pattern with a good performance.

regards Matthias

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Hello!
I have the following XML document obtained through a string:

<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<MainDocument xmlns="urn:hl7-org:v3">
<realmCode code="IT"/>
<title>kjbkjkjbkjb</title>
<effectiveTime value="20090905150716"/>
[.....other tags.....]
<versionNumber value="1"/>
<component>
<body>mhvjhjkvhj</body>
<component>
<section>content</section>
<ID>5</ID>

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The Interoperability user interface project has continued from 2025.1 and has incorporated many of the items that you – our customers and partners – have suggested and observed. We are continuing to invest in feedback and updating this important user experience. In the latest relea

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I'm pleased to announce the publication of gj :: dataLoader, a new VS Code extension that simplifies the task of loading data from local CSV files into SQL tables on your InterSystems IRIS servers.

Here's an introductory video:

https://www.youtube.com/embed/XohVoW5rSy4
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Hello Community,

Great news for developers who have just started working with InterSystems IRIS! We have hands‑on interactive tutorials available via the Instruqt platform! These are perfect for getting up to speed quickly, playing in real environments, and building confidence with IRIS‑based development.

Here is the list of available tutorials:

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In this article, we will discuss all the debugging tools included in the Microsoft Visual Studio Code IDE.

What will be covered:

  • Breakpoints
  • Watch window
  • Call Stack

Let's start by learning about debugging requirements!

Prerequisites

There are two plugins (extensions) for debugging ObjectScript:

The first is part of the InterSystems ObjectScript Extension Pack. The second is Serenji, a standalone plugin that provides an editor, file manager, and debugging functionality. Both plugins can be installed from the plugin store. To activate key functionality, Serenji requires a license. For this article, we'll use the InterSystems ObjectScript Extension Pack to reduce the learning curve. After you've mastered the basics, you can consider purchasing a paid license for Serenji.

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I am trying to add some validation to an existing Record Map, because recently we had some wrong data get consumed into the System and is causing all kinds of havoc.

Using the AI on the Developer community it suggested that I use PATTERN within the Datatype Parameters to force some validation on some fields. I have never used PATTERN before...

When I go to generate the updated Record Map, I am getting the following...

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In my previous article, I structured network communications
in these 3 possible layers, and covered the last

  • Client <---> Transport
  • Server <---> Transport
  • Client <---> Server

In fact, you have the most control over the last one.
The IRIS side as a server is yours and under your full control.
Up to now, the Transport layer was assumed to be as passive as a bare wire.

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Project Overview / Introduction

Developing and testing REST APIs in InterSystems IRIS often requires a significant amount of boilerplate code. While ObjectScript provides powerful tools for building APIs, writing consistent and repeatable tests can be time‑consuming. This is where IrisOASTestGen comes in.

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Hola amigo! 😊 Cómo estás hoy,

I would like to share a small part of my learnings from my first ever official project: POS/EDC machine integration with our billing system. This was an exciting challenge where I got hands-on experience working with APIs and vendors.

How does a Payment Machine actually work?

It's simple, start by initiating/creating a transaction, then retrieve its payment status.

Here, initiate/create refers to POST method and Retrieve refers to GET.

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