Table of Contents

1. Purpose of the article
2. What containers are and why they make sense with IRIS
    2.1 Containers and images in a nutshell
    2.2 Why containers are useful for developers
    2.3 Why IRIS works well with Docker
3. Prerequisites
4. Installing the InterSystems IRIS image
    4.1 Using Docker Hub
    4.2 Pulling the image
5. Running the InterSystems IRIS image
    5.1 Starting an IRIS container
    5.2 Checking container status
    5.3 Executing code in the container terminal
    5.4 Accessing the IRIS Management Portal
    5.5 Connecting the container to VS Code
    5.6 Stopping or removing the container
    5.7 Setting a specific password with a bind mount
    5.8 Using durable %SYS volumes
     5.8.1 What gets stored with durable %SYS
     5.8.2 How to enable durable %SYS
6. Using Docker Compose
    6.1 Docker Compose example
    6.2 Running Docker Compose
7. Using a Dockerfile to run custom source code
    7.1 Dockerfile example
    7.2 Docker Compose example
    7.3 Understanding layers, image tagging and build vs. run time
    7.4 Source code and init script
    7.5 Building the image with Dockerfile
    7.6 Running instructions in the containerized IRIS terminal
8. Conclusion and what’s next

I am truly excited to continue my "InterSystems for Dummies" series of articles, and today, we want to tell you everything about one of the most powerful features we have for interoperability.

Hey, even if you have already had a go, we plan to take a really close look at how to get the most out of them and make our production even better.

Hello community,

I wanted to share my experience about working on Large Data projects. Over the years, I have had the opportunity to handle massive patient data, payor data and transactional logs while working in an hospital industry. I have had the chance to build huge reports which had to be written using advanced logics fetching data across multiple tables whose indexing was not helping me write efficient code.

Here is what I have learned about managing large data efficiently.

Choosing the right data access method.

As we all here in the community are aware of, IRIS provides multiple ways to access data. Choosing the right method, depends on the requirement.

• Direct Global Access: Fastest for bulk read/write operations. For example, if i have to traverse through indexes and fetch patient data, I can loop through the globals to process millions of records. This will save a lot of time.

Set ToDate=+H
Set FromDate=+$H-1 For  Set FromDate=$O(^PatientD("Date",FromDate)) Quit:FromDate>ToDate  Do
. Set PatId="" For  Set PatId=$Order(^PatientD("Date",FromDate,PatID)) Quit:PatId=""  Do
. . Write $Get(^PatientD("Date",FromDate,PatID)),!

• Using SQL: Useful for reporting or analytical requirements, though slower for huge data sets.

If you thought native Go support for IRIS was exciting, wait until you see what happens when GORM enters the mix.

Just recently, we welcomed native GoLang support for InterSystems IRIS with the release of go-irisnative. That was just the beginning. Now, we’re kicking things up a notch with the launch of gorm-iris — a GORM driver designed to bring the power of Object Relational Mapping (ORM) to your IRIS + Go stack.

Why GORM?

GORM is one of the most popular ORM libraries in the Go ecosystem. It makes it easy to interact with databases using Go structs instead of writing raw SQL. With features like auto migrations, associations, and query building, GORM simplifies backend development significantly.

So naturally, the next step after enabling Go to talk natively with IRIS was to make GORM work seamlessly with it. That’s exactly what gorm-iris does.

Hi,

It's me again😁, recently I am working on generating some fake patient data for testing purpose with the help of Chat-GPT by using Python. And, at the same time I would like to share my learning curve.😑

1st of all for building a custom REST api service is easy by extending the %CSP.REST

Creating a REST Service Manually

Let's Start !😂

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.

To better understand Streams in IRIS I'll start with a short

Hi all,

It's me again 😁. In the pervious article Writing a REST api service for exporting the generated FHIR bundle in JSON, we actually generated a resource DocumentReference, with the content data encoded in Base64

Hi all,

I needed to pick up the username from a set of credentials configured for a business operation in an interop production, and it turned out to take a few steps, so I'm sharing my method here, in the dual hope that someone has an easier way to do it, or failing that, that it is useful to someone.

Best,

Otto

Since we reached two important milestones for Go developers working with InterSystems IRIS:

• The Wait Is Over: Welcome GoLang Support for InterSystems IRIS — where we unveiled the new go-irisnative driver, providing a native database/sql interface for Go.
• GORM Meets InterSystems IRIS: Introducing gorm-iris — where we showed how to integrate IRIS with GORM, one of the most popular Go ORM libraries.

Now it’s time to see everything working together.

To demonstrate how easily Go developers can adopt InterSystems IRIS, I took an existing production-grade open-source project — the RealWorld Example App — which showcases a full-stack Medium.com-style clone implemented with Go Fiber, GORM, and SQLite.

With just a few configuration tweaks, I swapped out SQLite for gorm-iris, keeping everything else unchanged. The result?
A fully functional Go + Fiber application powered by InterSystems IRIS — no code rewrites, no ORM gymnastics, just a different database backend.

You can find the complete working demo here: github.com/caretdev/golang-fiber-iris-realworld-example-app

Hey Community,

Enjoy the new video on InterSystems Developers YouTube:

⏯ InterSystems Different by Design @ Ready 2025

https://www.youtube.com/embed/TdEperSTNlA
[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]

Can someone help me understand what type of user error (?) is going on here please?

One one system, I write out a group of $c() values and get the expected results:

USER>for i=250:1:260 { write i," ", $c(i),! }
250 ú
251 û
252 ü
253 ý
254 þ
255 ÿ
256 Ā
257 ā
258 Ă
259 ă
260 Ą

USER>w $zv
IRIS for Windows (x86-64) 2023.1.4 (Build 580U) Fri Apr 19 2024 11:16:07 EDT
USER>

On another system, I get some unexpected results:

With the rapid adoption of telemedicine, remote consultations, and digital dictation, healthcare professionals are communicating more through voice than ever before. Patients engaging in virtual conversations generate vast amounts of unstructured audio data, so how can clinicians or administrators search and extract information from hours of voice recordings?