Thirteen years ago, I attained dual undergraduate degrees in electrical engineering and math, then promptly started full-time at InterSystems using neither. One of my most memorable and stomach-churning academic experiences was in Stats II. On an exam, I was solving a moderately difficult confidence interval problem. I was running out of time, so (being an engineer) I wrote out the definite integral on the exam paper, punched it into my graphing calculator, wrote an arrow with “calculator” over it, then wrote the result.

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It helps to remove special characters, such as non-utf-8 characters either control characters or unicode characters from text that is not printable or can't be parsed by downstream systems.

There is also $C(32) in this condition; sometimes NBSP appears in the text and it will not be recognized by TIE, but downstream it displays as "?".

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Article
· May 2 3m read
Minify XML in IRIS

In a project I'm working on we need to store some arbitrary XML in the database. This XML does not have any corresponding class in IRIS, we just need to store it as a string (it's relatively small and can fit in a string).
Since there are MANY (millions!) of records in the database I decided to reduce as much as possible the size without compressing. I know that some XML to be stored is indented, some not, it varies.

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The Good Old Days

The %Library.DynamicObject class has been in IRIS since before it became IRIS. If you have been using it since the Cache days, you may want to brush up on some of its changes.

In Cache 2018, the %Get method only had one argument. It was the key to retrieving from the JSON, meaning that if your JSON object called myObj, it would look like the following:

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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:

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Sometimes customers need a small IRIS instance to do something in the cloud and shut it down, or they need hundreds of containers (i.e. one per end user or one per interface) with small workloads. This exercise came about to see how small an IRIS instance could be. For this exercise we focused on what is the smallest amount of memory we can configure for an IRIS instance. Do you know all the parameters that affect the memory allocated by IRIS ?

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I know that people who are completely new to VS Code, Git, Docker, FHIR, and other tools can sometimes struggle with setting up the environment. So I decided to write an article that walks through the entire setup process step by step to make it easier to get started.

I’d really appreciate it if you could leave a comment at the end - let me know if the instructions were clear, if anything was missing, or if there’s anything else you'd find helpful.

The setup includes:

✅ VS Code – Code editor
✅ Git – Version control system
✅ Docker – Runs an instance of IRIS for Health Community
✅ VS Code REST Client Extension – For running FHIR API queries
✅ Python – For writing FHIR-based scripts
✅ Jupyter Notebooks – For AI and FHIR assignments

Before you begin: Ensure you have administrator privileges on your system.

In addition to reading the guide, you can also follow the steps in the videos:

For Windows

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

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Who hasn't been developing a beautiful example using a Docker IRIS image and had the image generation process fail in the Dockerfile because the license under which the image was created doesn't contain certain privileges?

In my case, what I was deploying in Docker is a small application that uses the Vector data type. With the Community version, this isn't a problem because it already includes Vector Search and vector storage. However, when I changed the IRIS image to a conventional IRIS (the latest-cd), I found that when I built the image, including the classes it had generated, it returned this error:

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As we all know, InterSystems is a great company.

Their products can be just as useful as they are complex.

Yet, our pride sometimes prevents us from admitting that we might not understand some concepts or products that InterSystems offers for us.

Today we are beginning a series of articles explaining how some of the intricate InterSystems products work, obviously simply and clearly.

In this essay, I will clarify what Machine Learning is and how to take advantage of it.... because this time, you WILL KNOW for sure what I am talking about.

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An extension “extends” or enhances a FHIR resource or a data element in a custom way. The extension can be added to the root of a resource, such as “Patient.ethnicity” in US Core profile, and they can be added to individual elements such as HumanName, Address or Identifier.

Did you know that you can also add an extension to a primitive data type?

Primitives usually store a single item and are the most basic element in FHIR. For example: "Keren", false, 1234, 12/08/2024 etc.

For example, the patient resources might look like this:

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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?

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Here at InterSystems, we often deal with massive datasets of structured data. It’s not uncommon to see customers with tables spanning >100 fields and >1 billion rows, each table totaling hundred of GB of data. Now imagine joining two or three of these tables together, with a schema that wasn’t optimized for this specific use case. Just for fun, let’s say you have 10 years worth of EMR data from 20 different hospitals across your state, and you’ve been tasked with finding….

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I'm sure most of you are familiar with utility %SYS.MONLBL that is crucial when analysing code performance bottlenecks. It allows you to select a number of routines that you want to monitor at runtime and also specify what process(es) you want to watch. BUT, what if you do not know exactly, what process would execute your code? This is true with many web based (CSP/REST) applications today. You want to minimize the resource utilization on your production system that needs analysis. So, how about doing a small tweak?

1. Define an INC file with these macros:

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I recently had to refresh my knowledge of the HealthShare EMPI module and since I've been tinkering with IRIS's vector storage and search functionalities for a while I just had to add 1 + 1.

For those of you who are not familiar with EMPI functionality here's a little introduction:

Enterprise Master Patient Index

In general, all EMPIs work in a very similar way, ingesting information, normalizing it and comparing it with the data already present in their system. Well, in the case of HealthShare's EMPI, this process is known as NICE:

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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.

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If one of your packages on OEX receives a review you get notified by OEX only of YOUR own package.
The rating reflects the experience of the reviewer with the status found at the time of review.
It is kind of a snapshot and might have changed meanwhile.
Reviews by other members of the community are marked by * in the last column.

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RabbitMQ is a message broker that allows producers (those who send a data message) and consumers (those who receive a data message) to establish asynchronous, real-time, and high-performance massive data flows. RabbitMQ supports AMQP (Advanced Message Queuing Protocol), an open standard application layer protocol.
The main reasons to employ RabbitMQ include the following:

  • You can improve the performance of the applications using an asynchronous approach.
  • It lets you decouple and reduce dependencies between services, microservices, and applications with the help of a data message mediator, meaning that there is no need for producers and consumers of exchanged data to know each other.
  • It allows the long-running processing of sent data (with the results) to be delivered after utilizing a response queue.
  • It helps you migrate from monolithic to microservices, where microservices exchange data via Rabbit in a decoupled and asynchronous way.
  • It offers reliability and resilience by making it possible for messages to be stored and forwarded. A message can be delivered multiple times until it is processed.
  • Message queueing is the key to scaling your application. As the workload increases, you will only have to add more workers to handle the queues faster.
  • It works well with data streaming applications.
  • It is beneficial for IoT applications.
  • It is a must for Bots’ communication.

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For my hundredth article on the Developer Community, I wanted to present something practical, so here's a comprehensive implementation of the GPG Interoperability Adapter for InterSystems IRIS.

Every so often, I would encounter a request for some GPG support, so I had several code samples written for a while, and I thought to combine all of them and add missing GPG functionality for a fairly complete coverage. That said, this Business Operation primarily covers data actions, skipping management actions such as key generation, export, and retrieval as they are usually one-off and performed manually anyways. However, this implementation does support key imports for obvious reasons. Well, let's get into it.

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When using standard SQL or the object layer in InterSystems IRIS, metadata consistency is usually maintained through built-in validation and type enforcement. However, legacy systems that bypass these layers—directly accessing globals—can introduce subtle and serious inconsistencies.

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Regardless of whether an instance of IRIS is in the cloud or not, high availability and disaster recovery are always important considerations. While IKO already allows for the use of NodeSelectors to enforce the scheduling of IRISCluster nodes across multiple zones, multi-region k8s clusters are generally not recommended or even supported in the major CSP's managed Kubernetes solutions. However, when discussing HA and DR for IRIS, we may want to have an async member in a completely separate region, or even in a different cloud provider altogether.

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Hi developers!

Observing the avalanche of AI-driven and vibe-coding developer tools that have been appearing lately almost every month with more and more exciting dev features, I was puzzled whether it is possible to leverage it with InterSystems IRIS. At least to build a frontend. And the answer - yes! At least with the approach I followed.

Here is my recipe to prompt the UI vs InterSystems IRIS Backend:

  1. Have the REST API on the IRIS side, which reflects some Open API (swagger) spec.
  2. Generate the UI with any vibe-coding tool (e.g., Lovable) and point the UI to the REST API endpoint.
  3. Profit!

Here is the result of my own exercise - a 100% prompted UI vs IRIS REST API that allows to list, create, update delete entries of a persistent class (Open Exchange, frontend source, video):

What is the recipe in detail?

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Article
· May 12 7m read
An Overview of Database Degrade

Introduction

Hello! In this article, I will be discussing database degrade, a type of data integrity issue one can face when using IRIS. First, I will be going over a review of the structure of IRIS databases. I'll then discuss how database degrade can manifest and common causes of degrade issues. I'll then conclude with general tips we give our customers about how to prevent or prepare for database degrade issues.

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The first part of this article provides all the background information. It also includes links to the DATATYPE_SAMPLE database, which you can use to follow along with the examples.

In that section, we explored an error type ("Access Failure") that is easy to detect, as it immediately triggers a clear error message when attempting to read the data via the database driver.

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