Introducing IRIS DocDB Explorer: A Sleek UI for Managing JSON Documents in InterSystems IRIS

IRIS offers a dedicated feature for storing and retrieving JSON documents using DocDB, without requiring a predefined schema.

Developers often need a quick and intuitive way  to browse, create, and edit documents without switching between multiple tools or writing repetitive code. To solve this, I created iris-docdb-ui — a CSP-based open-source application that provides a clean, responsive interface for interacting with DocDB effortlessly.

When I was younger (a question about exactly how much younger is outside the scope of this article), the word “token” meant fun. You see, just a couple of times a year, I would get to go to an arcade and play some amusing video games with my friends.

These days, tokens signify security. JSON Web Token (JWT) Authentication has become one of the most popular standards for securing REST APIs. Fortunately for IRIS users, we have a straightforward way to set up applications to be protected this way. Yet, the idea is still similar to my old arcade days.

Over time, while I was working with Interoperability on the IRIS Data Platform, I developed rules for organizing a project code into packages and classes. That is what is called a Naming Convention, usually. In this topic, I want to organize and share these rules. I hope it can be helpful for somebody.

Disclaimer: This guide is a good fit for Interoperability projects. When you have integrations, data flows, when you use IRIS for ETL processes, etc. Overall, when your code is combined with Productions.

Yes, yes! Welcome! You haven't made a mistake, you are in your beloved InterSystems Developer Community in Spanish.

You may be wondering what the title of this article is about, well it's very simple, today we are gathered here to honor the Inquisitor and praise the great work he performed. 

So, who or what is the Inquisitor?

Perfect, now that I have your attention, it's time to explain what the Inquisitor is. The Inquisitor is a solution developed with InterSystems technology to subject public contracts published daily on the platform  https://contrataciondelestado.es/ to scrutiny.

Hi All,

First I want give a Shout Out to @Theo Stolker  and @Rupert.Young. Because they helped me with the solution.

 When you're using the EnsLib.SQL.Snapshot as a Property in the Response Message to return Snapshot data (,e.g.: from Business Operation to Business Process,) the Snapshot data won't be cleaned with the Purge messages task/service.

Class ResponseMessage Extends Ens.Response

{

    Property SnapshotProp As EnsLib.SQL.Snapshot;

}

The data will be stuck in the global: ^Ens.AppData. You can find it with this query in System>Globals:  ^Ens.AppData("EnsLib.SQL.

InterSystems IRIS Community Edition HAOS Add-On

Run InterSystems IRIS inside of Home Assistant, as an add-on.  Before you dismiss this article possibly under the guise that this is just a gimmick, Id like you to step back and take a look at how easy it is to launch IRIS based applications using this platform.  If you look at Open Exchange, you will see dozens of dozens of applications worthy of launching while they are basically hung out to dry as gitware, and launchable if you want to get into a laptop battle with containerd or Docker.  With a simple git repo, and a specification, you can now build your app on IRIS, and make it launchable through a marketplace with limited hassle to your end users.  Run it along side Ollama and the LLM/LAM implementations, expose anything in IRIS as a sensor or expose an endpoint for interaction in your IRIS app to interact with anything you've connected to HAOS.  Wanna restart an IRIS production with a flick of a physical switch or Assisted AI? You can do it with this add-on, or your own, right alongside the home automation hackers.

I was facing the same issue as Jerry faced when connecting IRIS to SQL server. My ODBC connection is configured to authenticate via windows authentication.

Configure IRIS ODBC connection with Windows authentication using a
 

How I fixed it for myself?

Root cause

• The SQL Server DSN was set to Windows Integrated Authentication.
• IRIS opens the ODBC connection via irisdb.exe, which runs under the IRIS Windows services’ logon account.
• My IRIS services were running as LocalSystem, so SQL saw the machine account <ORGNAME>\<ASSETID>$.

Hi all,

This is a quick tip about how to use case insensitive URL in REST API.

If you have a class that extends from %CSP.REST and Ens.BusinessService, to create a REST API service, and you have defined your WebApplication in lowercase

XData UrlMap [ XMLNamespace = "http://www.intersystems.com/urlmap" ]
{
<Routes>
    <Route Url="/user" Method="POST" Call="User"/>
    <Route Url="/login" Method="POST" Call="Login"/>
</Routes>
}

Only accepts the url in lowercase, i.e. http://myserver/myproduction/user

If you have any uppercase character, the url doesn't work.

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.

Introduction

Businesses often use in-memory databases or key-value stores (caching layers) when applications require extremely high performance. However, in-memory databases incur a high total cost of ownership and have hard scalability limits, incurring reliability problems and restart delays when memory limits are exceeded. In-memory key-value stores share these limitations and introduce architectural complexity and network latency as well.

This article explains why InterSystems IRIS™ data platform is a superior alternative to in-memory databases and key-value stores for highperformance SQL and NoSQL applications.

Taking Performance and Efficiency to the Next Level

InterSystems IRIS is the only persistent database that can match or beat the performance of in-memory databases and caching layers for concurrent data ingestion and analytics processing. It can process incoming transactions, persist the data to disk, and index it for analytics in under one microsecond on commercially available hardware without introducing network latency.

The superior ingest performance of InterSystems IRIS results in part from its multi-dimensional data engine, which allows efficient and compact storage in a rich data structure. Using an efficient, multi-dimensional data model with sparse storage techniques instead of two-dimensional tables, random data access and updates are accomplished with very high performance, fewer resources and less disk capacity. It also provides in-memory, in-process APIs in addition to traditional TCP/IP access APIs to optimize ingest performance.

Hello!!!

Data migration often sounds like a simple "move data from A to B task" until you actually do it. In reality, it is a complex process that blends planning, validation, testing, and technical precision.

Over several projects where I handled data migration into a HIS which runs on IRIS (TrakCare), I realized that success comes from a mix of discipline and automation.

Here are a few points which I want to highlight.

1. Start with a Defined Data Format.

Before you even open your first file, make sure everyone, especially data providers, clearly understands the exact data format you expect. Defining templates early avoids unnecessary bank-and-forth and rework later. 

While Excel or CSV formats are common, I personally feel using a tab-delimited text file (.txt) for data upload is best. It's lightweight, consistent, and avoids issues with commas inside text fields. 

PatID   DOB Gender  AdmDate
10001   2000-01-02  M   2025-10-01
10002   1998-01-05  F   2025-10-05
10005   1980-08-23  M   2025-10-15

Make sure that the date formats given in the file is correct and constant throughout the file because all these files are usually converted from an Excel file and an Basic excel user might make mistakes while giving you the date formats wrong. Wrong date formats can irritate you while converting into horolog.

If you start with InterSystems ObjectScript, you will meet the XECUTE command.
And beginners may ask: Where and Why may I need to use this ?

The official documentation has a rich collection of code snippets. No practical case.
Just recently, I met a use case that I'd like to share with you.

The scenario:

When you build an IRIS container with Docker, then, in most cases,
you run the  initialization script  

iris session iris < iris.script 

This means you open a terminal session and feed your input line-by-line from the script.
And that's fine and easy if you call methods, or functions, or commands.

What is XML?

XML (eXtensible Markup Language) is a flexible, text-based, and platform-independent format used to store and transport data in a well-structured way that is both human- and machine-readable. XML permits users to define custom tags to describe the meaning and organization of their data. For example: <book><title>The Hitchhiker's Guide</title></book>.

An XML document is self-descriptive and structured as a hierarchical tree of elements. Every document has a single root element that encapsulates all other content.

Kamino for IrisClusters

If you are in the business of building a robust High Availability, Disaster Recovery or Stamping multiple environments rapidly and in a consistent manner Karmada may just be the engine powering your Cloning Facility.

As in the previous package, all is running from a CSP page.
And it is all classic CSP written with InterSystems ObjectScript, JavaScript, HTML
Besides the graphic, you also get concrete numbers freshly collected from your local
instance or from remote instances that also installed the package. 

The final result

The processing steps 

• A CSP page is called by its URL
• In method OnPreHTTP
  • The System Query fills the temp table from %SYS
  • If the correct URL parameter is provided 
  • ?

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

History

In the beginning (before IRIS), there was just basic access to external devices.
The 4 commands OPEN, CLOSE, READ, WRITE still work and are documented 
by  Introduction to I/O in detail.
Especially for files, this is a direct access to your actual file system.
You have to take care of any status or other signal in your code.  
Also, any code conversion or similar is up to you. 

Class %Library.File aka %File offers a large collection of methods and queries 
for standard operations on directories and files.

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.

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

Question!! Is it possible to write a REST service for decoding it? Because I am very curious what is the message data talking about🤔🤔🤔

OK, Let's start!

1. Create a new utility class datagen.utli.decodefhirjson.cls for decoding the data inside the DocumentReference
 

Class datagen.utli.decodefhirjson Extends %RegisteredObject
{
}

2. Write a Python function decodebase64docref to 
a

Hi all,

Let's do some more work about the testing data generation and export the result by REST API.😁

Here, I would like to reuse the datagen.restservice class which built in the pervious article Writing a REST api service for exporting the generated patient data in .csv

This time, we are planning to generate a FHIR bundle include multiple resources for testing the FHIR repository.

Here is some reference for you, if you want to know mare about FHIR The Concept of FHIR: A Healthcare Data Standard Designed for the Future

OK... Let's start😆

1. Create a new utility class datagen.utli.

Technical Documentation — Quarkus IRIS Monitor System

1. Purpose and Scope

This module enables integration between Quarkus-based Java applications and InterSystems IRIS’s native performance monitoring capabilities.
It allows a developer to annotate methods with @PerfmonReport, which triggers IRIS’s ^PERFMON routines automatically around method execution, generating performance reports without manual intervention.

2. System Components

2.1 Annotation: @PerfmonReport

• Defined as a CDI InterceptorBinding.
• Can be applied to methods or classes.

To manage the accumulation of production data, InterSystems IRIS enables users to manage the database size by periodically purging the data. This purge can apply to messages, logs, business processes, and managed alerts.

Please check the documentation for more details on the settings of the purge task:
https://docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls?KEY=EGMG_purge#EGMG_purge_settings

An issue that many users ran into is still finding old messages after running the purge task for messages. For example, a user has a purge task for messages that has NumberOfDaysToKeep=45.

Deploying new IRIS instances can be a time-consuming task, especially when setting up multiple environments with mirrored configurations.

I’ve encountered this issue many times and want to share my experience and recommendations for using Ansible to streamline the IRIS installation process. My approach also includes handling additional tasks typically performed before and after installing IRIS.

This guide assumes you have a basic understanding of how Ansible works, so I won’t go into much detail on its fundamentals.

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 !😂

1. Create a class datagen.restservice which extends  %CSP.REST 

Class datagen.restservice Extends %CSP.REST
{
Parameter CONTENTTYPE = "application/json";
}

2.

The ObjectScript language has incredible JSON support through classes like %DynamicObject and %JSON.Adaptor. This support is due to the JSON format's immense popularity over the previous dominance of XML. JSON brought less verbosity to data representation and increased readability for humans who needed to interpret JSON content. To further reduce verbosity and increase readability, the YAML format was created. The very easy-to-read YAML format quickly became the most popular format for representing configurations and parameterizations, due to its readability and minimal verbosity.

Introduction

In my previous article, I introduced the FHIR Data Explorer, a proof-of-concept application that connects InterSystems IRIS, Python, and Ollama to enable semantic search and visualization over healthcare data in FHIR format, a project currently participating in the InterSystems External Language Contest.

In this follow-up, we’ll see how I integrated Ollama for generating patient history summaries directly from structured FHIR data stored in IRIS, using lightweight local language models (LLMs) such as Llama 3.2:1B or Gemma 2:2B.

The goal was to build a completely local AI pipeline that can extract, format, and narrate patient histories while keeping data private and under full control.

All patient data used in this demo comes from FHIR bundles, which were parsed and loaded into IRIS via the IRIStool module. This approach makes it straightforward to query, transform, and vectorize healthcare data using familiar pandas operations in Python. If you’re curious about how I built this integration, check out my previous article Building a FHIR Vector Repository with InterSystems IRIS and Python through the IRIStool module.

Both IRIStool and FHIR Data Explorer are available on the InterSystems Open Exchange — and part of my contest submissions. If you find them useful, please consider voting for them!

• IRIStool and Data Manager
• FHIR Data Explorer

Introduction

In a previous article, I presented the IRIStool module, which seamlessly integrates the pandas Python library with the IRIS database. Now, I'm explaining how we can use IRIStool to leverage InterSystems IRIS as a foundation for intelligent, semantic search over healthcare data in FHIR format.

This article covers what I did to create the database for another of my projects, the FHIR Data Explorer. Both projects are candidates in the current InterSystems contest, so please vote for them if you find them useful.

You can find them at the Open Exchange:

• IRIStool and Data Manager
• FHIR Data Explorer

In this article we'll cover:

• Connecting to InterSystems IRIS database through Python
• Creating a FHIR-ready database schema
• Importing FHIR data with vector embeddings for semantic search

UPDATE: since version 0.0.4 of the extension was published on 2025-11-23 it is now possible to use gj :: configExplorer directly in VS Code on Windows.

In my previous article introducing gj :: configExplorer I flagged up how an apparent bug in the Windows elements of the Native API for Node.js means it's not currently available to run in VS Code on a Windows desktop. In a comment on that article I offered a workaround, but this requires a Docker-equipped Linux host you can SSH to.

If you don't have a suitable target it's now possible to leverage your local Windows Docker Desktop.

While working with external languages for IRIS (such as Python and Node.js), one of the first things you must accomplish is making a connection to an IRIS instance.

For instance, to make a connection in python (from https://pypi.org/project/intersystems-irispython/):

import iris

# Open a connection to the server
args = {
	'hostname':'127.0.0.1', 
	'port': 1972,
	'namespace':'USER', 
	'username':'username', 
	'password':'password'
}
conn = iris.connect(**args)

# Create an iris object
irispy = iris.createIRIS(conn)

# Create a global array in the USER namespace on the server
irispy.

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