This is an excellent candidate for a developer community post (like Dev.to, Medium, or the InterSystems Community). It bridges the gap between high-level architecture and hands-on implementation.

Here is the summarized article format.

Building a Robust Asynchronous Queue Manager with InterSystems IRIS and Angular

As applications scale, handling heavy computational tasks synchronously becomes a bottleneck. Whether it's processing large data sets, sending high-volume emails, or managing API integrations, a decoupled architecture is essential.

Hey Community!

We're happy to share a new video from our InterSystems Developers YouTube:

     ⏯  Optimizing Query Performance in Health Insight @ Ready 2025

1-command only required for an entire IRIS instance for Data Science projects, and leveraging this to compare query methods' speed (Dynamic SQL, Pandas Query, and Globals).

Before joining InterSystems, I worked in a team of web developers as a data scientist. Most of my day-to-day work involved training and embedding ML models in Python-based backend applications through microservices, mainly built with the Django framework and using Postgres SQL for sourcing the data.

Introduction

The recent addition of FIFO groups allows First-In, First-Out (FIFO) message processing to be maintained in an interoperability production even when a Pool Size is greater than 1, enabling higher performance without sacrificing correctness. This feature first appears in InterSystems IRIS® data platform, InterSystems IRIS® for Health, and InterSystems Health Connect™ in version 2025.3.

First-In, First-Out message processing is critical in many integration scenarios, especially in healthcare. Traditionally, FIFO ordering is enforced by configuring each business host to process only one message at a time (Pool Size = 1). While effective, this approach can limit throughput and underutilize system resources. FIFO groups preserve FIFO ordering where needed without requiring a Pool Size of 1.

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….

Motivation

I didn't know about ObjectScript until I started my new job. Objectscript isn't actually a young programming language. Compared to C++, Java and Python, the community isn't as active, but we're keen to make this place more vibrant, aren't we?

I've noticed that some of my colleagues are finding it tricky to get their heads around the class relationships in these huge projects. There aren't any easy-to-use modern class diagram tool for ObjectScript.

Related Work

I have tried relavant works:

- InterSystems class view:
1. https://github.

Hi Everyone!

We have an existing process (running in FIFO order), all business hosts having pool size=1.

Currently we are reading records from a file (one record at a time) then that record goes to business process for further processing and finally through the business operation. As of now we are using synchronous call in our existing code. Before processing the last record we are using hang of 50 seconds because we need to initiate a batch once the processing of last record is finished.

This process is taking too much time for a file having 0.1 million records (max records in a file).

Embeddedpy-bridge: A Toolkit for Embedded Python

Overview

Embedded Python is a game-changer for InterSystems IRIS, offering access to the vast Python ecosystem directly within the database. However, bridging the gap between ObjectScript and Python can sometimes feel like translating between two different worlds.

To make this transition seamless using embeddedpy-bridge.

This package is a developer-centric utility kit designed to provide high-level ObjectScript wrappers, familiar syntax, and robust error handling for Embedded Python.

The Rut

Up until early this year, I haven't been not doing much coding at all -- I had gotten sick of it.

After many years as a hands-on software engineer and data scientist, I got burned out around 2015. I switched to business development roles focused on "external innovation," then joined InterSystems in 2019 as a product manager. I missed the creative aspects of coding, but not the tedium. The endless cycle of boilerplate, debugging, and context-switching had left me creatively depleted.

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.

Looking at my database I see I have a very big ^rINDEXSQL global? Why is that? 😬

In the Management Portal SQL page, under "SQL Statements" I see a 'Clean stale' button - what does this do? 🤔

In the list of Statements some have a 'Location' value and some don't? How is that? 🤨

FastJsonSchema: High-Performance JSON Validation in IRIS

Validating JSON data against JSON Schema is a common requirement for modern applications. FastJsonSchema brings this capability natively to InterSystems IRIS, combining speed, simplicity, and full schema compliance.

Unlike traditional validation approaches, FastJsonSchema generates native ObjectScript code from your JSON Schemas and compiles it directly to iris object code, enabling idiomatic performance without relying on external libraries or runtimes.

In Part 1, we explored how window functions operate. We learned the logic behind PARTITION BY, ORDER BY, and such functions as ROW_NUMBER() and RANK(). Now, in Part 2, let's delve into more window functions with practical examples.

1. Aggregate-over-Window Functions

Overview

These functions compute an aggregate (e.g., sum, average, min, max, count, etc.) over the defined window frame but don’t collapse rows.
Each row remains visible, augmented with aggregated values for its partition.

Supported functions include the following:

• AVG() — average of values in the window frame.

Since the introduction of Embedded Python there has always been doubt about its performance compared to ObjectScript and on more than one occasion I have discussed this with @Guillaume Rongier , well, taking advantage of the fact that I was making a small application to capture data from public competitions in Spain and to be able to perform searches using the capabilities of VectorSearch I saw the opportunity to carry out a small test.

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:

• Secure Wallet: A new encrypted framework for managing sensitive data, built on the IRISSECURITY database, improving system security and access control.
• Expanded Observability & Cloud Integrations: Enhanced OpenTelemetry metrics, including new process and ECP metrics, plus journal archiving now supports Azure Blob Storage for cost-efficient retention.

APM normally focuses on the activity of the application but gathering information about system usage gives you important background information that helps understand and manage the performance of your application so I am including the IRIS History Monitor in this series.

In this article I will briefly describe how you start the IRIS or Caché History Monitor to build a record of the system level activity to go with the application activity and performance information you gather. I will also give examples of SQL to access the information.

What is the IRIS or Caché History Monitor?

High-Performance Message Searching in Health Connect

The Problem

Have you ever tried to do a search in Message Viewer on a busy interface and had the query time out? This can become quite a problem as the amount of data increases. For context, the instance of Health Connect I am working with does roughly 155 million Message Headers per day with 21 day message retention. To try and help with search performance, we extended the built-in SearchTable with commonly used fields in hopes that indexing these fields would result in faster query times. Despite this, we still couldn't get some of these queries to finish at all.

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=+HSet FromDate=+$H-1ForSet FromDate=$O(^PatientD("Date",FromDate)) Quit:FromDate>ToDate  Do
. Set PatId=""ForSet 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.

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.

I need to build an integration solution that reads messages from a Kafka topic. The topic has 3 partitions and contains several million messages.

For certain reasons, I can only use the standard EnsLib.Kafka.Service class and cannot use either KafkaClient or Python.

To measure performance and collect statistics I created a simple key + timestamp table with no indexes (so it is unlikely to be a bottleneck). Next, I started an instance of EnsLib.Kafka.Service. In the OnProcessInput method, I receive a message, extract the key from it, get the current time, and write the row to the table.

This article describes a significant enhancement of how InterSystems IRIS deals with table statistics, a crucial element for IRIS SQL processing, in the 2025.2 release. We'll start with a brief refresher on what table statistics are, how they are used, and why we needed this enhancement. Then, we'll dive into the details of the new infrastructure for collecting and saving table statistics, after which we'll zoom in onto what the change means in practice for your applications. We'll end with a few additional notes on patterns enabled by the new model, and look forward to the follow-on phases of this initial delivery.

This article outlines the process of utilizing the renowned Jaeger solution for tracing InterSystems IRIS applications. Jaeger is an open-source product for tracking and identifying issues, especially in distributed and microservices environments. This tracing backend that emerged at Uber in 2015 was inspired by Google's Dapper and Twitter's OpenZipkin. It later joined the Cloud Native Computing Foundation (CNCF) as an incubating project in 2017, achieving graduated status in 2019. This guide will demonstrate how to operate the containerized Jaeger solution integrated with IRIS.

Optimizing Performance of Apache Web Server and Web Gateway

By Patrick Jamieson, M.D., Product Technical Manager, InterSystems IRIS for Health

When working with InterSystems IRIS or IRIS for Health, an external web server like Apache2 or NGINX is essential for managing HTTP workloads, especially for FHIR servers Starting with version 2023.3, the private Apache server was removed from the installation kit (except for Community Editions and Health Connect).

InterSystems IRIS 2024 recently introduced the vector types. This addition empowers developers to work with vector search, enabling efficient similarity searches, clustering, and a range of other applications. In this article, we will delve into the intricacies of vector types, explore their applications, and provide practical examples to guide your implementation.

At its essence, a vector type is a structured collection of numerical values arranged in a predefined order. These values serve to represent different attributes, features, or characteristics of an object.

Hi,

If I test the Native api for Node.js from the documentation, I noticed (if I'm correct) all methods and calls are synchronous. By default due to the nature of Node.js, there is only one thread of execution and normally  all JavaScript methods and all calls should be asynchronous and use either a callback function (the "old way") or promises or the async/await contruct to return their result, e.g.:

• myFunction(params, callbackFunction(response))
• myFunction(params).

In IRIS, every time a request need to be processed, a specific IRIS process (IRISDB.EXE) need to be assigned to handle that request.

If there is no spare IRIS process at that time, process will need to be created (and later destroyed). On some Windows systems (especially with security/antimalware solutions being active) creating new processes can be slow, which can results in delays during peak times (due to inrush of requests).

I have a database that is 2TB in size (from a LIVE system).

I would like to defragment it but I have some questions. I took a look at official documentation but it does not help.

• Is is possible to get a measurement about how much database is fragmented ? (eg: a percentage). Most operating systems are able to give a an indication about how much a filesystem is fragmented before any operation.
• The database is on a LIVE server, the IRIS service is restarted every night (around 1AM). Because of that I can't run defragment continuously. I don't think it will be able to complete under 24 hours.