#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.

gj :: configExplorer is a new VS Code extension integrating with Server Manager and leveraging Structurizr to produce configuration diagrams of your servers.

Here's a short introductory video.

https://www.youtube.com/embed/WHkoZsg6P-A
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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.

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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.

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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.

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Introduction

The InterSystems IRIS Data Platform has long been known for its performance, interoperability, and flexibility across programming languages. For years, developers could use IRIS with Python, Java, JavaScript, and .NET — but Go (or Golang) developers were left waiting.

Golang Logo

That wait is finally over.

The new go-irisnative driver brings GoLang support to InterSystems IRIS, implementing the standard database/sql API. This means Go developers can now use familiar database tooling, connection pooling, and query interfaces to build applications powered by IRIS.

Why GoLang Support Matters

GoLang is a language designed for simplicity, concurrency, and performance — ideal for cloud-native and microservices-based architectures. It powers some of the world’s most scalable systems, including Kubernetes, Docker, and Terraform.

Bringing IRIS into the Go ecosystem enables:

  • Lightweight, high-performance services using IRIS as the backend.
  • Native concurrency for parallel query execution or background processing.
  • Seamless integration with containerized and distributed systems.
  • Idiomatic database access through Go’s database/sql interface.

This integration makes IRIS a perfect fit for modern, cloud-ready Go applications.

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This anthropic article made me think of several InterSystems presentations and articles on the topic of data quality for AI applications. InterSystems is right that data quality is crucial for AI, but I imagined there would be room for small errors, but this study suggests otherwise. That small errors can lead to big hallucinations. What do you think of this? And how can InterSystems technology help?

https://www.anthropic.com/research/small-samples-poison

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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.

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