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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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Some languages ​​have the concept of a garbage collector for automatic memory management. I'd like to know if something like this exists in InterSystems Caché in the context of routines, %CSP.REST, or %CSP.Page.

The question arises from the %Close() method of the %RegisteredObject class. This indicates that I need to manually clear the object from memory if it's no longer in use, or in the web context, if the request has completed. Is this correct? Or am I completely wrong?

Thank you!

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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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Hello and welcome to the September 2025 Developer Community YouTube Recap.
InterSystems Ready 2025
By Dean Andrews, David Reche
By Aldo Faisal
By Kari Hedges
By Scott Gnau, Peter Lesperance, Tom Woodfin, Gokhan Uluderya, Jeff Fried, Daniel Franco
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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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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.

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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

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