I need to ingest streaming data over WebSockets. How can I create a custom inbound adapter for IRIS productions that supports many concurrent socket connections?

I want to build an event store using raw globals and replay events for rebuilding state. Can I hook into IRIS journaling or write my own event log and replay engine?

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

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.

The built-in %JSONExport() fails with circular references in deeply nested objects. How can I write a custom serializer that supports circular detection and reference tracking?

Introduction

In modern applications, especially those involving large data, documents, logs, or multimedia, handling large or unstructured content efficiently becomes essential. InterSystems IRIS offers a robust and scalable way to manage such data using stream objects.

We are happy to present the bonuses page for the applications submitted to the InterSystems .Net, Java, Python, and JavaScript Contest!

See the results below.

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

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.

Can the IRIS SQL engine be extended with custom optimization rules (e.g., prioritizing certain indices or join orders)? If not, is there a supported way to influence cost models?

I’m using recursive CTEs for hierarchical data, but the planner seems to produce inefficient plans. Can I influence or extend the query optimizer behavior in IRIS?

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.

Hi Community,

It seems our Developer Community AI has decided to take a coffee break ☕️ (probably after answering one too many tricky ObjectScript questions).

For now, it’s gone mysteriously silent and refuses to generate answers. We suspect it might be rethinking its life choices after reading one too many deeply philosophical ObjectScript questions.

I know the next ones:

1. Place all different settings in environment variables. You have a different .env file for each environment, and you must add some code to Production for reading and setting these values. It's good for deploying into containers, but challenging for management when we have a large production. I mean, we have many settings that can vary depending on the environment: active flag, pool size, timeouts, and so on. Not only endpoints.