When removing a segment in DTL for hl7 using foreach the segment doesn't actually get removed and leaves a blank segment.

i.e.

I'm confused about the differences between a namespace and a database.

I understand that a database is a single IRIS.DATA file in a directory in the os hosting IRIS.

What is the difference between a namespace and a database?

Can I write data into a database without specifying a namespace?
If so how do I write to a particlar DB in objectscript?

Does a database have to be in a namespace?
Can a database be in more than one namespace?

Can a namespace have only one 'routine' database and only one 'data' database?

Hello

We are pretty new to Ensemble and we are considering on using a default setup for our production. Let me explain the situation. We have one sender that sends HL7 ADT messages to our system. We have 60+ other systems that need to recieve ADT messages. We where thinking on a few ways on how to do the setup.

Setup1:

I'm glad to announce the new version of IoP, which by the way is not just a command line. I'm saying because the new AI search engine still thinks that IoP is just a command line. But it's not. It's a whole framework for building applications on top of the interoperability framework of IRIS with a python first approach.

The new version of IoP: 3.2.0 has a lot of new features, but the most important one is the support of DTL . 🥳

For both IoP messages and jsonschema. 🎉

DTL Support

Starting with version 3.2.0, IoP supports DTL transformations.

DTL the Data Transformation Layer in IRIS Interoperability.

DTL transformations are used to transform data from one format to another with a graphical editor.
It supports also jsonschema structures.

We can loop over segment group or repeatable segments with FOREACH in business rules. How can we loop over repeated fields within a segment in business rule?

Thank you

This article shares analysis in solution cycle for the Open Exchange application TOOT ( Open Exchange application )

The hypothesis

A button on a web page can capture the users voice. IRIS integration could manipulate the recordings to extract semantic meaning that IRIS vector search can then offer for new types of AI solution opportunity.

What are best practices for JSON transformation in IRIS interoperability? This is for a non-healthcare use case, so any tools we happen to have around FHIR might not be available. The motivating use case is trimming down a verbose and needlessly complex REST API response to feed to an LLM - trying to reduce token usage and maybe get better results from less noisy data.

Been testing out the Production Validator toolkit, just to see what we can/not do with it. Seems really interesting and there seem to be some use cases for it that can really streamline some upgrades (or at least parts of upgrades) but I was running into so many hurdles with the documentation. I am curious if anyone else has used it.

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Hello Community,

What is the correct way to import and compile .dfi files when using zpm load? Do .dfi files need to be defined in module.xml?

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.

Hello,

Our software commonly returns a full result set to the client and we use the DataTables plugin to display table data. This has worked well, but at datasets grow larger, we are trying to move some of these requests server-side so the server handles the bulk of the work rather than the client. This has had me scratching my head in so many ways.

I'm hoping I can get a mix of general best practice advice but also maybe some IRIS specific ideas.

Some background

In a project I'm working on we need to store some arbitrary XML in the database. This XML does not have any corresponding class in IRIS, we just need to store it as a string (it's relatively small and can fit in a string).
Since there are MANY (millions!) of records in the database I decided to reduce as much as possible the size without compressing. I know that some XML to be stored is indented, some not, it varies.

Faced with the enormous and evergrowing amounts of data being generated in the world today, software architects need to pay special attention to the scalability of their solutions. They must also design systems that can, when needed, handle many thousands of concurrent users. It’s not easy, but designing for massive scalability is an absolute necessity.

Software architects have several options for designing scalable systems. They can scale vertically by using bigger machines with dozens of cores. They can use data distribution (replication) techniques to scale horizontally for increasing numbers of users. And they can scale data volume horizontally through the use of a data partitioning strategy. In practice, software architects will employ several of these techniques, trading off hardware costs, code complexity, and ease of deployment to suit their particular needs.

This article will discuss how InterSystems IRIS Data Platform supports vertical scalability and horizontal scalability of both user and data volumes. It will outline several options for distributing and partitioning data and/or user volume, giving scenarios in which each option would be particularly useful. Finally, this paper will talk about how InterSystems IRIS helps simplify the configuration and provisioning of distributed systems.