Columnar storage is one of the newer offers provided by InterSystems IRIS. Unlike traditional row-based storage, it optimizes query processing by storing data in columns rather than rows, enabling faster access and retrieval of relevant information.

A couple of articles have been written on when it should be used to give a system the biggest boost, how to create tables like that using SQL

CREATE TABLE table (column1 type1, column2 type2, column3 type3) WITH STORAGETYPE = COLUMNAR  -- ex 1
CREATE TABLE table (column1 type1, column2 type2, column3 type3 WITH STORAGETYPE = COLUMNAR)  -- ex 2

and even the performance tests.

As we all know, InterSystems IRIS is a multi-model DBMS and it gives seamless access to the same data using relational and object access. So the former is covered in other articles, but what about the latter?

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What I find really useful about IRIS when teaching my subject of Postrelational databases is the fact that it is a multi model database. Which means that I can actually go into architecture and structure and all that only once but then show the usage of different models (like object, document, hierarchy) using the same language and approach. And it is not a huge leap to go from an object oriented programming language (like C#, Java etc) to an object oriented database.

However, along with advantages (which are many) come some drawbacks when we switch from object oriented model to relational. When I say that you can get access to the same data using different models I need to also explain how it is possible to work with lists and arrays from object model in relational table. With arrays it is very simple - by default they are represented as separate tables and that's the end of it. With lists - it's harder because by default it's a string. But one still wants to do something about it without damaging the structure and making this list unreadable in the object model.

So in this article I will showcase a couple of predicates and a function that are useful when working with lists, and not just as fields.

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Recently, the question came up while discussing the access to the data stored in IRIS from different languages with my students if it was possible to initiate the connection and get data from Cloud solution (InterSystems IRIS CloudSQL) from Microsoft Excel, not the other way around. Considering the many varied ways one can get data in Excel (import data from external sources, connecting to databases using ODBC drivers, using power queries and web queries etc.) the obvious choice was to try ODBC driver. The only task left was to try to connect to the database in the cloud using the ODBC driver.

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We all know that having a set of proper test data before deploying an application to production is crucial for ensuring its reliability and performance. It allows to simulate real-world scenarios and identify potential issues or bugs before they impact end-users. Moreover, testing with representative data sets allows to optimize performance, identify bottlenecks, and fine-tune algorithms or processes as needed. Ultimately, having a comprehensive set of test data helps to deliver a higher quality product, reducing the likelihood of post-production issues and enhancing the overall user experience.

In this article, let's look at how one can use generative AI, namely Gemini by Google, to generate (hopefully) meaningful data for the properties of multiple objects. To do this, I will use the RESTful service to generate data in a JSON format and then use the received data to create objects.

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Hey guys,

I do love Global Masters (and all the rewards that I get from it) and one of my favourite activities is answering quiz questions. And just now I've had a really cute question What is the result of: WRITE "99" + "cents"? which brought a smile to my face (because I do love to add questions like this to the tests for my students). This made me think that I would really like to somehow show my appreciation to the author of this question.

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Article
· Mar 31, 2023 3m read
Using JSON in IRIS

Saw the other day an article with the usage of the %ZEN package when working with JSON and decided to write an article describing a more modern approach. At some recent point, there was a big switch from using %ZEN.Auxiliary.* to dedicated JSON classes. This allowed to work with JSONs more organically.

Thus, at this point there are basically 3 main classes to work with JSON:

  • %Library.DynamicObject - provides a simple and efficient way to encapsulate and work with standard JSON documents. Also, there is a possibility instead of writing the usual code for creating an instance of a class like
set obj = ##class(%Library.DynamicObject).%New()

it is possible to use the following syntax

set obj = {}
  • %Library.DynamicArray - provides a simple yet efficient way to encapsulate and work with standard JSON arrays. With arrays you can use the same approach as with objects, meaning that yu can either create an instance of the class
set array = ##class(%DynamicArray).%New()

or you can do it by using brackets []

set array = []
  • %JSON.Adaptor is a means for mapping ObjectScript objects (registered, serial or persistent) to JSON text or dynamic entities.
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Article
· Mar 16, 2023 6m read
Kinds of properties in IRIS

InterSystems IRIS has quite a few different kinds properties. Let’s put them in order so that they make better sense.

First of all, I would divide them into categories:

  • Atomic or simple properties (all those %String, %Integer, %Data and other system or user datatypes)
  • References to stored objects
  • Built-in objects
  • Streams (both binary and character)
  • Collections (which are divided into arrays and lists)
  • Relationships (one-many and parent-children)

Some of these kinds of properties are quite straightforward. For example, atomic properties:

Property Name As %Name;
Property DoB As %Date
Property Age As %Integer

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Because I had no idea how to build an integration solution for HL7 and didn't know where to start, I decided to follow the course Building Basic HL7 Integrations with InterSystems on Learning portal to get at least the idea of where to begin. After I studied all of it, I decided it might be a good idea to share my thoughts and reflections about it with everyone.

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In the previous article, we talked about the flow of data to request the test and receive the results of the requested test. Now let's talk about one of the most important messages of HL7v2 standard.

Every time a receiving application accepts a message and consumes the message data, it is expected to send an ACKnowledgement (ACK) message back to the sending application. The sending application is expected to keep on sending a message until it has received an ACK message. It is done to inform the sending application that its message was successfully received, that it is (not) valid in accordance with HL7 rules and, if it is compliant, that it will be processed at some point.

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In the previous article, we've seen the structure of one of the most used types of HL7 message - ADT (Admit, Discharge, Transfer) and an example of ADT^A04 with the description of all its fields. Now let's look at another flow of data having to do with ordering and fulfilling the orders of tests. I'm talking about ORM (as of version 2.5 you should use specific messages to order tests, like OMG, OML, OMD, OMS, OMN, OMI, and OMP), ORL and ORU messages. In a very simplified case, the exchange of data may look like this.

Let's look at these messages in more detail.

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In the previous article, we've discussed the origin of the standard HL7v2, the structure and the types of messages. Let's now look at one of the most used types of messages and an example of its structure. I'm talking about ADT.

HL7 ADT messages (Admit, Discharge, Transfer) are used to communicate basic patient information, visit information and patient state at a healthcare facility. ADT messages are one of the most widely-used and high volume HL7 message types, as it provides information for many trigger events including patient admissions, registrations, cancellations, updates, discharges, patient data merges, etc.

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Article
· Nov 29, 2022 6m read
What's HL7v2?!

HL7 (Health Level 7) is a set of technical specifications for computerized exchanges of clinical, financial and administrative data between hospital information systems (HIS). These specifications are variously integrated into the corpus of formal American (ANSI) and international (ISO) standards.

The L7 of HL7 indicates that it is a standard that operates at layer 7, in other words at the application layer, of the OSI model. This means that HL7 does not have to take into account exchange security considerations, or those of message transport (this being ensured by lower-level layers such as SSL/TLS for security or TCP for the transport of data for example). To be more precise, layer 7 supports communications for end-user processes and applications and the presentation of data for user-facing software applications. As the highest layer of the OSI model, and the closest to the end user, layer 7 provides application-specific functions such as identifying communication partners and the quality of service between them, determining resource availability, considering privacy and user authentication, and synchronizing communication, as well as connecting the application to the lower levels of the OSI model.

Returning to the HL7 standard, the HL7 version 2 standard (also known as Pipehat) was originally created in 1989 but is still being used and updated regularly, resulting in versions 2.1, 2.2, 2.3, 2.3.1, 2.4, 2.5, 2.5.1, 2.6, 2.7, 2.7.1, 2.8, 2.8.1, 2.8.2 and 2.9. The v2.x standards are backward compatible (e.g., a message based on version 2.3 will be understood by an application that supports version 2.6) and in higher versions, you will see some fields are left just for it.

Despite it being more than 30 years old, HL7v2 remains the most widely used healthcare interface standard by a large margin according to the HL7.org portal that tells that:

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