InterSystems Native SDK for Python is a lightweight interface to InterSystems IRIS APIs that were once available only through ObjectScript.

I'm especially interested in the ability to call ObjectScript methods, class methods, to be precise. It works, and it works great, but by default, calls only support scalar arguments: strings, booleans, integers, and floats.

But if you want to:
- Pass or return structures, such as dicts or lists
- Pass or return streams

You'll need to write some glue code or take this project (installs with pip install edpy). edpy package gives you one simple signature:

call(iris, class_name, method_name, args)

which allows you to call any ObjectScript method and get results back.

An interesting pattern around unique indices came up recently (in internal discussion re: isc.rest) and I'd like to highlight it for the community.

As a motivating use case: suppose you have a class representing a tree, where each node also has a name, and we want nodes to be unique by name and parent node. We want each root node to have a unique name too. A natural implementation would be:

@Ming Zhou asked a great question in https://community.intersystems.com/post/how-get-all-properties-defined-c... and the answer sums up exactly why ObjectScript is my favorite.

Adding VSCode into your IRIS container

One of the easiest ways to setup repeatable development environments is to spin up containers for them. I find that when iterating quickly, it was very convenient to host a vscode instance within my development container. Thus, I have created a quick container script to add a browser-based vscode into an IRIS container. This should work for most 2021.1+ containers. My code repository can be found here

My major interest is  Working with Globals in Embedded Python.
So I checked the available official documentation.

#1 Introduction to Globals
an attempt of a generic description of what a global is. Pointing to

Hello, everyone!

InterSystems IRIS has a menu called Interoperability.

It provides mechanisms to easily create system integrations (adapters, record maps, BPMs, data conversions, etc.) so different systems can be easily connected.

While reviewing our documentation for our ^pButtons (in IRIS renamed as ^SystemPerformance) performance monitoring utility, a customer told me: "I understand all of this, but I wish it could be simpler… easier to define profiles, manage them etc.".

After this session I thought it would be a nice exercise to try and provide some easier human interface for this.

The first step in this was to wrap a class-based API to the existing pButtons routine.

I was also able to add some more "features" like showing what profiles are currently running, their time remaining to run, previously running processes and more.

The next step was to add on top of this API, a REST API class.

With this artifact (a pButtons REST API) in hand, one can go ahead and build a modern UI on top of that.

For example -

You may have seen my series of articles on GlobalToJSON closing with Embedded Python.
And to me, Embedded means inside a Class and not PEX or other external variants.
The first variants showed that ePy is not an ultimate requirement. ISOS/COS may do it as well.
Here I try to position ePy inside IRIS.

It is very interesting to compare different BI technologies. It is curious to me what the differences are in functionality, development tools, speed and usability.

M:N Relationship is a recurring object of my interest for a long time.
So the subject turns up with me repeatedly.
For this article, I found a nice example with TWO  separate M:N relationships. Some M:N².

It is not pure fiction but taken from a closer view of OEX Contests that we run on a regular base.

I think each of us has on the computer directories with photo files of relatives and friends, music albums of favorite bands and singers, as well as e-book libraries. I also have them, and I came up with the idea to implement a convenient interface for these files. You can read more about the project in the article.
And I proposed this project for the contest, maybe it will be useful to you.

Diabetes can be discovered from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the National Institute of Diabetes and Digestive and Kidney Diseases published a very useful dataset for training ML algorithms in the detection/prediction of diabetes. This publication can be found on the largest and best known data repository for ML, Kaggle at https://www.kaggle.com/datasets/mathchi/diabetes-data-set.

IRIS Interoperability formerly known as Ensemble comes with many built in adapters. It does not have a service or adapter to receive mail. I have written email service to receive mail messages via SMTP which can be passed to email operation.