Best Practices recommendations on how to develop, test, deploy and manage solutions on InterSystems Data Platforms better.
Hi Community!
We are introducing a new feature on DC site - Best Practices of InterSystems Data Platforms.
It is the tag, which highlights articles on DC considered as Best Practices on how to develop, test, deploy and manage solutions on InterSystems Data Platforms.
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IRIS makes SIEM systems integration simple with Structured Logging and Pipes!
IRIS can use a KMS (Key Managment Service) as of release 2023.3. Intersystems documentation is a good resource on KMS implementation but does not go into details of the KMS set up on the system, nor provide an easily followable example of how one might set this up for basic testing.
The purpose of this article is to supplement the docs with a brief explanation of KMS, an example of its use in IRIS, and notes for setup of a testing system on AWS EC2 RedHat Linux system using the AWS KMS. It is assumed in this document that the reader/implementor already has access/knowledge to set up an AWS EC2 Linux system running IRIS (2023.3 or later), and that they have proper authority to access the AWS KMS and AWS IAM (for creating roles and polices), or that they will be able to get this access either on their own or via their organizations Security contact in charge of their AWS access.
Who hasn't been developing a beautiful example using a Docker IRIS image and had the image generation process fail in the Dockerfile because the license under which the image was created doesn't contain certain privileges?
In my case, what I was deploying in Docker is a small application that uses the Vector data type. With the Community version, this isn't a problem because it already includes Vector Search and vector storage. However, when I changed the IRIS image to a conventional IRIS (the latest-cd), I found that when I built the image, including the classes it had generated, it returned this error:
When using InterSystems IRIS as an interoperability engine, we all know and love how easy it is to use the Message Viewer to review message traces and see exactly what's going on in your production. When a system is handling millions of messages per day, you may not know exactly where to begin your investigation though.
Over my years supporting IRIS productions, I often find myself investigating things like...
Here at InterSystems, we often deal with massive datasets of structured data. It’s not uncommon to see customers with tables spanning >100 fields and >1 billion rows, each table totaling hundred of GB of data. Now imagine joining two or three of these tables together, with a schema that wasn’t optimized for this specific use case. Just for fun, let’s say you have 10 years worth of EMR data from 20 different hospitals across your state, and you’ve been tasked with finding…. every clinician within your network who has administered a specific drug between the years of 2017-2019
Sometimes customers need a small IRIS instance to do something in the cloud and shut it down, or they need hundreds of containers (i.e. one per end user or one per interface) with small workloads. This exercise came about to see how small an IRIS instance could be. For this exercise we focused on what is the smallest amount of memory we can configure for an IRIS instance.Do you know all the parameters that affect the memory allocated by IRIS ?
These are the different buckets that affect memory allocation by IRIS and its corresponding parameters:
Monitoring your IRIS deployment is crucial. With the deprecation of System Alert and Monitoring (SAM), a modern, scalable solution is necessary for real-time insights, early issue detection, and operational efficiency. This guide covers setting up Prometheus and Grafana in Kubernetes to monitor InterSystems IRIS effectively.
This guide assumes you already have an IRIS cluster deployed using the InterSystems Kubernetes Operator (IKO), which simplifies deployment, integration and mangement.
Introduction
In today's rapidly evolving threat landscape, organizations deploying mission-critical applications must implement robust security architectures that protect sensitive data while maintaining high availability and performance. This is especially crucial for enterprises utilizing advanced database management systems like InterSystems IRIS, which often powers applications handling highly sensitive healthcare, financial, or personal data.
A unit of ObjectScript code (a ClassMethod, say) may produce a variety of unexpected side effects by interacting with parts of the system outside of its own scope and not properly cleaning up. As a non-exhaustive list, these include:
When using standard SQL or the object layer in InterSystems IRIS, metadata consistency is usually maintained through built-in validation and type enforcement. However, legacy systems that bypass these layers—directly accessing globals—can introduce subtle and serious inconsistencies.
Using embedded Python while building your InterSystems-based solution can add very powerful and deep capabilities to your toolbox.
I'd like to share one sample use-case I encountered - enabling a CDC (Change Data Capture) for a mongoDB Collection - capturing those changes, digesting them through an Interoperability flow, and eventually updating an EMR via a REST API.
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Following 2 local Webinars we had focused on VS Code ["Intro" and "Beyond Basics"; in Hebrew], I prepared for the participants some related links of relevant resources which we sent as a follow-up. Sharing them here as well for the benefit of the Community.
You are all of course welcome to add more useful resources.
In InterSystems IRIS 2024.3 and subsequent IRIS versions, the AutoML component is now delivered as a separate Python package that is installed after installation. Unfortunately, some recent versions of Python packages that AutoML relies on have introduced incompatibilities, and can cause failures when training models (TRAIN MODEL statement). If you see an error mentioning "TypeError" and the keyword argument "fit_params" or "sklearn_tags", read on for a quick fix.
This part of the OMOP Journey, we reflect before attempting to challenge Scylla on how fortunate we are that InterSystems OMOP transform is built on the Bulk FHIR Export as the source payload. This opens up hands off interoperability with the InterSystems OMOP transform across several FHIR® vendors, this time with the Google Cloud Healthcare API.
This part of the OMOP Journey, we reflect before attempting to challenge Scylla on how fortunate we are that InterSystems OMOP transform is built on the Bulk FHIR Export as the source payload. This opens up hands off interoperability with the InterSystems OMOP transform across several FHIR® vendors, including Amazon Web Services HealthLake.
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In recent versions of IRIS, a powerful new data loading command has been introduced to SQL: LOAD DATA. This feature has been highly optimized to import data into IRIS extremely fast, allowing hundreds of gigabytes of data to be inserted in seconds instead of hours or days.
This is a very exciting improvement. However, a big problem in the data loading experience still exists. Namely, the time and hassle it takes to:
If you want to find out what exact version your Docker image is (and since the latest image tagging scheme you cannot just rely on the image tag; and assuming you don't want to actually run it just in order to find out) you can run this docker command:
Thirteen years ago, I attained dual undergraduate degrees in electrical engineering and math, then promptly started full-time at InterSystems using neither. One of my most memorable and stomach-churning academic experiences was in Stats II. On an exam, I was solving a moderately difficult confidence interval problem. I was running out of time, so (being an engineer) I wrote out the definite integral on the exam paper, punched it into my graphing calculator, wrote an arrow with “calculator” over it, then wrote the result. My professor, affectionately known as “Dean, Dean, the Failing Machine,”
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:
Some of these kinds of properties are quite straightforward. For example, atomic properties:
Property Name As %Name;Property DoB As %DateProperty Age As %IntegerHere's a sample code to display JSON or dynamic object.
It shows how to iterate over object, get property values and their paths.
With the release of InterSystems IRIS Cloud SQL, we're getting more frequent questions about how to establish secure connections over JDBC and other driver technologies. While we have nice summary and detailed documentation on the driver technologies themselves, our documentation does not go as far to describe individual client tools, such as our personal favourite DBeaver. In this article, we'll describe the steps to create a secure connection from DBeaver to your Cloud SQL deployment.
Hi community,
The aim of this article is to explain how to create messaging between IRIS and Microsoft Teams.
In my company, we wanted to monitor error messages, and we used the Ens.Alert class to redirect those error messages through a Business Operation that sent an email.
The problem was that we sent those error messages to a support account where there were many emails. We wanted something specific for a specific team.
So we investigated how to make these messages reach the development team directly and they could have, in real time, a notification of an error in our production.
In our company we use Microsoft Teams as a corporate tool, so we asked ourselves: How could we make these messages reach the IRIS development team?
If you are a customer of the new InterSystems IRIS® Cloud SQL and InterSystems IRIS® Cloud IntegratedML® cloud offerings and want access to the metrics of your deployments and send them to your own Observability platform, here is a quick and dirty way to get it done by sending the metrics to Google Cloud Platform Monitoring (formerly StackDriver).