Introduction

The InterSystems IRIS Data Platform has long been known for its performance, interoperability, and flexibility across programming languages. For years, developers could use IRIS with Python, Java, JavaScript, and .NET — but Go (or Golang) developers were left waiting.

That wait is finally over.

The new go-irisnative driver brings GoLang support to InterSystems IRIS, implementing the standard database/sql API. This means Go developers can now use familiar database tooling, connection pooling, and query interfaces to build applications powered by IRIS.

Why GoLang Support Matters

GoLang is a language designed for simplicity, concurrency, and performance — ideal for cloud-native and microservices-based architectures. It powers some of the world’s most scalable systems, including Kubernetes, Docker, and Terraform.

Bringing IRIS into the Go ecosystem enables:

• Lightweight, high-performance services using IRIS as the backend.
• Native concurrency for parallel query execution or background processing.
• Seamless integration with containerized and distributed systems.
• Idiomatic database access through Go’s database/sql interface.

This integration makes IRIS a perfect fit for modern, cloud-ready Go applications.

Overview

The typeorm-iris project provides experimental support for integrating TypeORM with InterSystems IRIS, enabling developers to interact with IRIS using TypeORM’s well-known decorators and repository abstractions. This allows a more familiar development experience for JavaScript and TypeScript developers building Node.js applications with IRIS as the backend database.

While the project implements key integration points with TypeORM and supports basic entity operations, it’s not yet battle-tested or suitable for production environments.

After so many years of waiting, we finally got an official driver available on Pypi

Additionally, found JDBC driver finally available on Maven already for 3 months, and .Net driver on Nuget more than a month.

As an author of so many implementations of IRIS support for various Python libraries, I wanted to check it. Implementation of DB-API means that it should be replaceable and at least functions defined in the standard. The only difference should be in SQL.

And the beauty of using already existing libraries, that they already implemented other databases by using DB-API standard, and these libraries already expect how driver should work.

I decided to test InterSystems official driver by implementing its support in SQLAlchemy-iris library.

While working on getting JSON support for some Python libraries, I discovered some capabilities IRIS provided.

• JSON_OBJECT - A conversion function that returns data as a JSON object.
• JSON_ARRAY - A conversion function that returns data as a JSON array.
• IS JSON - Determines if a data value is in JSON format.
• JSON_TABLE function returns a table that can be used in a SQL query by mapping JSON.
• JSONPath support - is a query language for querying values in JSON.

We have Webterminal around for quite a while, but it was limited, not all features worked there. There was no shell support or the latest feature as embedded Python support. There are some issues with tools that require programmer mode. Basic Authorization, not as handy as simple login page, where you could have options to add own login page, in case if you would wish to change the way how to login to the application, such as using SSO.

With the original iris terminal, wrapped into a web form, using most used in the web world xterm.js, used in tools like VSCode as well, with some magic from Python, which helped with interprocess tty. We can get the the terminal in the web, in the full capacity.

For a few latest versions, I noticed that Web Applications got some support for JWT Authentication

What is the point of having a dedicated registry if it does not work properly most of the time?

$ docker pull containers.intersystems.com/intersystems/iris-community:latest-em
latest-em: Pulling from intersystems/iris-community
5526e9f3d5c5: Already exists
352b407541ae: Already exists
f6bff04f5338: Already exists
8c3b528467ff: Downloading [=======================>                           ]  125.8MB/262.2MB
e2b23b0bff05: Downloading [=============>                                     ]  124.8MB/462.1MB
failed to copy: httpReadSeeker: failed open: could not fetch content descriptor sha256:8c3b528467ff8cc07d86a4979e3e7b8fd36734205539923c8750e1d523f23367 (application/vnd.docker.image.rootfs.diff.tar.gzip) from remote: not found

I would like to share some of the latest projects I've implemented

Nowadays so much noise around LLM, AI, and so on. Vector databases are kind of a part of it, and already many different realizations for the support in the world outside of IRIS.

Why Vector?

• Similarity Search: Vectors allow for efficient similarity search, such as finding the most similar items or documents in a dataset. Traditional relational databases are designed for exact match searches, which are not suitable for tasks like image or text similarity search.
• Flexibility: Vector representations are versatile and can be derived from various data types, such as text (via embeddings like Word2Vec, BERT), images (via deep learning models), and more.
• Cross-Modal Searches: Vectors enable searching across different data modalities. For instance, given a vector representation of an image, one can search for similar images or related texts in a multimodal database.

And many other reasons.

So, for this pyhon contest, I decided to try to implement this support. And unfortunately I did not manage to finish it in time, below I'll explain why.