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.

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

Hi Community,

Are you ready to transform your healthcare organization with the power of Artificial Intelligence? Join us for an enlightening webinar that will equip you with the knowledge and tools to harness AI effectively in your practice.

🌐 Are you and your data AI ready? 🌐

📅 Tuesday, 15th October, 1 pm ICT | 2 pm SGT| 4 pm AEST // 8 am CEST | 2 am EDT

🗣 Presenter: @Kathleen Aller, Global Head of Healthcare Market Strategy, InterSystems

Hi Community,

In this article, I will introduce my application iris-AgenticAI .

The rise of agentic AI marks a transformative leap in how artificial intelligence interacts with the world—moving beyond static responses to dynamic, goal-driven problem-solving. Powered by OpenAI’s Agentic SDK , The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, Swarm.
This application showcases the next generation of autonomous AI systems capable of reasoning, collaborating, and executing complex tasks with human-like adaptability.

Application Features

• Agent Loop 🔄 A built-in loop that autonomously manages tool execution, sends results back to the LLM, and iterates until task completion.
• Python-First 🐍 Leverage native Python syntax (decorators, generators, etc.) to orchestrate and chain agents without external DSLs.
• Handoffs 🤝 Seamlessly coordinate multi-agent workflows by delegating tasks between specialized agents.
• Function Tools ⚒️ Decorate any Python function with @tool to instantly integrate it into the agent’s toolkit.
• Vector Search (RAG) 🧠 Native integration of vector store (IRIS) for RAG retrieval.
• Tracing 🔍 Built-in tracing to visualize, debug, and monitor agent workflows in real time (think LangSmith alternatives).
• MCP Servers 🌐 Support for Model Context Protocol (MCP) via stdio and HTTP, enabling cross-process agent communication.
• Chainlit UI 🖥️ Integrated Chainlit framework for building interactive chat interfaces with minimal code.
• Stateful Memory 🧠 Preserve chat history, context, and agent state across sessions for continuity and long-running tasks.

Hey Community!

We're happy to share the next video in the series dedicated to Gen AI on our InterSystems Developers YouTube:

⏯ Energy Consumption for AI

https://www.youtube.com/embed/o99hGFoCeuU
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Hello community,

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.

Hey Community!

Check out the new video dedicated to Gen AI on our InterSystems Developers YouTube:

⏯ Healthcare Use-Case: Patient Portal Messages

https://www.youtube.com/embed/z6lNuZSZ9mg
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Hi Community,

Play the new video on InterSystems Developers YouTube:

⏯ Using LLMs with InterSystems IRIS Interoperability Productions @ Global Summit 2024

https://www.youtube.com/embed/IQ9r0OWYSZ8
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I'm exploring this right now: given a bunch of types defined as Pydantic models, how can I come up with an equivalent %RegisteredObject/%SerialObject and convert to/from (e.g., to support persistence and match validation as much as possible)?

People who know Python better than I do (e.g., your average undergraduate from this decade): is this a stupid idea or a cool idea? Has anyone else done this before?

Hello everyone! This is my first post into the developer community and one that I hope is fairly simple to answer. In our environment we currently have 3 different Test environments for testing before migrating code to our Prod environment. Currently we are working on establishing a source control method using Git in house which has been a bit of a struggle. We have also had developers that had used different test and migration methods in the past which has caused some issues with keeping the different test environment in sync.

I assume IPM manifest tag UnitTest is tightly coupled with the standard unit test framework, right? However we have our own one that predates the standard one and we are not going to switch.

I think I need to import the unit test cases and then be able to run a single class method for the test run. Any ideas how this would be possible?

hi everyone,

I'm trying to make some use of AI to help with coding. To achieve this I've moved to VS Code linking to a cache instance, and installed the "Cody" extension from Sourcegraph that talks to Claude Sonnet.

Audience

Those curious in exploring new GenerativeAI usecases.

Shares thoughts and rationale when training generative AI for pattern matching.