Hi Community,

It's voting time! Cast your votes for the best applications in our InterSystems .Net, Java, Python, and JavaScript Contest:

🔥 VOTE FOR THE BEST APPS 🔥

How to vote? Details below.

This anthropic article made me think of several InterSystems presentations and articles on the topic of data quality for AI applications. InterSystems is right that data quality is crucial for AI, but I imagined there would be room for small errors, but this study suggests otherwise. That small errors can lead to big hallucinations. What do you think of this? And how can InterSystems technology help?

https://www.anthropic.com/research/small-samples-poison

The ObjectScript language has incredible JSON support through classes like %DynamicObject and %JSON.Adaptor. This support is due to the JSON format's immense popularity over the previous dominance of XML. JSON brought less verbosity to data representation and increased readability for humans who needed to interpret JSON content. To further reduce verbosity and increase readability, the YAML format was created. The very easy-to-read YAML format quickly became the most popular format for representing configurations and parameterizations, due to its readability and minimal verbosity.

I want to build an event store using raw globals and replay events for rebuilding state. Can I hook into IRIS journaling or write my own event log and replay engine?

We want to use IRIS as a backend for a lightweight blockchain or DLT. Can we model blocks using deterministic globals and implement consensus (PBFT, Raft) externally while using IRIS as the ledger?

I need to ingest streaming data over WebSockets. How can I create a custom inbound adapter for IRIS productions that supports many concurrent socket connections?

We require user-specific row access (row-level security). How can we enforce this in SQL and ObjectScript using custom class parameters and dynamic WHERE clause injections?

We’re ingesting high-volume HL7 messages and converting them to FHIR in near-real-time. How do we design a streaming ETL pipeline using interoperability (productions) that scales horizontally?

For space optimization, we want to apply a domain-specific compression algorithm to binary stream data before writing to %Stream.GlobalBinary. Is it possible to override or extend stream classes to include compression/decompression?

We use Apache Flink for complex event processing. Is there a way to integrate IRIS (as a source/sink) with Flink’s streaming API, possibly using the IRIS Native API or JDBC?

What are the best practices for creating a multi-tenant app in IRIS? How can I isolate data per tenant using namespaces, control resource usage, and delegate access via roles securely?

We’re encountering occasional deadlocks when accessing persistent objects. How can I trace lock acquisition and identify cyclic dependencies in real time?

We want to expose both REST and GraphQL endpoints over the same data models. Is there a way to implement or integrate GraphQL with ObjectScript and map to class methods?

I have large joins involving millions of rows. How can I profile and tune the SQL engine’s parallel execution? Are there EXPLAIN plan features to inspect threading and task distribution?

When writing dynamic SQL queries using embedded SQL, how can I force or ensure that filter conditions are pushed down to the data access layer rather than evaluated in memory?

We run mixed workloads in IRIS. For analytical queries, are bitmap indexes effective? What are the caveats for concurrent OLTP updates, and how should I maintain bitmap indexes efficiently?

The built-in task manager is limited. How can I implement a robust, distributed job scheduler in IRIS with support for dependencies, CRON syntax, and failover recovery?

We need to authenticate users via Azure AD or Okta. What are the best practices to implement federated authentication using OAuth2/OIDC or SAML in IRIS Management Portal or custom web apps?

For geographically distributed nodes using async mirroring or ECP, how can I detect and resolve data conflicts manually (custom logic) while maintaining eventual consistency?

I’m using recursive CTEs for hierarchical data, but the planner seems to produce inefficient plans. Can I influence or extend the query optimizer behavior in IRIS?

We are using IRIS with a sharded architecture. Complex SQL queries (with joins, aggregates, and subqueries) are performing slowly. How can I design queries or indexes to optimize distributed execution across shards?

Instead of default storage classes, I want to implement my own SQL storage mapping for a persistent class (e.g., denormalized or sparse matrix structures). How do I define and manage custom storage definitions?

Deploying new IRIS instances can be a time-consuming task, especially when setting up multiple environments with mirrored configurations.

I’ve encountered this issue many times and want to share my experience and recommendations for using Ansible to streamline the IRIS installation process. My approach also includes handling additional tasks typically performed before and after installing IRIS.

This guide assumes you have a basic understanding of how Ansible works, so I won’t go into much detail on its fundamentals.

To manage the accumulation of production data, InterSystems IRIS enables users to manage the database size by periodically purging the data. This purge can apply to messages, logs, business processes, and managed alerts.

Please check the documentation for more details on the settings of the purge task:
https://docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls?KEY=EGMG_purge#EGMG_purge_settings

An issue that many users ran into is still finding old messages after running the purge task for messages. For example, a user has a purge task for messages that has NumberOfDaysToKeep=45.

Hi Community,

It seems our Developer Community AI has decided to take a coffee break ☕️ (probably after answering one too many tricky ObjectScript questions).

For now, it’s gone mysteriously silent and refuses to generate answers. We suspect it might be rethinking its life choices after reading one too many deeply philosophical ObjectScript questions.

We’ll let you know as soon as our digital colleague is back online, refreshed, and ready to assist you again. In the meantime, if you notice it suddenly waking up and replying again, please do let us know in the comments before it changes its mind.

Hi,

It's me again😁, recently I am working on generating some fake patient data for testing purpose with the help of Chat-GPT by using Python. And, at the same time I would like to share my learning curve.😑

1st of all for building a custom REST api service is easy by extending the %CSP.REST

Creating a REST Service Manually

Let's Start !😂

1. Create a class datagen.restservice which extends  %CSP.REST 

Class datagen.restservice Extends %CSP.REST
{
Parameter CONTENTTYPE = "application/json";
}

2.

I know the next ones:

1. Place all different settings in environment variables. You have a different .env file for each environment, and you must add some code to Production for reading and setting these values. It's good for deploying into containers, but challenging for management when we have a large production. I mean, we have many settings that can vary depending on the environment: active flag, pool size, timeouts, and so on. Not only endpoints.

2. My own case.

This one's for those of you who feel that InterSystems tech makes your life easier... whether you’re working on integration projects, building innovative solutions, or evolving existing ones.

Introduction

In my previous article, I introduced the FHIR Data Explorer, a proof-of-concept application that connects InterSystems IRIS, Python, and Ollama to enable semantic search and visualization over healthcare data in FHIR format, a project currently participating in the InterSystems External Language Contest.

In this follow-up, we’ll see how I integrated Ollama for generating patient history summaries directly from structured FHIR data stored in IRIS, using lightweight local language models (LLMs) such as Llama 3.2:1B or Gemma 2:2B.

The goal was to build a completely local AI pipeline that can extract, format, and narrate patient histories while keeping data private and under full control.

All patient data used in this demo comes from FHIR bundles, which were parsed and loaded into IRIS via the IRIStool module. This approach makes it straightforward to query, transform, and vectorize healthcare data using familiar pandas operations in Python. If you’re curious about how I built this integration, check out my previous article Building a FHIR Vector Repository with InterSystems IRIS and Python through the IRIStool module.

Both IRIStool and FHIR Data Explorer are available on the InterSystems Open Exchange — and part of my contest submissions. If you find them useful, please consider voting for them!

• IRIStool and Data Manager
• FHIR Data Explorer