Hi Community,

In this article, we will explore the concepts of Dynamic SQL and Embedded SQL within the context of InterSystems IRIS, provide practical examples, and examine their differences to help you understand how to leverage them in your applications.

InterSystems SQL provides a full set of standard relational features, including the ability to define table schema, execute queries, and define and execute stored procedures. You can execute InterSystems SQL interactively from the Management Portal or programmatically using a SQL shell interface. Embedded SQL enables you to embed SQL statements in your ObjectScript code, while Dynamic SQL enables you to execute dynamic SQL statements from ObjectScript at runtime. While static SQL queries offer predictable performance, dynamic and embedded SQL offer flexibility and integration, respectively.

Window functions in InterSystems IRIS let you perform powerful analytics — like running totals, rankings, and moving averages — directly in SQL.
They operate over a "window" of rows related to the current row, without collapsing results like GROUP BY.
This means you can write cleaner, faster, and more maintainable queries — no loops, no joins, no temp tables.

In this article let's understand the mechanics of window functions by addressing some common data analisys tasks.

So, you checked your server and saw that IRISTEMP is growing too much. There's no need to panic. Let’s investigate the issue before your storage runs out.

Step 1: Confirm the IRISTEMP Growth Issue

Before assuming IRISTEMP is the problem, let’s check its actual size.

Check the Free Space

Run the following command in the IRIS terminal:

%SYS>do ^%FREECNT

When prompted, enter:

Hello all,

I'm trying to build a cube based on a linked table but seems that IRIS is not able to do it :O

Long story short, I have a linked table in IRIS that sources a Microsoft SQL table (using standard linked feature from the portal). It works fine, I can access it using SQL as many other times. On top of that, I've created in DeepSee (ok, Analytics) a cube that uses this class as source. It compiles correctly, no errors given. When I build it with 100 records, all goes well and using Analyzer I can see results.

Hi community.

I have a query:

Hello community,

I wanted to share my experience about working on Large Data projects. Over the years, I have had the opportunity to handle massive patient data, payor data and transactional logs while working in an hospital industry. I have had the chance to build huge reports which had to be written using advanced logics fetching data across multiple tables whose indexing was not helping me write efficient code.

Here is what I have learned about managing large data efficiently.

Choosing the right data access method.

As we all here in the community are aware of, IRIS provides multiple ways to access data. Choosing the right method, depends on the requirement.

• Direct Global Access: Fastest for bulk read/write operations. For example, if i have to traverse through indexes and fetch patient data, I can loop through the globals to process millions of records. This will save a lot of time.

Set ToDate=+H
Set FromDate=+$H-1 For  Set FromDate=$O(^PatientD("Date",FromDate)) Quit:FromDate>ToDate  Do
. Set PatId="" For  Set PatId=$Order(^PatientD("Date",FromDate,PatID)) Quit:PatId=""  Do
. . Write $Get(^PatientD("Date",FromDate,PatID)),!

• Using SQL: Useful for reporting or analytical requirements, though slower for huge data sets.

Hi Team,

Can I please check if anyone has built a simple web interface for maintaining custom SQL lookup class.

We have a simple persistent class in HealthShare which is used for storing Pathology test codes. Test codes in this lookup class is used for message filtering and applying additional logic when processing pathology results/orders.

Is there a way to create a table for the results of a SQL statement ?

Example: Consider the below query and results.

Select ID,Name,DOB,Gender from EMS.EMS

RESULT

Introduction

In a previous article, I presented the IRIStool module, which seamlessly integrates the pandas Python library with the IRIS database. Now, I'm explaining how we can use IRIStool to leverage InterSystems IRIS as a foundation for intelligent, semantic search over healthcare data in FHIR format.

This article covers what I did to create the database for another of my projects, the FHIR Data Explorer. Both projects are candidates in the current InterSystems contest, so please vote for them if you find them useful.

You can find them at the Open Exchange:

• IRIStool and Data Manager
• FHIR Data Explorer

In this article we'll cover:

• Connecting to InterSystems IRIS database through Python
• Creating a FHIR-ready database schema
• Importing FHIR data with vector embeddings for semantic search

When we need to integrate Caché/IRIS with other relational databases, one common question arises: “How do I set up the JDBC connection?”.
The official documentation doesn’t always provide a straightforward step-by-step guide, which can be frustrating, especially for beginners.

I joined InterSystems less than a year ago. Diving into ObjectScript and IRIS was exciting, but also full of small surprises that tripped me up at the beginning. In this article I collect the most common mistakes I, and many new colleagues, make, explain why they happen, and show concrete examples and practical fixes. My goal is to help other new developers save time and avoid the same bumps in the road.

InterSystems FAQ rubric

One way to optimize query performance is to use query parallelism on a per-query or system-wide basis (a standard feature).

This is a technique for dividing the execution of a particular query among processors on a multi-processor system. The query optimizer will execute parallel processing only if there is a possibility of benefiting from parallel processing. Parallel processing is only applicable to SELECT statements.

In the previous article, we saw how to build a customer service AI agent with smolagents and InterSystems IRIS, combining SQL, RAG with vector search, and interoperability.

In that case, we used cloud models (OpenAI) for the LLM and embeddings.

This time, we’ll take it one step further: running the same agent, but with local models thanks to Ollama.

We currently have Business Operation that we built to use the EnsLib.SQL.OutboundAdapter so we can make Microsoft SQL Server Stored Procedure calls. The BO is attached to a Java Gateway Service.

Some of our MS SQL Databases have moved from being OnPrem to Azure Cloud. We have started seeing where we are receiving errors on the BO saying that we cannot connect to the Azure Database, but we never receive a Disconnect from the Azure Database.