Hello,

Our software commonly returns a full result set to the client and we use the DataTables plugin to display table data. This has worked well, but at datasets grow larger, we are trying to move some of these requests server-side so the server handles the bulk of the work rather than the client. This has had me scratching my head in so many ways.

I'm hoping I can get a mix of general best practice advice but also maybe some IRIS specific ideas.

Some background

InterSystems Announces General Availability of InterSystems IRIS, InterSystems IRIS for Health, and HealthShare Health Connect 2025.1

My query that I am running on my Custom SQL Inbound Service has columns that are larger than the typical string length. How do I enlarge the SQL Snapshot Column limitations

Hi,

We’re launching an Early Access Program for an upcoming Table Partitioning feature that will help IRIS customers manage very large tables, and distribute row data and associated indices across databases and storage tiers. Table Partitioning cuts deep into the core of IRIS relational data management, so we want to make sure we get things right through working with a few engaged customers who can provide feedback on the initial deliverables, and fine-tune as needed.

We recently changed the 'UserID" property in a "User" class from type of %String to be %Library.Username. This is for better consistency across our codebase regarding MAXLEN limit.

%Library.Username is a system wrapper datatype which extends %String and has a MAXLEN of 160. This change should have minimal/no impact on code behavior. However, we found that some SQL query cannot return expected rows after the change. Query will return empty values even if the entry is in the table.

Parallel query hinting boosts certain query performances on multi-processor systems via parallel processing. The SQL optimizer determines when this is beneficial. On single-processor systems, this hint has no effect.

Parallel processing can be managed by:

1. Setting the auto parallel option system-wide.
2. Using the %PARALLEL keyword in the FROM clause of specific queries.

%PARALLEL is ignored when it applied to:

Introduction

To achieve optimized AI performance, robust explainability, adaptability, and efficiency in healthcare solutions, InterSystems IRIS serves as the core foundation for a project within the x-rAI multi-agentic framework. This article provides an in-depth look at how InterSystems IRIS empowers the development of a real-time health data analytics platform, enabling advanced analytics and actionable insights. The solution leverages the strengths of InterSystems IRIS, including dynamic SQL, native vector search capabilities, distributed caching (ECP), and FHIR interoperability. This innovative approach directly aligns with the contest themes of "Using Dynamic SQL & Embedded SQL," "GenAI, Vector Search," and "FHIR, EHR," showcasing a practical application of InterSystems IRIS in a critical healthcare context.

High-Performance Message Searching in Health Connect

The Problem

Have you ever tried to do a search in Message Viewer on a busy interface and had the query time out? This can become quite a problem as the amount of data increases. For context, the instance of Health Connect I am working with does roughly 155 million Message Headers per day with 21 day message retention. To try and help with search performance, we extended the built-in SearchTable with commonly used fields in hopes that indexing these fields would result in faster query times. Despite this, we still couldn't get some of these queries to finish at all.

Hi Community,

I've created a method in my File Service to do a cleanup for every file load. Currently, I've set it to delete data when LastUpdated date is greater than maxdate. However, I want to do a cleanup for every new file load. Any suggestions or advice on how to do this? Thanks!

Method Cleanup()
{

February 19, 2025 – Alert: SQL Queries Returning Wrong Results

InterSystems has corrected two issues that can cause a small number of SQL queries to return incorrect results. In addition, InterSystems has corrected an inconsistency in date/time datatype handling that may lead to different, unexpected – yet correct – results for existing applications that rely on the earlier, inconsistent behavior.

DP-436825: SQL Queries with Lateral Join May Return Wrong Results

Using SQL Gateway with Python, Vector Search, and Interoperability in InterSystems Iris

Part 2 – Python and Vector Search

Since we have access to the data from our external table, we can use everything that Iris has to offer with this data. Let's, for example, read the data from our external table and generate a polynomial regression with it.

For more information on using python with Iris, see the documentation available at https://docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls?KEY=AFL_epython

Let's now consume the data from the external database to calculate a polynomial regression. To do this, we will use a python code to run a SQL that will read our MySQL table and turn it into a pandas dataframe:

In this article, exceptions are covered.

Working with Exceptions

Instead of returning a %Status response, you can raise and throw an Exception. You are then responsible for catching the exception and validating it. IRIS provides five main classes to handle exceptions effectively. Additionally, you can create custom exception class definition based on your needs.

From the previous article, we identified some issues when working with JSON in SQL.

IRIS offers a dedicated feature for handling JSON documents, called DocDB.

InterSystems IRIS® data platform DocDB is a facility for storing and retrieving database data. It is compatible with, but separate from, traditional SQL table and field (class and property) data storage and retrieval. It is based on JSON (JavaScript Object Notation) which provides support for web-based data exchange. InterSystems IRIS provides support for developing DocDB databases and applications in REST and in ObjectScript, as well as providing SQL support for creating or querying DocDB data.

By its nature, InterSystems IRIS Document Database is a schema-less data structure. That means that each document has its own structure, which may differ from other documents in the same database. This has several benefits when compared with SQL, which requires a pre-defined data structure.

The word “document” is used here as a specific industry-wide technical term, as a dynamic data storage structure. “Document”, as used in DocDB, should not be confused with a text document, or with documentation.

Let's explore how DocDB can help store JSON in the database and integrate it into projects that rely solely on xDBC protocols.