InterSystems Developer Community is a community of 25,072 amazing developers
We're a place where InterSystems IRIS programmers learn and share, stay up-to-date, grow together and have fun!

What We’re Looking For:
3+ years of experience with InterSystems IRIS, Caché, or Ensemble
Strong skills in ObjectScript, SQL, and scripting languages (Python/Java a plus)
Experience with healthcare interoperability (HL7, FHIR, X12)
Cloud knowledge is a bonus (Azure, AWS, or GCP)
Curious mind + collaborative spirit + bias for action

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Question
· Dec 17, 2024
UDP Adapter not working

Hello

I am trying to work with UDP Connection/Adapter and I get this error.

ERREUR #5002: Erreur Cache: <WRITE>zSend+5^EnsLib.UDP.Common.1

Here is the method

Class TEMPLATE.BO.UDPSend Extends Ens.BusinessOperation
{

Parameter ADAPTER = "EnsLib.UDP.OutboundAdapter";

Property Adapter As EnsLib.UDP.OutboundAdapter;

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Hi ,

I am creating a report on Trakcare with the help of Jreport. But the date parameter from trakcare is not reading on Jreport and the report is blank.

If I hardcode the date on Jreport,it is displaying on Trakcare but not taking report from Trakcare.

Parameter used on report manager on Trakcare is $g(%request.Data("DateFrom",1)).

Can anyone please tell me any change of settings on Trakcare or Jreport will help to work ?

Thanks for the help

Jude

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Hi Community,

We're excited to invite you to the webinar Configuring a Web Server for use with InterSystems IRIS.

Join this webinar for an engaging and insightful tech talk on the configuration of a web server to use with InterSystems IRIS.

Date & Time: Thursday, February 27, 10:30 AM GMT

👨‍🏫 Speaker: @Steve Mallam, HealthShare Team Lead, InterSystems UK&I

2025 Tech Talk Social Tile 27 Feb

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InterSystems has corrected a defect that causes invalid database and journal records to be introduced when using a specific $LIST syntax. The likelihood of encountering this defect is very low but the operational impacts can be significant.

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If you thought native Go support for IRIS was exciting, wait until you see what happens when GORM enters the mix.


Just recently, we welcomed native GoLang support for InterSystems IRIS with the release of go-irisnative. That was just the beginning. Now, we’re kicking things up a notch with the launch of gorm-iris — a GORM driver designed to bring the power of Object Relational Mapping (ORM) to your IRIS + Go stack.

Why GORM?

GORM is one of the most popular ORM libraries in the Go ecosystem. It makes it easy to interact with databases using Go structs instead of writing raw SQL. With features like auto migrations, associations, and query building, GORM simplifies backend development significantly.

So naturally, the next step after enabling Go to talk natively with IRIS was to make GORM work seamlessly with it. That’s exactly what gorm-iris does.

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Article
· May 2 3m read
Minify XML in IRIS

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.

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While starting with Intersystems IRIS or Cache, developers often encounter three core concepts: Dynamic Objects, Globals & Relational Table. Each has its role in building scalable and maintainable solutions. In this article, we'll walk through practical code examples, highlight best practices, and show how these concepts tie together.

1. Working with Dynamic Objects:

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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.

A workload averaging 1000 1-kilobyte queries per second is compared with another involving 10 1-terabyte queries per hour

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.

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🚨 Update, January 21, 2025 – Maintenance Completed 🚨

The planned maintenance on the Developer Community has been successfully completed! While the site is up and running, we are still performing additional testing, so occasional issues may occur.

We apologize for any inconvenience and greatly appreciate your patience. If you notice anything unusual, please don’t hesitate to let us know.

Thank you for your understanding and support!

9 Under Maintenance Page Tips | Messages for a Down Website

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Hi Community,

Enjoy the new video on InterSystems Developers YouTube:

Boost Your Productivity with Command History & Shell Aliases in InterSystems IRIS

https://www.youtube.com/embed/R1W9f0w8n7s
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Hi Community,

Enjoy the new video on InterSystems Developers YouTube:

Security In the Cloud - Assuring a Secure InterSystems IRIS Cloud Overlay @ Global Summit 2024

https://www.youtube.com/embed/BGQG7gwBxZc
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Let's suppose two different routines use one and the same chunk of code. From the object-oriented POV, a good decision is to have this chunk of code in a separate class and have both routines call it. However, whenever you call code outside of the routine as opposed to calling code in the same routine, some execution speed is lost. For reports churning through millions of transactions this lost speed might be noticeable. Any advice how to optimize specifically speed?

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At the moment we're creating multiple BPLs are using a router (or another BPL) to direct to these based on a unique key modulo the amount of BPLs available, e.g. if we have 3 BPLs created.

Message key = 1 mod 3 + 1 -> BPL02
Message key = 2 mod 3 + 1 -> BPL03
Message key = 3 mod 3 + 1 -> BPL01

FIFO only matters in that each messages for each key is processed in order.

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Hey Community,

We’re excited to announce a new upgrade to Developer Community AI!

Now, in addition to Community posts and comments, Developer Community AI also pulls information from official InterSystems IRIS documentation to answer your questions. This new feature lets you customise your search sources so you get the most relevant answers tailored to your needs.

Just choose the sources and ask the question:

  • Community posts
  • InterSystems IRIS Documentation
  • or both!

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In this article I'll show you how to set up in your laptop, very quickly, a cluster of IRIS nodes in sharding. It's not the goal of this article neither to talk about sharding in detail nor define a deployment of a production ready architecture, but to show how to set up quickly, in your own machine, a cluster of IRIS instances configured as shard nodes, with which you'll able to play and test this functionality. If you're insterested in knowing more about sharding in IRIS, take a look at the documentation clicking here.

First and foremost, I want to remark that IRIS sharding will allow us 2 things:

  • Define, load and query shard tables, which data will be distributed transparently between the cluster's nodes
  • Define federated tables, which offer a global and composed view of data belonging to different tables that will be physically stored in different distributed nodes

So, as I said, we let for other article playing with shard or federated tables, and just focus now in the previous step, that is, setting up the cluster of shard nodes.

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