This example is extracted from a long-running installation.
The purpose is to have simple monitoring of several servers at a rather primitive level.
Just slightly more intelligent than a raw PING. But still easy to integrate.
It avoids the overkill of information you are often confronted with while you are just
interested in the number of active processes or similar basic figures.
The example shows a basic skeleton that might be easily filled by your real needs.

It consists of 3 sections:

Intro

Please note, this article is considered deprecated, check out the new revision over here: https://community.intersystems.com/post/tutorial-websockets

The goal of this post is to discuss working with Websockets in a Caché environment. We are going to have a quick discussion of what websockets are and then talk through an example chat application implemented on top of Websockets.

Recently, a partner company started to develop an Angular client for their Cache application. Together, we decided to leverage the power of Caché dynamic objects to exchange JSON encoded data between client and server parts. However, we realized that currently there is a gap in Cache JSON implementation that prevents simple use of traditional registered and persistent classes to exposed their data with the same ease as with XML. I wrote a small JSON adapter, that does the job and bridgers the gap. It's purpose is simple expose data described by a regular Cache class in a one-to-one fashion to a %DynamicObject. On the other hand, when a serialized JSON data comes in, it can be easily deserialized into dynamic object and subsequently bound to regular class by the newly created adapter.

Last week, we announced the InterSystems IRIS Data Platform, our new and comprehensive platform for all your data endeavours, whether transactional, analytics or both. We've included many of the features our customers know and loved from Caché and Ensemble, but in this article we'll shed a little more light on one of the new capabilities of the platform: SQL Sharding, a powerful new feature in our scalability story.

Ansible helped me solve the problem of quickly deploying Caché and application components for Data Platforms benchmarks. You can use the same tools and methodology for standing up your test labs, training systems, development or other environments. If you deploy applications at customer sites you could automate much of the deployment and ensure that system, Caché and your application are configured to your applications best practice standards.

Introduction

The field test of Caché 2016.2 has been available for quite some time and I would like to focus on one of the substantial features that is new in this version: the document data model. This model is a natural addition to the multiple ways we support for handling data including Objects, Tables and Multidimensional arrays. It makes the platform more flexible and suitable for even more use cases.

[Background]

InterSystems IRIS family has a nice utility ^SystemPerformance (as known as ^pButtons in Caché and Ensemble) which outputs the database performance information into a readable HTML file. When you run ^SystemPerformance on IRIS for Windows, a HTML file is created where both our own performance log mgstat and Windows performance log are included.

Considering new business interest in applying Generative-AI to local commercially sensitive private data and information, without exposure to public clouds. Like a match needs the energy of striking to ignite, the Tech lead new "activation energy" challenge is to reveal how investing in GPU hardware could support novel competitive capabilities. The capability can reveal the use-cases that provide new value and savings.

Sharpening this axe begins with a functional protocol for running LLMs on a local laptop.

Hello DC members,

Considering the unparalleled success of last year's activity, we've decided to prepare it for you once more. So, without further ado, welcome

🔥 YOUR 2023 RECAP 🔥

iris-docker-multi-stage-script

A python script to keep your docker iris images in shape ;)

Witout changing your dockerfile or your code you can reduce the size of your image by 50% or more !

TL;DR

Name the builder image builder and the final image final and add this to end of your Dockerfile:

Modify your Dockerfile to use a multi-stage build:

ARG IMAGE=intersystemsdc/irishealth-community:latest
FROM $IMAGE as builder

Add this to end of your Dockerfile:

FROM $IMAGE as final

ADD --chown=${ISC_PACKAGE_MGRUSER}:${ISC_PACKAGE_IRISGROUP} https://github.com/grongierisc/iris-docker-multi-stage-script/releases/latest/download/copy-data.py /irisdev/app/copy-data.py

RUN --mount=type=bind,source=/,target=/builder/root,from=builder \
    cp -f /builder/root/usr/irissys/iris.cpf /usr/irissys/iris.cpf && \
    python3 /irisdev/app/copy-data.py -c /usr/irissys/iris.cpf -d /builder/root/ 

Boom! You're done!

Write $Piece($SYSTEM.SYS.Horolog(),",",1)

We are glad to announce that DBeaver has supported InterSystems IRIS out-of-the-box since version 7.2.4. You don't need to configure it manually anymore, just find the IRIS icon in the Connections list.

This tutorial is a follow on to Working with %Query
It was displaying the content of the input stream chopped in fixed-size chunks.
But often those streams are structured and have well-defined separators (e.g HL7)
So as a side subject of this tutorial, this chapter shows how to break a stream into PIECES.

It is exactly the same idea as the $PIECE() function for strings with some add-ons.

Hey Community,

More than 700 applications are already available for everyone to download on the InterSystems Open Exchange.

And now it's time to announce the best developers and the most downloaded apps for 2022!

Let's take a closer look at our heroes and their apps: