Article
· Jul 12, 2019 2m read
Basic Database Metrics example

This is a self contained class that can be run from the Intersystems Task Scheduler which records peak usage details for databases and licenses built up throughout the day and retaining 30 days history.

To schedule the task to run every hour:

d ##class(Metrics.Task).Schedule()

You can also specify your own start time, stop time, and run interval:

d ##class(Metrics.Task).Schedule(startTime, stopTime, intervalMins)

Metrics are stored in ^Metrics in the namespace that the class resides in/is run from.

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Why to use it

This app offers an easy interface to analyze memory:

  • Filter by database (namespace), global name, used size, or allocated size;
  • View a sum of the used and allocated sizes for the filters applied;
  • Export the table to JSON, CSV, or XML.

How to use it

Follow the instructions on the README file from the GitHub repository, and configure the settings to connect to your instance.

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Article
· Apr 20, 2021 2m read
ZPM Explorer

Hi Community,

@José Pereira and I want to introduce ZPM Explorer, our graphic interface to explorer the greats applications that we have inside InterSystems Package Manager.

The idea

ZPM Explorer's idea is to make it easier for people to find out what ZPM offers. Every week, every day, a new app joins the ZPM world, so why not help developers and non-developers take advantage of this incredible world?!

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image

Hi Community,
In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):

  • Step1: Install required libraries

  • Step2: Create patterns and responses file

  • Step3: Train the Model

  • Step4: Create ChatBot Application based on the trained model

So Let us start.

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Article
· Oct 24, 2022 9m read
PerfTools IO Test Suite

Purpose

This pair of tools (RanRead and RanWrite) is used to generate random read and write events within a database (or pair of databases) to test Input/Output operations per second (IOPS). They can be used either in conjunction or separately to test IO hardware capacity, validate target IOPS, and ensure acceptable disk response times are sustained. Results gathered from the IO tests will vary from configuration to configuration based on the IO sub-system. Before running these tests ensure corresponding operating system and storage level monitoring are configured to capture IO performance metrics for later analysis. The suggested method is by running the System Performance tool that comes bundled within IRIS. Please note that this is an update to a previous release, which can be found here.

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FHIR has revolutionized the healthcare industry by providing a standardized data model for building healthcare applications and promoting data exchange between different healthcare systems. As the FHIR standard is based on modern API-driven approaches, making it more accessible to mobile and web developers. However, interacting with FHIR APIs can still be challenging especially when it comes to querying data using natural language.

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Hi Developers!

Often I find questions on how to install IRIS, connect to IRIS from IDE, setup the environment, compile, debug, maintain the repository.

Here below possibly the shortest way to set up all the environment and start development with ObjectScript on InterSystems IRIS.

Prerequisites

Make sure you have Git, Docker, and VSCode installed

Install Docker and ObjectScript extensions into VSCode

Sign in or Create an account on Github

Here we go!

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Last time we launched an IRIS application in the Google Cloud using its GKE service.

And, although creating a cluster manually (or through gcloud) is easy, the modern Infrastructure-as-Code (IaC) approach advises that the description of the Kubernetes cluster should be stored in the repository as code as well. How to write this code is determined by the tool that’s used for IaC.

In the case of Google Cloud, there are several options, among them Deployment Manager and Terraform. Opinions are divided as to which is better: if you want to learn more, read this Reddit thread Opinions on Terraform vs. Deployment Manager? and the Medium article Comparing GCP Deployment Manager and Terraform.

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Most of us are more or less familiar with Docker. Those who use it like it for the way it lets us easily deploy almost any application, play with it, break something and then restore the application with a simple restart of the Docker container.

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Why should you connect Flask to InterSystems IRIS?

The first thing that comes to mind when we think about combining Flask with IRIS is a portal to interact with your clients and partners. A good example would be a website for patients to access their clinical exams. Of course, this case would require a whole new layer of security, which we did not cover in our last article. However, we can effortlessly add it with Werkzeug, for instance.

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Hi Developers!

"objectscript.conn" :{
      "ns": "IRISAPP",
      "active": true,
      "docker-compose": {
        "service": "iris",
        "internalPort": 52773
      }

I want to share with you a nice new feature I came across in a new 0.8 release of VSCode ObjectScript plugin by @Dmitry Maslennikov and CaretDev.

The release comes with a new configuration setting "docker-compose" which solves the issue with ports you need to set up to make your VSCode Editor connect to IRIS. It was not very convenient if you had more than one docker container with IRIS running on the same machine. Now, this is solved!

Read below how it works now.

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Article
· Feb 24, 2023 2m read
InterSystems IRIS in Containers

InterSystems has also released IRIS as containerized deployments. This post is to demonstrate how InterSystems IRIS and applications those rely on IRIS a backend can be packaged into an image and be run in other machines in containers and how simple it is to do that.

A container runs image/s which have all the necessary executables, binary code, libraries, and configuration files. And the images can be moved from one machine to another, and an images repository like Docker Hub can simplify that process.

I have used an application from Open Exchange for this demo.

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Hi Developers!

Another way to start using InterSystems ObjectScript Package Manager is to use prebuilt container images of InterSystems IRIS Community Edition and InterSystems IRIS for Health Community Edition.

We deploy this IRIS images on DockerHub and you can run it with the following command:

docker run --rm -p 52773:52773 --init --name my-iris -d intersystemsdc/iris-community:2019.4.0.383.0-zpm

Launch a terminal with:

docker exec -it my-iris iris session IRIS

And install zpm-module as:

USER>zpm 

zpm: USER>install objectscript-math

[objectscript-math] Reload START

[objectscript-math] Reload SUCCESS

[objectscript-math] Module object refreshed.

[objectscript-math] Validate START

[objectscript-math] Validate SUCCESS

[objectscript-math] Compile START

[objectscript-math] Compile SUCCESS

[objectscript-math] Activate START

[objectscript-math] Configure START

[objectscript-math] Configure SUCCESS

[objectscript-math] Activate SUCCESS

zpm: USER>

And use same commands for InterSystems IRIS for Health using the tag: intersystemsdc/irishealth-community:2019.4.0.383.0-zpm

The images are being published on IRIS Community Edition and IRIS Community Edition for Health repositories of Docker Hub.

We will update tags with every new release of IRIS and ZPM.

Happy coding!

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Article
· Apr 20, 2021 3m read
Why gj :: locate?

You may think it isn’t too difficult to get from label+offset^routine to the actual source line responsible for the error. For an expert it isn't that hard... most of the time. But there are enough oddities and special rules that even an expert can get it wrong, whilst spending a lot of time trying to get there.

gj :: locate is the latest tool from George James Software – it debugs any error, class or routine by converting the location of an error in compiled .int code to the corresponding location in your source, and then taking you right there.

Image this scenario…

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In recent years, artificial intelligence technologies for text generation have developed significantly. For example, text generation models based on neural networks can produce texts that are almost indistinguishable from texts written by humans.
ChatGPT is one such service. It is a huge neural network trained on a large number of texts, which can generate texts on various topics and be matched to a given context.

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