Article
David Underhill · 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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On the Latest GlobalSummit 2022, InterSystems Introduced Cloud SQL. So, you may have lightweight InterSystems IRIS with access to SQL only. Well, what if you would still need some Interoperability features in the cloud as well? There are various solutions on the market nowadays, which offer a bunch of integration adapters out of the box and can be extended with support from the community. Some time ago, I've implemented an adapter for the Node-RED project, which can be deployed manually everywhere you want. Now I would like to introduce a new integration with my recent discovery, n8n.io

Banner image

n8n.io is a workflow automation platform, that supports over 200 different integrations out of the box and from a community, and now including InterSystems IRIS.

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Hello everyone, I’m a French student that just arrived in Prague for an academical exchange for my fifth year of engineering school and here is my participation in the interop contest.

I hadn’t much time to code since I was moving from France to Prague and I’m participating alone, so I decided to make a project that’s more like a template rather than an application.

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I am happy to share with you my first experience of using a docker container version of IRIS for Health to explore your interest in using or having a trial by taking the advantage of a docker container that is lightweight, and easy to deploy. This cookbook will go through the implementation steps using the GitHub repository called ENSDEMO written by Renan Lourenco.

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Date and Time is an important factors in our life. Because all applications are engaging based on Date/Time. But our world is split into multiple time zones. if our product is launched in the world, to maintain the history of events we are definitely required to convert all times to our local time or UTC ( Coordinated Universal Time ). As I know, many known programming languages of C#, JavaScript, Java, etc., provided the library to convert the date and time. i.e with a time zone name we can be able to convert without knowing the UTC offset.

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I recently started to study interoperability and I found the official documentation very helpful in understanding how it works, though I still had some trouble implementing it myself. With the help I got from my coworkers, I managed to create a Demo of a system and learn through practice. Because of that, I decided to write this to help others with "getting their hands dirty" and share the help I got.

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Say I've been developing a web application that uses IRIS as the back end. I've been working on it with unauthenticated access. It's getting to the point where I would like to deploy it to users, but first I need to add authentication. Rather than using the default IRIS password authentication, I'd like users to sign in with my organization's Single Sign On, or some other popular identity provider like Google or GitHub. I've read that OpenID Connect is a common authentication standard, and it's supported by IRIS. What is the simplest way to get up and running?

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In this GitHub we fine tune a bert model from HuggingFace on review data like Yelp reviews.

The objective of this GitHub is to simulate a simple use case of Machine Learning in IRIS :
We have an IRIS Operation that, on command, can fetch data from the IRIS DataBase to train an existing model in local, then if the new model is better, the user can override the old one with the new one.
That way, every x days, if the DataBase has been extended by the users for example, you can train the model on the new data or on all the data and choose to keep or let go this new model.

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Hi Developers!

Sometimes we need to import CSV data programmatically to InterSystems IRIS either from CSV or from URL. And we expect the class with proper datatypes to be created and the data to be imported.

I published a module csvgen on Open Exchange which does exactly that.

If you just need the CSV file be imported into IRIS you can do the following:

USER>do ##class(community.csvgen).Generate("/usr/data/titanic.csv",,"Data.Titanic")

Class name: Data.Titanic
Header: PassengerId INTEGER,Survived INTEGER,Pclass INTEGER,Name VARCHAR(250),Sex VARCHAR(250),Age INTEGER,SibSp INTEGER,Parch INTEGER,Ticket VARCHAR(250),Fare MONEY,Cabin VARCHAR(250),Embarked VARCHAR(250)
Records imported: 891
USER>

Or if you have the CSV on the internet, e.g. COVID-19 Data on Github you can get the data in the following way:

USER>d ##class(community.csvgen).GenerateFromURL("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/05-29-2020.csv",",","Data.Covid19")

Class name: Data.Covid19
Header: FIPS INTEGER,Admin2 VARCHAR(250),Province_State VARCHAR(250),Country_Region VARCHAR(250),Last_Update DATE,Lat MONEY,Long_ DOUBLE,Confirmed INTEGER,Deaths INTEGER,Recovered INTEGER,Active INTEGER,Combined_Key VARCHAR(250),Incidence_Rate DOUBLE,Case-Fatality_Ratio DOUBLE
Records imported: 3522
USER>

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Being interoperable is more and more important nowadays. InterSystems IRIS 2022.1 comes with a new messaging API to communicate with event streaming platforms like Kafka, AWS SQS/SNS, JMS and RabbitMQ.

This article shows how you can connect to Kafka and AWS SQS easily.
We start with a brief discussion of the basic concepts and terms of event streaming platforms.

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In this article you will have access to the curated base of articles from the InterSystems Developer Community of the most relevant topics to learning InterSystems IRIS. Find top published articles ranked by Machine Learning, Embedded Python, JSON, API and REST Applications, Manage and Configure InterSystems Environments, Docker and Cloud, VSCode, SQL, Analytics/BI, Globals, Security, DevOps, Interoperability, Native API. Learn and Enjoy!

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One of the most important tasks of dashboards is, on the one hand, to help you perceive data in an aggregated form, and, on the other hand, not to lose the depth of immersion in the data if you need this. One of the tools that help us achieve this result is drill down. It enables us to display several hierarchical levels of data, from aggregated to detailed.

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Article
Iryna Mykhailova · Aug 2, 2022 8m read
Data models in InterSystems IRIS

Before we start talking about databases and different data models that exist, first we'd better talk about what a database is and how to use it.

A database is an organized collection of data stored and accessed electronically. It is used to store and retrieve structured, semi-structured, or raw data which is often related to a theme or activity.

At the heart of every database lies at least one model used to describe its data. And depending on the model it is based on, a database may have slightly different characteristics and store different types of data.

To write, retrieve, modify, sort, transform or print the information from the database, a software called Database Management System (DBMS) is used.

The size, capacity, and performance of databases and their respective DBMS have increased by several orders of magnitude. It has been made possible by technological advances in various areas, such as processors, computer memory, computer storage, and computer networks. In general, the development of database technology can be divided into four generations based on the data models or structure: navigational, relational, object and post-relational.

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On this GitHub you can find all the information on how to use a HuggingFace machine learning / AI model on the IRIS Framework using python.

1. iris-huggingface

Usage of Machine Learning models in IRIS using Python; For text-to-text, text-to-image or image-to-image models.

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