Hi Community,

This post is a introduction of my open exchange iris-climate-change application.

iris-climate-change web application Imports The Food and Agriculture Organization (FAO) Climate Change dataset by using LOAD DATA (SQL) functionality and analyze and visualize data with the help of Python Flask Web Framework, Pandas Python data analysis Library, Plotly Open Source Graphing Library for Python and Plotly JavaScript Open Source Graphing Library.

Foreword

InterSystems IRIS versions 2022.2 and newer feature the ability to authenticate to a REST API using JSON web tokens (JWTs). This feature enhances security by limiting where and how often passwords transfer over the network in addition to setting an expiration time on access.

The goal of this article is to serve as a tutorial on how to implement a mock REST API using InterSystems IRIS and lock access to it behind JWTs.

Talking with a friend of mine, Machine Learning specialist @Renato Banzai , he brought one of the biggest challenges faced by companies nowadays: deploying ML/AI in live environments.

I ran into an interesting ObjectScript use case today with a general solution that I wanted to share.

Use case:

I have a JSON array (specifically, in my case, an array of issues from Jira) that I want to aggregate over a few fields - say, category, priority, and issue type. I then want to flatten the aggregates into a simple list with the total for each of the groups. Of course, for the aggregation, it makes sense to use a local array in the form:

agg(category, priority, type) = total

Such that for each record in the input array I can just:

With InterSystems IRIS 2022.2, we introduced Columnar Storage as a new option for persisting your IRIS SQL tables that can boost your analytical queries by an order of magnitude. The capability is marked as experimental in 2022.2 and 2022.3, but will "graduate" to a fully supported production capability in the upcoming 2023.1 release.

The product documentation and this introductory video, already describe the differences between row storage, still the default on IRIS and used throughout our customer base, and columnar table storage and provide high-level guidance on choosing the appropriate storage layout for your use case. In this article, we'll elaborate on this subject and share some recommendations based on industry-practice modelling principles, internal testing, and feedback from Early Access Program participants.

The goal of this “DeepSee Troubleshooting Guide” is to help you track down and fix problems in your DeepSee project.

If the problem can’t be fixed by following the guidelines, you will at least have enough information to submit a WRC issue with DeepSee Support and provide all the evidence to us, so we can continue the investigation together and resolve it faster!

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.

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:

Working in support, I usually get asked how many days I should keep journals. Should it be two days or after two backups? More? Less? Why two?

The correct answer (for most of the environments) is that you should keep the journals since the last validated Backup. I.e., until you don't check if a Backup is valid (restoring the file and checking with the Integrity utility), you can't be sure there is a good copy of your data and can't purge the journals safely.

In this article I'll show you how you can easily convert between many different document formats, these to be precise:

• in: html, odt, doc, docx, rtf, sdw, txt...
• out: html, odt, docx, rtf, pdf, txt, tex, xhtml...

and many more, check out this table. We'll use LibreOffice as a conversion engine.

Running a Background Job using the JOB command is a well-known feature.
Using ECP to distribute databases to several servers is also well-known.
But using the combination of both to run a process on a different server
seems to be a rare case.

Sure there are enough other ways to start a remote job, but the special
combination with ECP where the application server starts a process on a
data server without additional networking is worth to be remembered.

Hello everyone!

We are Longevica (https://www.longevica.com/) Healthtech, a Boston-based healthy aging digital health startup. Longevica was born as a research company back in 2009; we pioneered the screening of chemicals, which would drastically extend the life span. With 1000 screened pharmaceuticals and 20 000 mice experiments, we have identified specific compounds that, if taken daily, could extend life by years. This discovery leads to two questions: how to measure the effect of aging progress in real-time and how to make this a lifelong habit. This led us to the digital health market to create a new company Longevica HealthTech.

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.