Hi Developers!
Let me share with you some exercises from a workshop about developing interoperability components in Java using PEX (Production EXtension).

You will find slides and step by step exercises about:

• Understanding simple PEX components coded in Java.
• Making some changes on them.

All built using containers, so you don't need to build a local Java environment.

At the heart of IRIS and Cache is a very interesting database architecture that we, at M/Gateway Developments, refer to as "Global Storage". If you ever wanted to know more about the fundamentals and capabilities of this underlying database, you might want to read a major analysis we've put together:

https://github.com/robtweed/global_storage

Amongst other things you'll discover that:

Let's imagine if you would like to write some real web application, for instance, some simple clone of medium.com. Such sort of application can be written using any different language on the backend side, or with any framework on the frontend side. So many ways to do the same application, and you can look at this project. Which offers a bunch of frontends and backends realizations for exactly the same application. And you can easily mix them, any chosen frontend should work with any backend.

Let me introduce the same application realization for InterSystems IRIS on a backend side.

In this 3-part series of articles, is shown how you can use IAM to simply add security, according to OAuth 2.0 standards, to a previously unauthenticated service deployed in IRIS.

In the first part, was provided some OAuth 2.0 background together with some IRIS and IAM initial definitions and configurations in order to facilitate the understanding of the whole process of securing your services.

Earlier this year, the AppS.REST package was released. AppS.REST is a framework for easily exposing existing persistent classes in IRIS as REST resources. AppS.REST-enabled classes support CRUD operations with little effort from the developer, bridging the gap between persistent data in IRIS and data consumers, such as an Angular front end application.

But IRIS classes are much more than just a definition for loading and saving individual records! This article aims to highlight a few ways to leverage the power of IRIS in your REST applications. Using the Phone.Contact sample app, we'll look at out-of-the-box query support, use of class queries and finally ObjectScript methods.

According to IDC, more than 80% of information it is NoSQL, especially text into documents. When the digital services or applications not process all this information, the business lose. To face this challenge, it is possible use OCR technology. OCR uses machine learning and/or trained image patterns to transform image pixels into text. This is important, because many documents are scanned into images inside PDF, or many documents contains images with text inside. So OCR are an important step to get all possible data from a document.

In addition to IntelliJ IDEA, it's now possible to configure SublimeText3 to be able to compile code there too. And what you will need is just the language server I did.

Let me introduce the support of InterSystems IRIS in IntelliJ IDEA. This plugin adds Syntax Highlighting support for ObjectScript, and auto import and compile on the server after saving a changed file. It uses LanguageServer written in Rust, where was added an ability to import and compile code.

Hello everyone, let me introduce, one of my latest projects. It is a DataSource plugin for Grafana, which can connect directly to InterSystems IRIS and gather any data (in the future).

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…

Hi Developers,

In the previous article, we describe how to use config-api to configure IRIS.

Now, let's try to combine the library with the ZPM client.
The goal is to load a configuration document during zpm install at the configure phase.

For this exercise, a template repository is available here (this is based on objectscript-docker-template ).

When you have more than ten thousand users in your database, it becomes time-consuming and inconvenient to assign group access rights through the standard IRIS interface. In this artilce I want to introduce you an application to automate this process.

I’ll show you how to assign and change the role lists for the users, selecting them by context, and I will also show you how to expand this application’s functionality. you know how to apply your work as an administrator and developer to the new features of the well-proven apptools software complex. With the addition of an adminLTE template, you can now quickly and easily create interface interactions for any entity and many functional modules.
The goal while developing this toolkit is to write as little JavaScript as possible as well as transfer and implement all the dynamics as much as possible in the ObjectScript.

Ever noticed that the Type column of the Web Applications page in the Security section of Management Portal is showing you redundant information?

What is Distributed Artificial Intelligence (DAI)?

Attempts to find a “bullet-proof” definition have not produced result: it seems like the term is slightly “ahead of time”. Still, we can analyze semantically the term itself – deriving that distributed artificial intelligence is the same AI (see our effort to suggest an “applied” definition) though partitioned across several computers that are not clustered together (neither data-wise, nor via applications, not by providing access to particular computers in principle). I.e., ideally, distributed artificial intelligence should be arranged in such a way that none of the computers participating in that “distribution” have direct access to data nor applications of another computer: the only alternative becomes transmission of data samples and executable scripts via “transparent” messaging. Any deviations from that ideal should lead to an advent of “partially distributed artificial intelligence” – an example being distributed data with a central application server. Or its inverse. One way or the other, we obtain as a result a set of “federated” models (i.e., either models trained each on their own data sources, or each trained by their own algorithms, or “both at once”).