When implementing our Deltanji source control product on Ensemble or HealthShare sites I sometimes encounter places that implement multiple production classes, e.g. one for their DEV environment and another for their LIVE one.

I have a few cubes and numerous dashboards and I am ready to deploy them to our end users and administrators. How to configure DeepSee so that users don’t disrupt each other’s areas and are restricted from using functionalities specific to developers?

See Part 1 here.

Part 2: Index Handling

Hi Developers!

InterSystems Package Manager (ZPM) is a great thing, but it is even better if you don't need to install it but can use immediately.

There are several ways how to do this and here is one approach of having IRIS container with ZPM built with dockerfile.

I've prepared a repository which has a few lines in dockerfile which perform the download and install the latest version of ZPM.

Add these lines to your standard dockerfile for IRIS community edition and you will have ZPM installed and ready to use.

To download the latest ZPM client:

As you know in Caché / IRIS you have the possibility to define a property as Multidimensional as documented here:
https://docs.intersystems.com/iris20201/csp/docbook/DocBook.UI.Page.cls?KEY=GOBJ_proplit#GOBJ_proplit_multidim
and the explanation of how to use it
https://docs.intersystems.com/iris20201/csp/docbook/Doc.View.cls?KEY=GOBJ_proplit#GOBJ_proplit_multidim_values

Though the access is quite comfortable (in traditional COS sense) there are 2 main restrictions that hurt:

#1) It is not saved to disk unless your application includes code to save it specifically.
#2) It cannot be stored in or exposed through SQL tables
there are some more
I'll show how to overcome these limits

In the previous articles, we learned the basics of using IMAP protocol to handle messages from mailboxes in an e-mail server. That was cool and interesting, but you could take advantage of implementations created by other ones, available in libraries ready to use.

This formation, accessible on my GitHub, will cover, in half a hour, how to read and write in csv and txt files, insert and get inside the IRIS database and a distant database using Postgres or how to use a FLASK API, all of that using the Interoperability framework using ONLY Python following the PEP8 convention.

In general Global Streams are data objects embedded in Classes / Tables.
Using and viewing them with SQL is normally a part of the access to the containing tables.

SO WHAT?

During debugging or searching for strange or unexpected behavior there could be the need to
get closer to the stored stream. No big problem with direct access to Globals with SMP or Terminal.
But with SQL you are lost.
So my tool provides dynamic access to Global Streams wherever you may need this
Special thanks to @Oliver Wilms for the inspiration for this tool.

InterSystems FAQ rubric

You can avoid the error by specifying a stream object as the argument of %ToJSON() used when generating a JSON string from a dynamic object.

A code example is below.

In today's digital age, effective data management and accurate information analysis are becoming essential for successful enterprise operations. InterSystems IRIS Data Platform offers two critical tools designed to provide convenient data management: ARCHITECT and ANALYZER.

In the last article we presented the architecture of our SMART On FHIR project, so it's time to get down to business and start configuring all the elements that we are going to need.

We will first start with Auth0.

AUTH0 configuration

We will start by creating an Auth0 account with a valid email, once registered we will have to create our first application, and we will do it from the menu on the left:

I have a large tree outside my window. Here in Phoenix, Arizona, USA it's still warm and sunny so our plants are still covered in lush foliage despite so many other places in the full throws of Autumn. My desk is oriented such that I can look out at the tree throughout the day.

Attached code contains a very basic $system.WorkMgr example.

It uses several jobs (workers) to update different chunks of rows of a table.

Steps:

• Creates a table with 100 records.
• Split table in chunks
• Initialize WorkMgr and queue chunks to workers.
  • Every worker simply sets its process number in the Job field of the processed row.

In this case, I have tested the example in a 8-core laptop: