We have been storing raw messages in a MySQL database for DR and ad hoc purposes. We are thinking of using an Ensemble instance as our data lake instead. We could segregate the source data by namespace or by global. But either way we'll want a custom global to index the data for data retrieval performance purposes.

Anyone else taking this approach? Any feedback?

If you work with Productions, highlighting connections between Business Hosts is a very convenient feature, allowing developers to get a visual representation of a data flow.

This feature works by default with all system Business Hosts. If a user writes their own Business Services, Processes, or Operations, they must implement the OnGetConnections method for this functionality to work with their custom Business Hosts (or use Ens.DataType.ConfigName properties for connections).
That said, the SMP shows only the first layer of connections of the selected Business Host. Sometimes, we need to get connections of connections recursively to build a complete data flow graph. Or we might need this connection information to check which downstream systems might be affected by a change upstream.

In the discussions at https://community.intersystems.com/post/intersystems-studio-deprecated-starting-20232 one notable topic has been that some Studio users make regular use of its facilities for exporting multiple code artifacts (e.g. classes and routines) into a single XML file on the workstation, then exporting that file into a different server namespace.

Some Usage cases

1. A deployment may consist of two high availability instances and two disaster recovery instances in a different data center.

The corresponding UAT environment could replicate this giving a total of 8 instances. How do you confirm CPF and Scheduled task alignment across ALL instances.

I have created a package that offers a utility to load a Global into JSON object and reverse
to create a Global from this type of JSON object. Efficient refers to the structure created.
Only Globals nodes containing data are presented in the generated JSON object.

I want to create simple CSP page with some fields to filter and a table in the page that shows results from a table. Where to start any tips

Let's say we have two serial classes, one as a property of another:

Class test.Serial Extends %SerialObject
{
Property Serial2 As test.Serial2;
}

Class test.Serial2 Extends %SerialObject
{
Property Property As %String;
}

And a persistent class, that has a property of test.Serial type:

Class test.Persistent Extends %Persistent
{

Property Datatype As %String;

Property Serial As test.Serial;

}

So it's a serial, inside a serial, inside a persistent object.

Hi,

Do you have any experience in inserting a panda dataframe into SQL IRIS Table?
The dataframe.to_sql method uses sqlalchemy and it seems no dialect is available for IRIS.

thanks,
Blaise

Intersytems documentation says not to hold entire SDA as object in In-memory.

http://localhost:57772/csp/documatic/%25CSP.Documatic.cls?LIBRARY=registry&CLASSNAME=HS.SDA3.Container&MEMBER=&CSPCHD=001000000000x1zyO3kkajy0iIjjpi2dxUJUnwFSY_pz7jtbNV&CSPSHARE=1

During the realization of some required functionality, I discovered that I need to use Deflate compression, and found, that IRIS does not offer any options, $system.Util.Compress, works one way open (GZIP/COMPRESS) totally different, even for the same algorithms, I see that what I would expect as a result for Deflate somewhere there, but Compress returns something more. Is there any way to get a correct result using just ObjectScript without a need to use external tools? Was it really so difficult to have Deflate implemented in the first place?

When there's a performance issue, whether for all users on the system or a single process, the shortest path to understanding the root cause is usually to understand what the processes in question are spending their time doing. Are they mostly using CPU to dutifully march through their algorithm (for better or worse); or are they mostly reading database blocks from disk; or mostly waiting for something else, like LOCKs, ECP or database block collisions?

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.

This tutorial is a follow on to Working with %Query
It was displaying the content of the input stream chopped in fixed-size chunks.
But often those streams are structured and have well-defined separators (e.g HL7)
So as a side subject of this tutorial, this chapter shows how to break a stream into PIECES.

It is exactly the same idea as the $PIECE() function for strings with some add-ons.

Hi developers! In this article I’m going to explore the use of Iris Python Native API in a specific problem: large strings to store.