The following post concludes the series with a list of all databases seen in the example for the fully flexible architecture.

Sometimes, we need to copy part of the properties of an object into a different one.
The simplest thing would be to do the following:

Set obj1.FirstName = obj2.FirstName

Set obj1.SecondName = obj2.SecondName

What happens if the object contains a large number of properties? or we just want to extract an important group of data, and complement the information in another object?

Having the following classes:

"Telegram" is a well-known instant messenger, which provides an API for creating bots. The features of this API allow you to create bots with a wide range of functionality including receiving payments.
With the help of the telegram bot, I solved a simple task - sending Alerts from Ensemble to Telegram.

Advantages: Alerts come to the mobile phone, a notification appears, so there is no need to install any additional applications (in contrast to the solution https://community.intersystems.com/post/sending-alerts-mobile-phone-using-pushover- httpoutboundadapter).

The bot can be extended by adding new commands, for example, for managing the Production or for solving other tasks in Iris, Ensemble or Cache.

This is a sample Ensemble/Health Connect production which demonstrates how to receive an HL7 order (ORM) inbound from a file, extract fields (in this case, basic demographic information), and insert those into a table in an external SQL database via ODBC.

Included in the zip file:

• Exported code
• Sample ORM message
• 'How to configure' doc

Last time, we stopped on static stuff. Let’s continue...

In this series of articles, I'd like to present and discuss several possible approaches toward software development with InterSystems technologies and GitLab. I will cover such topics as:

• Git 101
• Git flow (development process)
• GitLab installation
• GitLab Workflow
• Continuous Delivery
• GitLab installation and configuration
• GitLab CI/CD
• Why containers?
• Containers infrastructure
• GitLab CI/CD using containers

In the first article, we covered Git basics, why a high-level understanding of Git concepts is important for modern software development, and how Git can be used to develop software.

In the second article, we covered GitLab Workflow - a complete software life cycle process and Continuous Delivery.

In the third article, we covered GitLab installation and configuration and connecting your environments to GitLab

In the fourth article, we wrote a CD configuration.

In the fifth article, we talked about containers and how (and why) they can be used.

In this article let's discuss main components you'll need to run a continuous delivery pipeline with containers and how they all work together.

The following post is a guide to implement a basic architecture for DeepSee. This implementation includes a database for the DeepSee cache and a database for the DeepSee implementation and settings.

I've collated a list of public Caché Projects that I've been able to find through GitHub topics and various other searches, let me know if I have missed any and I will add them to the list.

The full list is here...

https://github.com/SeanConnelly/Public-Cache-Projects/blob/master/README.md

I recently helped a site investigate a problem that appeared after they upgraded their Windows instance of Caché from 2015.1 to 2017.1. A terminal session launched from the server's desktop cube was unable to run OS-level commands using the $ZF(-1) function. For instance, using the no-op command "REM" as follows:

write $zf(-1,"rem")

was returning -1, indicating that the Windows command could not be issued.

In the last post we scheduled 24-hour collections of performance metrics using pButtons. In this post we are going to be looking at a few of the key metrics that are being collected and how they relate to the underlying system hardware. We will also start to explore the relationship between Caché (or any of the InterSystems Data Platforms) metrics and system metrics. And how you can use these metrics to understand the daily beat rate of your systems and diagnose performance problems.

This is the first article of a series diving into visualization tools and analysis of time series data. Obviously we are most interested in looking at performance related data we can gather from the Caché family of products. However, as we'll see down the road, we are absolutely not limited to that. For now we are exploring python and the libraries/tools available within that ecosystem.

Windows 7 and some other Microsoft Operating Systems can shutdown too fast for large applications, such as a Cache instance with a large amount of data and changes, to close gracefully.

It is often necessary to sort the results of a query on a string field containing a combination of alphabetic and numeric characters. In cases like this the default string collation may not always return the data in the expected sequence.

An example of this may be where a select from Samples.Person should order the results by the street address, but firstly ordered by the street number part as numeric, and then by the street name.

The default query will return the results as follows:

If you have worked with Caché Objects,
You know already all about Relationships (one:many , parent:child) ...
But you will not find a word on many:many relationships in the docs.

But I met the question quite often from new adopters of Caché objects:
"Is it possible to implement many:many relationships ?" YES - of course !