Myself and the other Technology Architects often have to explain to customers and vendors Caché IO requirements and the way that Caché applications will use storage systems. The following tables are useful when explaining typical Caché IO profile and requirements for a transactional database application with customers and vendors. The original tables were created by Mark Bolinsky.

In future posts I will be discussing more about storage IO so am also posting these tables now as a reference for those articles.

When using Studio, ODBC or a terminal connection to Caché or Ensemble, you may have wondered how to secure the connection. One option is to add TLS (aka SSL) to your connection. The Caché client applications - TELNET, ODBC and Studio - all understand how to add TLS to the connection. They just need to be configured to do it.

Configuring these clients is easier in 2015.1 and later. I'm going to be discussing this new method. If you're already using the old, legacy method, it will continue to work, but I would recommend you consider switching to the new one.

NB. Please be advised that PKI is not intended to produce certificates for secure production systems. You should make alternate arrangements to create certificates for your productions.
NB. PKI is deprecated as of IRIS 2024.1: documentation and announcement.

This article is a simple quick starter (what I did was) with SqlDatabaseChain.

Hope this ignites some interest.

Many thanks to:

sqlalchemy-iris author @Dmitry Maslennikov

Your project made this possible today.

The article script uses openai API so caution not to share table information and records externally, that you didn't intend to.

A local model could be plugged in , instead if needed.

I' have done some tests with Caché and Apache Zeppelin. I want to share my experince to use both systems together. I'll try to describe all steps that are required to config Zeppelin to connect to Caché.

In my previous article, we reviewed possible use-cases for macros, so let’s now proceed to a more comprehensive example of macros usability. In this article we will design and build a logging system.

Logging system

Logging system is a useful tool for monitoring the work of an application that saves a lot of time during debugging and monitoring. Our system would consist of two parts:

• Storage class (for log records)
• Set of macros that automatically add a new record to the log

Swift-FHIR-Iris

iOS app to export HealthKit data to InterSystems IRIS for Health (or any FHIR repository)

In recent versions of IRIS, a powerful new data loading command has been introduced to SQL: LOAD DATA. This feature has been highly optimized to import data into IRIS extremely fast, allowing hundreds of gigabytes of data to be inserted in seconds instead of hours or days.

This is a very exciting improvement. However, a big problem in the data loading experience still exists. Namely, the time and hassle it takes to:

This article is translation of one from Habrahabr InterSystems blog (Russian).
The original post can be found here: https://habrahabr.ru/company/intersystems/blog/251611/

In this article, I would like to talk about the spec-first approach to REST API development.

While traditional code-first REST API development goes like this:

• Writing code
• REST-enabling it
• Documenting it (as a REST API)

Spec-first follows the same steps but reverse. We start with a spec, also doubling as documentation, generate a boilerplate REST app from that and finally write some business logic.

This is advantageous because:

• You always have relevant and useful documentation for external or frontend developers who want to use your REST API
• Specification created in OAS (Swagger) can be imported into a variety of tools allowing editing, client generation, API Management, Unit Testing and automation or simplification of many other tasks
• Improved API architecture. In code-first approach, API is developed method by method so a developer can easily lose track of the overall API architecture, however with the spec-first developer is forced to interact with an API from the position if API consumer which usually helps with designing cleaner API architecture
• Faster development - as all boilerplate code is automatically generated you won't have to write it, all that's left is developing business logic.
• Faster feedback loops - consumers can get a view of the API immediately and they can easier offer suggestions simply by modifying the spec

Let's develop our API in a spec-first approach!

I wanted to write it as a comment to article of @Evgeny Shvarov . But it happens to be so long, so, decided to post it separately.

I would like to add a bit of clarification about how docker uses disk space and how to clean it. I use macOS, so, everything below, is mostly for macOS, but docker commands suit any platform.

Managed File Transfer (MFT) feature of InterSystems IRIS enables easy inclusion of a third-party file transfer service directly into an InterSystems IRIS production. Currently, DropBox, Box, and Kiteworks cloud disks are available.

In this article, I'd like to describe how to add more cloud storage platforms.

Here's what we're going to talk about:

• What is MFT
• Reference: Dropbox
  • Connection
  • Interoperability
  • Direct access
• Interfaces you need to implement
  • Connection
  • Logic
• Installation

Hi developers!

Today I would like to address a subject that has given me a hard time. I am sure this must have been the case for quite a number of you already (so-called “the bottleneck”). Since this is a broad topic, this article will only focus on identifying incoming HTTP requests that could be causing slowness issues. I will also provide you with a small tool I have developed to help identify them.

Our software is becoming more and more complex, processing a large number of requests from different sources, be it front-end or third-party back-end applications. To ensure optimal performance, it is essential to have a logging system capable of taking a few key measurements, such as the response time, the number of global references and the number of lines of code executed for each HTTP response. As part of my work, I get involved in the development of EMR software as well as incident analysis. Since user load comes mostly from HTTP requests (REST API or CSP application), the need to have this type of measurement when generalized slowness issues occur has become obvious.

This is a posting about a particular feature of Caché which I find useful but is probably not well known or used. I am referring to the feature of Language Extensions.

This feature allows you to extend the commands, special variables and functions available in Caché Object Script with commands, special variables and functions of your own. This functionality also applies to other languages the Caché supports at the server, including Caché Basic and Multivalue Basic.

Why would I need or want to add new commands ?