InterSystems FAQ rubric

Temporary globals stored in the IRISTEMP/CACHETEMP databases are used when a process does not need to store data indefinitely, but requires the powerful performance of globals. The IRISTEMP/CACHETEMP databases are not journaled, so using temporary globals does not create journal files.

The system uses the IRISTEMP/CACHETEMP databases for temporary storage and are available to users for the same.

InterSystems IRIS currently limits classes to 999 properties.

But what to do if you need to store more data per object?

This article would answer this question (with the additional cameo of Community Python Gateway and how you can transfer wide datasets into Python).

The answer is very simple actually - InterSystems IRIS currently limits classes to 999 properties, but not to 999 primitives. The property in InterSystems IRIS can be an object with 999 properties and so on - the limit can be easily disregarded.

It's well-known that namespace global mapping helps us to write code independent on database storage details (Caché instance name, directory path). But sometimes we can face problems accessing an unsubscripted global which has subscript level mapping (SLM) defined. Most of such cases are evident and associated with administrative tasks that should be done on database level, but some of them can confuse even an experienced developer. Just to start:

Process-private Globals can be used as a data global in storage definition. That way, each process can have its own objects for the class with ppg storage. For example lets define a pool, which can:

• add elements to a pool (ignoring duplicates)
• check if an element exists in the pool

Here's the class:

InterSystems FAQ rubric

I'm participating in the Developing with InterSystems Objects and SQL with Joel Solon. The course is very nice and I will share with you some tips I got during the training. Tips presented in the day 4:

Introduction

In the first article in this series, we’ll take a look at the entity–attribute–value (EAV) model in relational databases to see how it’s used and what it’s good for. Then we'll compare the EAV model concepts to globals.

In my article I described the work using iris.gref .
As the official documetation is rather slim on the subject it was necessary to dig into it.
Using the power of Python I was able to detect what I needed but was hidden.
I decided to share this with you. pydoc did the magic.

Hi Community,

This article describes the small ZPM module global-archiver.
The goal is to move a part of a global from a database to another database.

Why this package?

A typical use case is read-only data sequentially added to your database that you can never delete.
For example:

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. Academic refers to the structure created.
Each logical node of the Global is presented separately with all its descendants.
Even if they don't contain any stored data.

Hi Community,


In this article, I will introduce Python Flask Web Framework. Together we will create a minimal web application to connect to IRIS and get data from it.

Below you can find the steps we will need to follow:

• Step 1 : Introduction to Python Flask Web Framework
• Step 2 : Installation of Flask module
• Step 3 : Creation of web application using Flask
• Step 4 : Use of HTML Templates
• Step 5 : Installation of IRIS Python Native module
• Step 6 : Establishment of a connection with IRIS
• Step 7 : Transferring data from IRIS to Flask and displaying it

So Let's start with step 1

Step1-Introduction to Python Flask Web Framework

Flask is a small and lightweight Python web framework that provides useful tools and features that make creating web applications in Python easier. It gives developers flexibility and is a more accessible framework for new developers since it allows to build a web application quickly using only a single Python file. Flask is also extensible and doesn’t requires a particular directory structure or complicated boilerplate code before getting started.

For more details please view Flask Documentations

The power of the presented tool becomes visible with a large JSON file.
The example I have prepared is composed of the daily results of AoC 2023.
Anonymization makes it usable for demo purposes.

I've posted this video on YouTube:

https://www.youtube.com/watch?v=gHUifpuav9I

Expand this article for some background to what it shows and what it's all about...

This package offers a utility to export an XLarge Global into a JSON object file and to show
or import it again. In a previous example, this all was processed in memory. But if this is a
large Global you may either experience <MAXSTRING> or an <STORE> error
if the generated JSON structure exceeds available memory.