Introduction

Hello! In this article, I will be discussing database degrade, a type of data integrity issue one can face when using IRIS. First, I will be going over a review of the structure of IRIS databases. I'll then discuss how database degrade can manifest and common causes of degrade issues. I'll then conclude with general tips we give our customers about how to prevent or prepare for database degrade issues.

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...

Inspired by a Question from @Evgeny Shvarov and a Reply from @Ashok Kumar T
I have created a base for Global download as XML file

This is the related article to the package in Java Contest.

I decided to present a CRUD++ Global Editor based on IRIS Native API for Java.
++ because it’s a little bit more than just Create, Read, Update, Delete
Visualization of a global is always important to review results immediately.

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:

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.

Hey developers!

Sometimes we need to insert or refer to the data of classes directly in globals.

And maybe a lot of you expect that data structure of global with records is:

^Sample.Person(Id)=$listbuild("",col1,col2,...,coln).

And this article is a heads up, that this is not always true, don't expect it as granted!

You may have seen my series of articles on GlobalToJSON closing with Embedded Python.
And to me, Embedded means inside a Class and not PEX or other external variants.
The first variants showed that ePy is not an ultimate requirement. ISOS/COS may do it as well.
Here I try to position ePy inside IRIS.

This example demonstrates the difference you may experience when you write to
Gllobals directly from Embedded Python compared to native ObjectScript.

To make this demo useful I start 2 background jobs that simply write sequentially
to a dedicated global. A common control method signals for a synchronous start.
Similar a common stop & view interrupts data feeding.

That's the principle process:

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.

Motivation

This project was thought of when I was thinking of how to let Python code deal naturally with the scalable storage and efficient retrieving mechanism given by IRIS globals, through Embedded Python.

My initial idea was to create a kind of Python dictionary implementation using globals, but soon I realized that I should deal with object abstraction first.

So, I started creating some Python classes that could wrap Python objects, storing and retrieving their data in globals, i.e., serializing and deserializing Python objects in IRIS globals.

JSON is a lightweight data interchange format for representing and exchanging data between a server and a web application. Its popularity has led to its widespread use in applications based on InterSystems technology, as well as demand for a converter that transforms globals to JSON and back. Therefore, @Evgeny Shvarov suggested developing Global->JSON->Global converter. Implementing such a converter is important to ensure interoperability, simplify data exchange, support web services, and provide a standardized approach to data representation across different software ecosystems. This article, the second in the "Implemented Ideas" series, focuses on several projects created by the legend of InterSystems Developer Community @Robert Cemper that deal with this task:

• GlobalToJSON-Compact
• GlobalToJSON-Efficient
• GlobalToJSON-Academic
• GlobalToJSON-embeddedPython
• GlobalToJSON-ePython-pure
• GlobalToJSON-XLA
• JSONfile-to-Global.

VSCode is the most used IDE in the world. It is strategic have more extensions for VSCode for InterSystems technologies to keep increasing the developer community.

My new app IRIS VSCode Global Editor is an excellent sample to learn how to create extensions to IRIS. You can check it on https://openexchange.intersystems.com/package/IRIS-Global-VSCode-Editor.

Parallel query hinting boosts certain query performances on multi-processor systems via parallel processing. The SQL optimizer determines when this is beneficial. On single-processor systems, this hint has no effect.

Parallel processing can be managed by:

1. Setting the auto parallel option system-wide.
2. Using the %PARALLEL keyword in the FROM clause of specific queries.

%PARALLEL is ignored when it applied to: