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:

The best way to list, edit, save and delete globals is using an IDE. Now, it is possible if you use VSCode. It is also possible to save globals using yaml files. Perform the following steps:

1. Get an InterSystems IRIS instance and install the application iris-global-yaml:

zpm:USER>install iris-global-yaml

2. If you just to want an InterSystems IRIS trial for tests git clone and run on docker:

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.

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.

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 question originally appeared in the comments of the post: Download globals from a particular server

InterSystems FAQ rubric

It can be obtained by using the Size query of the system-provided %SYS.GlobalQuery 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.

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

My major interest is Working with Globals in Embedded Python.
So I checked the available official documentation.

#1 Introduction to Globals
an attempt of a generic description of what a global is. Pointing to

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.

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:

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:

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.