#SQL

Nowadays so much noise around LLM, AI, and so on. Vector databases are kind of a part of it, and already many different realizations for the support in the world outside of IRIS.

Why Vector?

• Similarity Search: Vectors allow for efficient similarity search, such as finding the most similar items or documents in a dataset. Traditional relational databases are designed for exact match searches, which are not suitable for tasks like image or text similarity search.
• Flexibility: Vector representations are versatile and can be derived from various data types, such as text (via embeddings like Word2Vec, BERT), images (via deep learning models), and more.
• Cross-Modal Searches: Vectors enable searching across different data modalities. For instance, given a vector representation of an image, one can search for similar images or related texts in a multimodal database.

And many other reasons.

So, for this pyhon contest, I decided to try to implement this support. And unfortunately I did not manage to finish it in time, below I'll explain why.

The related package avoids adding %JSONAdaptor to each class but uses instead
SQL functions JSON_OBJECT() to create my JSON objects. With this approach, you can
add JSON to any class - even deployed ones - without any need for change or recompiling.

The trigger was the Export of M:N relationships as JSON objects or arrays.

As Bill has mentioned earlier in his post, we have carefully reviewed the JSON capabilities and made some adjustments to ensure they deliver the best benefit to you. In this post, I am going to describe the modifications in more detail and provide guidance for you to understand the implication for your code base.

The Art of Mapping Globals to Classes (2 of 3)

If you are looking to breathe new life into an old MUMPS application follow these steps to map your globals to classes and expose all that beautiful data to Objects and SQL.

This example is going to cram in 4 or 5 different things beyond what was covered in Part 1

Introduction

This article is intended to be a simple tutorial on how to create ODBC connections and working with them, since I found starting with them a little bit confused, but I had amazing people to take my hand and walk me through it, and I think everyone deserves that kind of help too.
I'm going to divide each little part in sections, so feel free to jump to the one you feel the need to, although I recommend reading everything.

InterSystems is pleased to announce the General Availability of InterSystems IRIS Cloud SQL and InterSystems IRIS Cloud IntegratedML, two foundational services for developing cloud-native solutions powered by the proven, enterprise-class performance and reliability of InterSystems IRIS technology.

GmOwl is a solution that offers an organized and engaging learning platform. It was developed to cater to the increasing need, for learning tools providing a versatile quiz environment that meets users requirements.

The main objective of GmOwl is to deliver an user experience for individuals participating in quizzes while giving administrators comprehensive control, over content and user engagement.

GmOwl uses Java EE with MVC template, and the InterSystems IRIS database is used to store data. The InterSystems JDBC Driver is used to connect to the database.

Hi everyone.

This is a full example how to use a %ScrollableResultSet for results pagination using %DynamicQuery:SQL and build a JSON response including page details.

Feel free to use, share, comment or improve it.

Kind Regards

SQLTools is a Visual Studio Code (VS Code) Extension with over 3.5 million downloads that provides connections to many of the most used databases, including InterSystems IRIS, using drivers.

Intro

In a fast-paced digital era, effective communication is crucial. This article introduces a Java-based chat project, combining the strength of IRIS database and ChatGPT intelligence. Built on Java, it goes beyond real-time messaging, leveraging IRIS and ChatGPT for an enhanced chat experience. Also, the name of the project references the cultural classic - Star Wars.

Let me introduce my new project, which is irissqlcli, REPL (Read-Eval-Print Loop) for InterSystems IRIS SQL

• Syntax Highlighting
• Suggestions (tables, functions)
• 20+ output formats
• stdin support
• Output to files

Install it with pip

pip install irissqlcli

Or run with docker

docker run -it caretdev/irissqlcli irissqlcli iris://_SYSTEM:SYS@host.docker.internal:1972/USER

Connect to IRIS

$ irissqlcli iris://_SYSTEM@localhost:1972/USER -W
Password for _SYSTEM:
Server:  InterSystems IRIS Version 2022.3.0.606 xDBC Protocol Version 65
Version: 0.1.0
[SQL]_SYSTEM@localhost:USER> select $ZVERSION
+---------------------------------------------------------------------------------------------------------+
| Expression_1                                                                                            |
+---------------------------------------------------------------------------------------------------------+
| IRIS for UNIX (Ubuntu Server LTS for ARM64 Containers) 2022.3 (Build 606U) Mon Jan 30 2023 09:05:12 EST |
+---------------------------------------------------------------------------------------------------------+
1 row in set
Time: 0.063s
[SQL]_SYSTEM@localhost:USER> help
+----------+-------------------+------------------------------------------------------------+
| Command  | Shortcut          | Description                                                |
+----------+-------------------+------------------------------------------------------------+
| .exit    | \q                | Exit.                                                      |
| .mode    | \T                | Change the table format used to output results.            |
| .once    | \o [-o] filename  | Append next result to an output file (overwrite using -o). |
| .schemas | \ds               | List schemas.                                              |
| .tables  | \dt [schema]      | List tables.                                               |
| \e       | \e                | Edit command with editor (uses $EDITOR).                   |
| help     | \?                | Show this help.                                            |
| nopager  | \n                | Disable pager, print to stdout.                            |
| notee    | notee             | Stop writing results to an output file.                    |
| pager    | \P [command]      | Set PAGER. Print the query results via PAGER.              |
| prompt   | \R                | Change prompt format.                                      |
| quit     | \q                | Quit.                                                      |
| tee      | tee [-o] filename | Append all results to an output file (overwrite using -o). |
+----------+-------------------+------------------------------------------------------------+
Time: 0.012s
[SQL]_SYSTEM@localhost:USER>

What I find really useful about IRIS when teaching my subject of Postrelational databases is the fact that it is a multi model database. Which means that I can actually go into architecture and structure and all that only once but then show the usage of different models (like object, document, hierarchy) using the same language and approach. And it is not a huge leap to go from an object oriented programming language (like C#, Java etc) to an object oriented database.

However, along with advantages (which are many) come some drawbacks when we switch from object oriented model to relational. When I say that you can get access to the same data using different models I need to also explain how it is possible to work with lists and arrays from object model in relational table. With arrays it is very simple - by default they are represented as separate tables and that's the end of it. With lists - it's harder because by default it's a string. But one still wants to do something about it without damaging the structure and making this list unreadable in the object model.

So in this article I will showcase a couple of predicates and a function that are useful when working with lists, and not just as fields.

Since Caché 2017 the SQL engine has included new set of statistics. These record the number of times a query is executed and the time it takes to run.

Introduction

This is a simple tutorial on the quickest way I found to create a sample database for any purposes such as testing, making samples for tutorials, etc.

Creating a namespace

1. Open the terminal
2. Write the command "D $SYSTEM.SQL.Shell()"
3. Write "CREATE DATABASE " and the name you want for your namespace.

Now you have a new namespace in a faster way than creating it from the Management Portal - which of course offers way more configuration options.