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#SQL

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SQL is a standard language for storing, manipulating and retrieving data in relational databases.

If you're migrating from Oracle to InterSystems IRIS—like many of my customers—you may run into Oracle-specific SQL patterns that need translation.

Take this example:

SELECT (TO_DATE('2023-05-12','YYYY-MM-DD') - LEVEL + 1) AS gap_date
FROM dual
CONNECT BY LEVEL <= (TO_DATE('2023-05-12','YYYY-MM-DD') - TO_DATE('2023-05-02','YYYY-MM-DD') + 1);

In Oracle:

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With the release of InterSystems IRIS Cloud SQL, we're getting more frequent questions about how to establish secure connections over JDBC and other driver technologies. While we have nice summary and detailed documentation on the driver technologies themselves, our documentation does not go as far to describe individual client tools, such as our personal favourite DBeaver. In this article, we'll describe the steps to create a secure connection from DBeaver to your Cloud SQL deployment.

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If you are a customer of the new InterSystems IRIS® Cloud SQL and InterSystems IRIS® Cloud IntegratedML® cloud offerings and want access to the metrics of your deployments and send them to your own Observability platform, here is a quick and dirty way to get it done by sending the metrics to Google Cloud Platform Monitoring (formerly StackDriver).

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Migrating from Oracle, MSSQL, or other purely relational database systems to a multimodel InterSystems IRIS is a strategic decision that requires careful planning and execution. While this transition offers significant benefits, including enhanced performance, scalability, and support for modern architectures, it also comes with challenges. In this article I will highlight some of the considerations connected to coding to ensure a successful migration. I will leave everything connected to an actual migration of structures and data outside the scope of this article.


First, when you're considering migrating to a different database system, you need to understand your business logic, whether it's on the side of the application (application server) or the database server. Basically, where do you have your SQL statements that you will need to potentially rewrite?

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When working with InterSystems IRIS, database developers and architects often face a critical decision: whether to use Dynamic SQL or Embedded SQL for querying and updating data. Both methods have their unique strengths and use cases, but understanding their performance implications is essential to making the right choice. Response time, a key metric in evaluating application performance, can vary significantly depending on the SQL approach used. Dynamic SQL offers flexibility, as queries can be constructed and executed at runtime, making it ideal for scenarios with unpredictable or highly variable query needs. Conversely, Embedded SQL emphasizes stability and efficiency by integrating SQL code directly into application logic, offering optimized response times for predefined query patterns.

In this article, I will explore the response times when using these two types of SQL and how they depend on different class structures and usage of parameters. So to do this, I'm going to use the following classes from the diagram:

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When using standard SQL or the object layer in InterSystems IRIS, metadata consistency is usually maintained through built-in validation and type enforcement. However, legacy systems that bypass these layers—directly accessing globals—can introduce subtle and serious inconsistencies.

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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.

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Prompt

Firstly, we need to understand what prompt words are and what their functions are.

Prompt Engineering

Hint word engineering is a method specifically designed for optimizing language models.
Its goal is to guide these models to generate more accurate and targeted output text by designing and adjusting the input prompt words.

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Article
· Mar 10 5m read
FHIR SQL Builder: step by step

The FHIR standard establishes a powerful but flexible data model that can smoothly adapt to the complexities of operational healthcare data management. This flexibility comes at the cost of a data model with many tables and relationships, even for simple data such as the patient's record of telephone numbers, addresses, and emails. It would easily require querying 4 different tables. However, FHIR SQL Builder eliminates this problem, allowing you to create visual projections (mappings) in web wizards.

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After so many years of waiting, we finally got an official driver available on Pypi

Additionally, found JDBC driver finally available on Maven already for 3 months, and .Net driver on Nuget more than a month.

As an author of so many implementations of IRIS support for various Python libraries, I wanted to check it. Implementation of DB-API means that it should be replaceable and at least functions defined in the standard. The only difference should be in SQL.

And the beauty of using already existing libraries, that they already implemented other databases by using DB-API standard, and these libraries already expect how driver should work.

I decided to test InterSystems official driver by implementing its support in SQLAlchemy-iris library.

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We recently changed the 'UserID" property in a "User" class from type of %String to be %Library.Username. This is for better consistency across our codebase regarding MAXLEN limit.

%Library.Username is a system wrapper datatype which extends %String and has a MAXLEN of 160. This change should have minimal/no impact on code behavior. However, we found that some SQL query cannot return expected rows after the change. Query will return empty values even if the entry is in the table.

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Unlike the movie mentioned in the image (for those who don't know, Matrix, 1999), the choice between Dynamic SQL and Embedded SQL is not a choice between truth and fantasy, but it is still a decision to be made. Below, I will try to make your choice easier.

If your need is interactions between the client and the application (and consequently the database), Dynamic SQL may be more appropriate, as it "adapts" very easily to these query changes. However, this dynamism has a cost: with each new query, it is remodeled, which can have a higher cost to execute. Below is a simple example of a Python code snippet.

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Using SQL Gateway with Python, Vector Search, and Interoperability in InterSystems Iris

Part 3 – REST and Interoperability

Now that we have finished the configuration of the SQL Gateway and we have been able to access the data from the external database via python, and we have set up our vectorized base, we can perform some queries. For this in this part of the article we will use an application developed with CSP, HTML and Javascript that will access an integration in Iris, which then performs the search for data similarity, sends it to LLM and finally returns the generated SQL. The CSP page calls an API in Iris that receives the data to be used in the query, calling the integration. For more information about REST in the Iris see the documentation available at https://docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls...

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Hi Community,

In this article, we will explore the concepts of Dynamic SQL and Embedded SQL within the context of InterSystems IRIS, provide practical examples, and examine their differences to help you understand how to leverage them in your applications.

InterSystems SQL provides a full set of standard relational features, including the ability to define table schema, execute queries, and define and execute stored procedures. You can execute InterSystems SQL interactively from the Management Portal or programmatically using a SQL shell interface. Embedded SQL enables you to embed SQL statements in your ObjectScript code, while Dynamic SQL enables you to execute dynamic SQL statements from ObjectScript at runtime. While static SQL queries offer predictable performance, dynamic and embedded SQL offer flexibility and integration, respectively.

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Introduction

To achieve optimized AI performance, robust explainability, adaptability, and efficiency in healthcare solutions, InterSystems IRIS serves as the core foundation for a project within the x-rAI multi-agentic framework. This article provides an in-depth look at how InterSystems IRIS empowers the development of a real-time health data analytics platform, enabling advanced analytics and actionable insights. The solution leverages the strengths of InterSystems IRIS, including dynamic SQL, native vector search capabilities, distributed caching (ECP), and FHIR interoperability. This innovative approach directly aligns with the contest themes of "Using Dynamic SQL & Embedded SQL," "GenAI, Vector Search," and "FHIR, EHR," showcasing a practical application of InterSystems IRIS in a critical healthcare context.

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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:

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High-Performance Message Searching in Health Connect

The Problem

Have you ever tried to do a search in Message Viewer on a busy interface and had the query time out? This can become quite a problem as the amount of data increases. For context, the instance of Health Connect I am working with does roughly 155 million Message Headers per day with 21 day message retention. To try and help with search performance, we extended the built-in SearchTable with commonly used fields in hopes that indexing these fields would result in faster query times. Despite this, we still couldn't get some of these queries to finish at all.

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In the world of APIs, REST is very extended. But what happens when you need more flexibility in your data-fetching strategies? For instance letting the client to choose what fields is going to receive. Enter GraphQL, a query language for your APIs that provides a flexible alternative to REST.

In this post, we will:

  • Compare REST and GraphQL.
  • Dive into the basics of GraphQL: Queries, Mutations, and HTTP.
  • Build a simple GraphQL server implementation using Graphene, SQLAlchemy, and Flask over data in InterSystems IRIS.
  • Explore how to deploy your GraphQL server as a WSGI application in IRIS.
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So, you checked your server and saw that IRISTEMP is growing too much. There's no need to panic. Let’s investigate the issue before your storage runs out.

Step 1: Confirm the IRISTEMP Growth Issue

Before assuming IRISTEMP is the problem, let’s check its actual size.

Check the Free Space

Run the following command in the IRIS terminal:

%SYS>do ^%FREECNT

When prompted, enter:

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Using SQL Gateway with Python, Vector Search, and Interoperability in InterSystems Iris

Part 2 – Python and Vector Search

Since we have access to the data from our external table, we can use everything that Iris has to offer with this data. Let's, for example, read the data from our external table and generate a polynomial regression with it.

For more information on using python with Iris, see the documentation available at https://docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls?KEY=AFL_epython

Let's now consume the data from the external database to calculate a polynomial regression. To do this, we will use a python code to run a SQL that will read our MySQL table and turn it into a pandas dataframe:

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Using SQL Gateway with Python, Vector Search, and Interoperability in InterSystems Iris

Part 1 - SQL Gateway

Hello

In this article we will look at the use of SQL Gateway in Iris. SQL Gateway allows Iris to have access to tables from other (external) database via ODBC or JDBC. We can access Tables or Views from various databases, such as Oracle, PostgreSQL, SQL Server, MySQL and others.

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In this article, exceptions are covered.

Working with Exceptions

Instead of returning a %Status response, you can raise and throw an Exception. You are then responsible for catching the exception and validating it. IRIS provides five main classes to handle exceptions effectively. Additionally, you can create custom exception class definition based on your needs.

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From the previous article, we identified some issues when working with JSON in SQL.

IRIS offers a dedicated feature for handling JSON documents, called DocDB.

InterSystems IRIS® data platform DocDB is a facility for storing and retrieving database data. It is compatible with, but separate from, traditional SQL table and field (class and property) data storage and retrieval. It is based on JSON (JavaScript Object Notation) which provides support for web-based data exchange. InterSystems IRIS provides support for developing DocDB databases and applications in REST and in ObjectScript, as well as providing SQL support for creating or querying DocDB data.

By its nature, InterSystems IRIS Document Database is a schema-less data structure. That means that each document has its own structure, which may differ from other documents in the same database. This has several benefits when compared with SQL, which requires a pre-defined data structure.

The word “document” is used here as a specific industry-wide technical term, as a dynamic data storage structure. “Document”, as used in DocDB, should not be confused with a text document, or with documentation.

Let's explore how DocDB can help store JSON in the database and integrate it into projects that rely solely on xDBC protocols.

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Article
· Feb 3 3m read
SQL Host Variables missing ?

Host Variables are a rather common programming feature in many implementations of SQL.
A recent question in DC made me aware that in IRIS, Caché, Ensemble, ...
host variables just exist within embedded SQL

> You can supply host variables for Embedded SQL queries only. <

Related examples are included in the available Documentation

This is a description for a workaround if you don't / can't use embedded SQL.

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Article
· Jan 22 4m read
JSON Support in IRIS SQL

While working on getting JSON support for some Python libraries, I discovered some capabilities IRIS provided.

  • JSON_OBJECT - A conversion function that returns data as a JSON object.
  • JSON_ARRAY - A conversion function that returns data as a JSON array.
  • IS JSON - Determines if a data value is in JSON format.
  • JSON_TABLE function returns a table that can be used in a SQL query by mapping JSON.
  • JSONPath support - is a query language for querying values in JSON.

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Not sure there are many that connect to MS SQL to execute queries, stored procedures, etc, but our Healthsystem has many different MS SQL based databases we use within the Interoperability environment for various reasons.

With the push to moving from on-prem to the Cloud we ran into some difficulties with our SQL Gateway connections and knowing how to config them to use Microsoft Entra for Active Directory Authentication.

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