New post

#SQL

13

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

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.

68 16
4 448

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.
Open Exchange app
24 3
1 338

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.

18 1
1 134

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.

17 0
6 112

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.

Open Exchange app
14 7
3 220
Article
· Sep 11, 2024 9m read
Dates with InterSystems

Do not let the title of this article confuse you; we are not planning to take the InterSystems staff out to a fine Italian restaurant. Instead, this article will cover the principles of working with date and time data types in IRIS. When we use these data types, we should be aware of three different conversion issues:

  1. Converting between internal and ODBC formats.
  2. Converting between local time, UTC, and Posix time.
  3. Converting to and from various date display formats.

14 4
5 548

This article describes a significant enhancement of how InterSystems IRIS deals with table statistics, a crucial element for IRIS SQL processing, in the 2025.2 release. We'll start with a brief refresher on what table statistics are, how they are used, and why we needed this enhancement. Then, we'll dive into the details of the new infrastructure for collecting and saving table statistics, after which we'll zoom in onto what the change means in practice for your applications. We'll end with a few additional notes on patterns enabled by the new model, and look forward to the follow-on phases of this initial delivery.

11 5
3 116
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.

Open Exchange app
9 0
0 131

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:

9 0
4 132

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.

Open Exchange app
8 0
5 59

sql-embedding cover

InterSystems IRIS 2024 recently introduced the vector types.
This addition empowers developers to work with vector search, enabling efficient similarity searches, clustering, and a range of other applications.
In this article, we will delve into the intricacies of vector types, explore their applications, and provide practical examples to guide your implementation.

Open Exchange app
8 2
2 166
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.

8 2
2 192

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...

8 1
0 90

Hi,

We’re launching an Early Access Program for an upcoming Table Partitioning feature that will help IRIS customers manage very large tables, and distribute row data and associated indices across databases and storage tiers. Table Partitioning cuts deep into the core of IRIS relational data management, so we want to make sure we get things right through working with a few engaged customers who can provide feedback on the initial deliverables, and fine-tune as needed.

7 11
0 206

Here at InterSystems, we often deal with massive datasets of structured data. It’s not uncommon to see customers with tables spanning >100 fields and >1 billion rows, each table totaling hundred of GB of data. Now imagine joining two or three of these tables together, with a schema that wasn’t optimized for this specific use case. Just for fun, let’s say you have 10 years worth of EMR data from 20 different hospitals across your state, and you’ve been tasked with finding….

7 1
3 170

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?

6 1
0 132

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.

6 5
0 181

The rise of Big Data projects, real-time self-service analytics, online query services, and social networks, among others, have enabled scenarios for massive and high-performance data queries. In response to this challenge, MPP (massively parallel processing database) technology was created, and it quickly established itself. Among the open-source MPP options, Presto (https://prestodb.io/) is the best-known option. It originated in Facebook and was utilized for data analytics, but later became open-sourced.

Open Exchange app
6 0
3 234

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:

6 3
0 147

image

Hi Community,

In this article, I will introduce my application iris-RAG-Gen .

Iris-RAG-Gen is a generative AI Retrieval-Augmented Generation (RAG) application that leverages the functionality of IRIS Vector Search to personalize ChatGPT with the help of the Streamlit web framework, LangChain, and OpenAI. The application uses IRIS as a vector store.

Application Features

  • Ingest Documents (PDF or TXT) into IRIS
  • Chat with the selected Ingested document
  • Delete Ingested Documents
  • OpenAI ChatGPT

Open Exchange app
5 1
0 285

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:

4 3
0 185