Contestant

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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Contestant

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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Contestant

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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Contestant

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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Contestant

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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Contestant
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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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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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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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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In this tutorial, I will discuss how can you connect your IRIS data platform to sql server db .

Prereq:

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SQL injection remains one of the most critical vulnerabilities in database-driven applications, allowing attackers to manipulate queries and potentially access or compromise sensitive data. In InterSystems IRIS, developers have access to both Dynamic SQL and Embedded SQL, each with distinct characteristics. Understanding how to use them securely is essential for preventing SQL injection.

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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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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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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….

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Like many others probably find themselves, we were stuck doing live data mapping in our Interface Engine that we really didn't want to do, but had no good alternative choice. We want to only keep mappings for as long as possibly needed and then purge expired rows based upon a TTL value. We actually had 4 use cases for it ourselves before we built this. Use cases:

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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.

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InterSystems FAQ rubric

By default, the order of columns in a table is determined automatically by the system. To change the order, explicitly set the order for each property using the property keyword SqlColumnNumber when defining the class.

Example:

Property Name As %String [SqlColumnNumber = 2];

Please see the documentation below.

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In the modern world, the most valuable asset for companies is their data. Everything from business processes and applications to transactions is based on data which defines the success of the organization's operations, analysis, and decisions. In this scenario, the data structures need to be ready for frequent changes, yet in a managed and governed way. Otherwise, we will inevitably lose money, time, and quality of corporate solutions.

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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.

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Article
· Sep 27, 2024 4m read
Inside Database Management Tool

In this article, we’ll dive into the inner workings of the database management tool, exploring the architecture and technologies that power it. Understanding how the application functions behind the scenes will give you insight into its design, how it manages databases, tables, and how the API interacts with data.

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Problems with Strings

I am accessing IRIS databases with JDBC (or ODBC) using Python. I want to fetch the data into a pandas dataframe to manipulate the data and create charts from it. I ran into a problem with string handling while using JDBC. This post is to help if anyone else has the same issues. Or, if there is an easier way to solve this, let me know in the comments!

I am using OSX, so I am unsure how unique my problem is. I am using Jupyter Notebooks, although the code would generally be the same if you used any other Python program or framework.

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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.

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