Article
· Jul 30, 2022 5m read
Introduction to Django part 3

Continuing to observe the possibilities of Django, and usage with IRIS. The first we have looked how to define models and connect to tables already existing in IRIS, than we extended embedded Django Administration portal, with an ability to see what data we have in that models, with filters, editing and even pagination.

Time to go to real action, now we a going to create some REST API, on Django, based on the same data, we used before from the package posts-and-tags.

To do so, we will use Django REST Framework

Django REST Framework

Django REST framework is a powerful and flexible toolkit for building Web APIs.

Some reasons you might want to use REST framework:

  • The Web browsable API is a huge usability win for your developers.
  • Authentication policies including packages for OAuth1a and OAuth2.
  • Serialization that supports both ORM and non-ORM data sources.
  • Customizable all the way down - just use regular function-based views if you don't need the more powerful features.
  • Extensive documentation, and great community support.
  • Used and trusted by internationally recognised companies including Mozilla, Red Hat, Heroku, and Eventbrite.

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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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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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The data model of your solution based on InterSystems platforms constantly changes over time. But what do you do with the data that was entered before? Back then, the data was valid, but what’s happening to it now after a number of data model changes? The answer to this question can be provided by the IDP DV tool that checks the property data of persistent and serial classes according to the types of these properties. In case any discrepancies are found, the tool generates a detailed error report for the user.

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Hi Developers!

As you know the concept of ObjectScript Package Manager consists of ZPM client - client application for IRIS which helps you to install packages from the registry. And the code which works "on the other side" is ZPM Registry - server which hosts packages and exposes API to submit, list and install it. Now when you install the ZPM client it installs packages from community package registry, which si hosted on pm.community.intersystems.com

But what if you want your own registry? E.g. you produce different software packages for your clients and you want to distribute it via private registry? Also, you may want to use your own registry to deploy solutions with different combinations of packages.

Is it possible? The answer is YES! You can have it if you deploy ZPM registry on your server with InterSystems IRIS.

To make it happen you would need to set up your own registry server.

How to do that?

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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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Hi developers!

Observing the avalanche of AI-driven and vibe-coding developer tools that have been appearing lately almost every month with more and more exciting dev features, I was puzzled whether it is possible to leverage it with InterSystems IRIS. At least to build a frontend. And the answer - yes! At least with the approach I followed.

Here is my recipe to prompt the UI vs InterSystems IRIS Backend:

  1. Have the REST API on the IRIS side, which reflects some Open API (swagger) spec.
  2. Generate the UI with any vibe-coding tool (e.g., Lovable) and point the UI to the REST API endpoint.
  3. Profit!

Here is the result of my own exercise - a 100% prompted UI vs IRIS REST API that allows to list, create, update delete entries of a persistent class (Open Exchange, frontend source, video):

What is the recipe in detail?

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Article
· Jul 4, 2019 1m read
Install EnsDemo on IRIS

Has you may know, EnsDemo from Ensemble are not available anymore on IRIS.

This is a good thing, Iris is cloud oriented, it must be light, fast. Now the new way of sharing samples or modules is through git, continuous integration and OpenExchange.

But, in some cases you want to go back to your good old samples from EnsDemo to get inspiration or best practices.

Good news, there is a git for that :

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Article
· Dec 27, 2020 2m read
Secure IRIS Digital Services

The InterSystems IRIS has two major paths to a digital service: API/Web Service into Interoperability module and multimodel Database/Analytics. Each of them has your security configuration.

To do API security you apply an OAuth or JWT plug-in to the API endpoint. So in the Admin Portal, API producer and consumers get the keys to authenticate the API and consume it. The Admin Portal allows you configure RBAC policies too.

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Article
· Mar 27, 2021 3m read
IRIS easy ECP workbench

Testing ECP-based applications often take quite some effort for setup and preparation.
I have created a Docker-based workbench that allows you to have it quick at hands.
And if you crash it? You just give your containers a fresh start.
The whole setup runs code-based during the start-up of your instance.
In that sense, it is also a portable coding example using ZPM and the objectscript-docker-template

see Video

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Article
· Feb 27, 2022 2m read
Dash-Python-IRIS

We are happy to share interesting information with you, as well as tell you why Python is good, where it is used.

Among the most used libraries are NumPy and Pandas. NumPy (Numerical Python) is used to sort large datasets. It simplifies mathematical operations and their vectorization on arrays. Pandas offers two data structures: Series (a list of elements) and Data Frames (a table with multiple columns). This library converts data into a Data Frame, allowing you to remove and add new columns, as well as perform various operations.

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Hello everyone, I’m a French student that just arrived in Prague for an academical exchange for my fifth year of engineering school and here is my participation in the interop contest.

I hadn’t much time to code since I was moving from France to Prague and I’m participating alone, so I decided to make a project that’s more like a template rather than an application.

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Article
· Jun 22, 2023 1m read
Countermeasures against SQL injection

InterSystems FAQ rubric

Countermeasures against SQL injection have been published on various websites, but we believe that it is possible to prevent SQL injection in applications using InterSystems SQL as well as other RDBMS by implementing these countermeasures appropriately. In addition, InterSystems Data Platform (hereinafter referred to as IRIS) incorporates several measures that make SQL injection more difficult than general RDBMS.

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Article
· Nov 20, 2023 3m read
Parquet files and InterSystems IRIS

In the world of Big Data, selecting the right file format is crucial for efficient data storage, processing, and analysis. With the massive amount of data generated every day, choosing the appropriate format can greatly impact the speed, cost, and accuracy of data processing tasks. There are several file formats available, each with its own set of advantages and disadvantages, making the decision of which one to use complex. Some of the popular Big Data file formats include CSV, JSON, Avro, ORC, and Parquet.

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Hi, Community!

Since this article is an overview of Flask Login, let's begin with Flask Introduction!

What is Flask?

In the realm of web development, Python has emerged as a formidable force, offering its versatility and robustness to create dynamic and scalable applications. For that reason, tools and services compatible with this language are in demand these days. Flask is a lightweight and easy-to-use web framework for Python. It stands out as a lightweight and user-friendly option. Its simplicity and flexibility have made it a popular choice for developers, particularly for creating smaller-scale applications. It is based on the Werkzeug toolkit and provides a simple but powerful API for building web applications.
Unlike its full-stack counterparts, Flask provides a core set of features, focusing on URL routing, template rendering, and request handling. This minimalist approach makes Flask lightweight and easy to learn, allowing developers to build web applications quickly and without the burden of unnecessary complexity.

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