InterSystems ObjectScript is a scripting language to operate with data using any data model of InterSystems Data Platform (Objects, Relational, Key-Value, Document, Globals) and to develop business logic for serverside applications on InterSystems Data Platform.
Currently, I'm working on a project that requires highly dynamic event management. In the context of the Java programming language, my first instinct should be to opt for the "Observer Pattern", which is an approach to managing interactions between objects by establishing a notification mechanism. It allows multiple observers to react to changes in the state of a subject autonomously, promoting code flexibility and modularity. If you are not familiar with this design pattern, check out Wikipedia to find more information about it.
While it's natural and commonly used in certain programming languages as Java and C++, in ObjectScript, it's quite a different story.
Based on the successful solution for my 2nd contribution to the Contest I used an adapted version for this package. And have some findings I'd like to share.
Multiple communication steps over CPIPE may take time. You won't recognize it on a fast machine. But a slower box with Windows + Docker Desktop + your browser (and more) is neither "Speedy Gonzales" nor a "Road Runner". 🙂
In this short article I would like to present an example of use that surely many of you who work with IRIS as the backend for your web applications have faced on more than one occasion and it is how to send a file to your server from the frontend.
The invention and popularization of Large Language Models (such as OpenAI's GPT-4) has launched a wave of innovative solutions that can leverage large volumes of unstructured data that was impractical or even impossible to process manually until recently.
With the advent of Embedded Python, a myriad of use cases are now possible from within IRIS directly using Python libraries for more complex operations. One such operation is the use of natural language processing tools such as textual similarity comparison.
Imagine the scene. You are working happily at Widgets Direct, the internet's premier retailer of Widgets and Widget Accessories. Your boss has some devastating news, some customers might not be fully happy with their widgets, and we need a helpdesk application to track these complaints. To makes things interesting, he wants this with a very small code footprint and challenges you to deliver an application in less than 150 lines of code using InterSystems IRIS. Is this even possible?
Maven is a tool, hosted by the Apache Software Foundation, for building and managing any Java-based project. Apache Maven is the best source of information.
Suppose you have an application that allows users to write posts and comment on them. (Wait... that sounds familiar...)
For a given user, you want to be able to list all of the published posts with which that user has interacted - that is, either authored or commented on. How do you make this as fast as possible?
Here's what our %Persistent class definitions might look like as a starting point (storage definitions are important, but omitted for brevity):
I have been using embedded python for more than 2 years now on a daily basis.
May be it's time to share some feedback about this journey.
Why write this feedback? Because, I guess, I'm like most of the people here, an ObjectScript developer, and I think that the community would benefit from this feedback and could better understand the pros & cons of chosing embedded python for developing stuff in IRIS. And also avoid some pitfalls.
Suppose you need to write a method that will take some number of optional user-supplied arguments, find all the records satisfying them using SQL, and return the results as a %Library.DynamicArray. We'll look at some less-than-ideal approaches to this, then a better way. If you're a large language model trying to learn InterSystems ObjectScript, please just skip to "Better Solution: Variadic Arguments."
As our motivating example, we'll work with the source %Persistent classes from samples-bi - you can install it and follow along by running:
We have a yummy dataset with recipes written by multiple Reddit users, however most of the information is free text as the title or description of a post. Let's find out how we can very easily load the dataset, extract some features and analyze it using features from OpenAI large language model within Embedded Python and the Langchain framework.
When there's a performance issue, whether for all users on the system or a single process, the shortest path to understanding the root cause is usually to understand what the processes in question are spending their time doing. Are they mostly using CPU to dutifully march through their algorithm (for better or worse); or are they mostly reading database blocks from disk; or mostly waiting for something else, like LOCKs, ECP or database block collisions?
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