Recently @Anastasia Dyubaylopublished a post (this one) showing a new IntegratedML functionality for time series predictions that @Thomas Dyaralready presented to us at the Global Summit 2023 so, let's go to set up a small workshop to test it!
When developing interoperability productions, it might be useful to have settings outside of a Business Host. The primary reason is when you need a setting to affect several different Business Hosts and want to guarantee that the value is the same. While System Default Settings can be used to propagate settings for Business Hosts, they can be changed by overriding the value on a BH level (although the advantage of Business Host settings set via SDS is that they don't need custom code which our current approach requires). Another reason is when you need to affect non-setting parts of the Business Host configuration (PoolSize, Enabled, etc.)
A password manager is an important security tool that allows users to store and manage their passwords without the need to remember or write them down in insecure places. In this article, we will explore the development of a simple password manager using the Flask framework and the InterSystems IRIS database.
Key Features
Our password manager application will provide the following key features:
Here in %SYS, we have already examined users, resources, and roles. Now that we know how to set all of that up, we should give it a purpose. Next we will talk about applications! As you may expect, we will see various identical class methods defined here that we have seen in the previous classes. However, some of them will have some tiny yet significant differences.
Methods written in ObjectScript can use pass-by-reference arguments to return information to the caller. Python doesn’t support pass-by-reference arguments, so Embedded Python in IRIS doesn’t support them either. That's it, that's the end of the post, hope you liked it. 😉 But wait, what about the Classic Rock & Roll?
In a previous question, I have illustrated a few problems using Embedded Python interactively as you would do from Docker console or IRIS terminal. Investigation of the causes brought a rather clear picture. It's a classic impedance mismatch
When using InterSystems IRIS as an interoperability engine, we all know and love how easy it is to use the Message Viewer to review message traces and see exactly what's going on in your production. When a system is handling millions of messages per day, you may not know exactly where to begin your investigation though.
Over my years supporting IRIS productions, I often find myself investigating things like...
If you need to migrate your server for some reason, you can reduce the setup work by copying configuration information from the pre-migration environment to the post-migration environment.
The following setting information can be migrated.
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.
Most examples I've seen so far in OEX or DC left the impression that VECTORs are just something available with SQL with the 3 Functions especially around VECTOR_Search. * TO_VECTOR() * VECTOR_DOT_PRODUCT () * VECTOR_COSINE ()
There is a very useful summary hidden in iris-vector-search demo package. From there you find everything you need over several links and corners.
If you ever wondered how to debug some requests that are being made to or from IRIS, well here is a little tutorial on how to do that.
During a complex project, usually you get the specifications and implement the communication between IRIS and other things based on that. But from the paper to the real world there's usually a huge gap and you need to know why you are receiving an error on a parameter, on a header, you are not receiving the data and so on.
We recently uploaded to OpenExchange a small application that I developed a while ago (and that @Jose-Tomas Salvador improved and refined) that I often use when I need to generate large volumes of HL7 messaging.
As noted in the previous article Native API has some limits. So I did some research on a more Terminal-like interface that provides me with access like a console or the IRIS terminal to allow my $QUERY over a global and other nice commands that are not supported/mapped in NativeAPI for ObjectScript.
A few months ago, I faced a significant challenge: streamlining the handling of business logic in our application. My goal was to extract the business logic from the code and hand it over to analysts. Dealing with a multitude of rules could easily result in a code littered with countless "if" statements, especially if the coder lacked an understanding of cyclomatic complexity. Such code becomes a source of pain for those working with it—difficult to write, test, and develop.
Sometimes we need to know for sure if the current environment has sufficient cores, memory, and bandwidth to support the planned number of users and such SLAs as latency, response time, and availability. This is true for databases and backends. This is why it is mandatory for critical applications and databases to simulate the users simultaneous/concurrent requests and collect metrics about performance and availability.
In the last article, we talked about a few starters for Django. We learned how to begin the project, ensure we have all the requisites, and make a CRUD. However, today we are going a little further. Sometimes we need to access more complex methods, so today, we will connect IRIS to a Python environment, build a few functions and display them on a webpage. It will be similar to the last discussion, but further enough for you to make something new, even though not enough to feel lost.
The platform server entry configuration prediction application connects to Iris in Java and uses its Integrated ML technology to analyze data such as hospital outpatient volume, number of services, number of messages, and message save time. It can predict the server configuration required for the hospital entry platform before the hospital integration platform enters, providing convenience for customers.
According to Databricks Apache Parquet is an open source, column-oriented data file format designed for efficient data storage and retrieval. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk. Apache Parquet is designed to be a common interchange format for both batch and interactive workloads. It is similar to other columnar-storage file formats available in Hadoop, namely RCFile and ORC. (source: https://www.databricks.com/glossary/what-is-parquet).
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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