I'm participating in the Developing with InterSystems Objects and SQL with Joel Solon. The course is very nice and I will share with you some tips I got during the training. Tips presented in the day 3:
FOREIGN TABLES is a rather fresh feature in IRIS (2023.?) So I was motivated to try something new by own hands. Documentation of Foreign Table from File is a good starting point. Also the related promotional video is fine to start with.
As you may well remember from Global Summit 2022 or the 2022.2 launch webinar, we're releasing an exciting new capability for including in your analytics solutions on InterSystems IRIS. Columnar Storage introduces an alternative way of storing your SQL table data that offers an order-of-magnitude speedup for analytical queries. First released as an experimental feature in 2022.2, the latest 2022.3 Developer Preview includes a bunch of updates we thought were worth a quick post here.
Application to import 12 Datasets along with 43 tables dynamically by using LOAD DATA command which loads data from a source into an IRIS SQL table. List of Datasets
This can be achieved by using the CSV() procedure of the %SQL.Util.Procedures class. Below is an example of usage code. (Assuming that the file test.csv is in c:\temp.)
If you are looking to breathe new life into an old MUMPS application follow these steps to map your globals to classes and expose all that beautiful data to Objects and SQL.
This example is going to cram in 4 or 5 different things beyond what was covered in Part 1
Let's say we have two serial classes, one as a property of another:
Class test.Serial Extends %SerialObject
{
Property Serial2 As test.Serial2;
}
Class test.Serial2 Extends %SerialObject
{
Property Property As %String;
}
And a persistent class, that has a property of test.Serial type:
Class test.Persistent Extends %Persistent
{
Property Datatype As %String;
Property Serial As test.Serial;
}
So it's a serial, inside a serial, inside a persistent object.
This is a translation of the following article. Thanks [@Evgeny Shvarov] for the help in translation.
Someone posted a question on DC asking whether it was possible to determine access rights for a particular table row always at runtime, and if it was, how could one do that? Answer: it is possible and it’s not hard at all.
With InterSystems IRIS 2022.2, we introduced Columnar Storage as a new option for persisting your IRIS SQL tables that can boost your analytical queries by an order of magnitude. The capability is marked as experimental in 2022.2 and 2022.3, but will "graduate" to a fully supported production capability in the upcoming 2023.1 release.
The product documentation and this introductory video, already describe the differences between row storage, still the default on IRIS and used throughout our customer base, and columnar table storage and provide high-level guidance on choosing the appropriate storage layout for your use case. In this article, we'll elaborate on this subject and share some recommendations based on industry-practice modelling principles, internal testing, and feedback from Early Access Program participants.
Presenter: Anton Umnikov Task: Identify your slowest SQL queries and tune them for better performance Approach: Use InterSystems’ query profiling and analysis tools. Discuss how system configuration can affect performance
This session will show you how you identify the weakest link in your application SQL and introduce you to the fine art of tuning those queries. To do this we will take a look at InterSystems query profiling and analysis tools, as well as how system configuration can impact SQL performance.
Problem: Obscurity on how our SQL engine works
Content related to this session, including slides, video and additional learning content can be found here.
You need to install the application first. If not installed, please refer to the previous article
Application demonstration
After successfully running the iris image vector search application, some data needs to be stored to support image retrieval as it is not initialized in the library.
Sometimes we need to import data into InterSystems IRIS from CSV. It can be done e.g. via csvgen tool that generates a class and imports all the data into it.
But what if you already have your own class and want to import data from CSV into your existing table?
There are numerous ways to do that but you can use csvgen (or csvgen-ui) again! I prepared and and example and happy to share. Here we go!
I've asked a lot of questions leading up to this, so I wanted to share some of my progress.
The blue line represents the number of messages processed. The background color represents the average response time. You can see ticks for each hour (and bigger ticks for each day). Hovering over any point in the graph will show you the numbers for that period in time.
This is super useful for "at a glance" performance monitoring as well as establishing patterns in our utilization.
Beginning in Caché 2013.1, InterSystems introduced Outlier Selectivity to improve query plan selection involving fields with one atypical value.
In this article, I hope to use an example 'Projects' table to demonstrate what Outlier Selectivity is, how it helps SQL performance and a few considerations for writing queries.
SQLTools is a Visual Studio Code (VS Code) Extension with over 3.5 million downloads that provides connections to many of the most used databases, including InterSystems IRIS, using drivers.
In this article I will explain how to display management portal dashboard by using Python Flask web and Boostrap framework with the help of embedded python
Recently I noticed a Kaggle dataset for the prediction of whether a Covid-19 patient will be admitted to ICU. It is a spreadsheet of 1925 encounter records of 231 columns of vital signs and observations, with the last column of "ICU" being 1 for Yes or 0 for No. The task is to predict whether a patient will be admitted to ICU based on known data.
One of the reasons why I love Cache and Iris is that not only you can do anything you can imagine, also you can do it in a lot of different ways!!.
Imagine that you have an integration running with IRIS connected by ODBC you probably only run SQL queries but you can also create stored procedures and inside write the code to do everything you can imagine.
I'm going to give you some examples but the limit is your imagination!!
How can IRIS productions be deployed more quickly and with greater peace of mind?
The aim of interoperability productions is to enable you to connect systems in order to transform and route messages between them. To connect systems, you develop, configure, deploy and manage productions that integrate several software systems.
There are three things most important to any SQL performance conversation: Indices, TuneTable, and Show Plan. The attached PDFs includes historical presentations on these topics that cover the basics of these 3 things in one place. Our documentation provides more detail on these and other SQL Performance topics in the links below. The eLearning options reinforces several of these topics. In addition, there are several Developer Community articles which touch on SQL performance, and those relevant links are also listed.
There is a fair amount of repetition in the information listed below. The most important aspects of SQL performance to consider are:
The types of indices available
Using one index type over another
The information TuneTable gathers for a table and what it means to the Optimizer
How to read a Show Plan to better understand if a query is good or bad
Earlier in this series, we've presented four different demo applications for iKnow, illustrating how its unique bottom-up approach allows users to explore the concepts and context of their unstructured data and then leverage these insights to implement real-world use cases. We started small and simple with core exploration through the Knowledge Portal, then organized our records according to content with the Set Analysis Demo, organized our domain knowledge using the Dictionary Builder Demo and finally build complex rules to extract nontrivial patterns from text with the Rules Builder Demo.
This time, we'll dive into a different area of the iKnow feature set: iFind. Where iKnow's core APIs are all about exploration and leveraging those results programmatically in applications and analytics, iFind is focused specifically on search scenarios in a pure SQL context. We'll be presenting a simple search portal implemented in Zen that showcases iFind's main features.
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:
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.
The object and relational data models of the Caché database support three types of indexes, which are standard, bitmap, and bitslice. In addition to these three native types, developers can declare their own custom types of indexes and use them in any classes since version 2013.1. For example, iFind text indexes use that mechanism.
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