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
Python has become the most used programming language in the world (source: https://www.tiobe.com/tiobe-index/) and SQL continues to lead the way as a database language. Wouldn't it be great for Python and SQL to work together to deliver new functionality that SQL alone cannot? After all, Python has more than 380,000 published libraries (source: https://pypi.org/) with very interesting capabilities to extend your SQL queries within Python.
A good writer is supposed to draw you in with the title and bury the answer somewhere in the article. I suppose this makes me a bad writer – don’t think less of me, my whole feeling of self-worth comes from the opinions of internet strangers!
In some of the last few articles I've talked about types between IRIS and Python, and it is clear that it's not that easy to access objects from one side at another.
Fortunately, work has already been done to create SQLAlchemy-iris (follow the link to see it on Open Exchange), which makes everything much easier for Python to access IRIS' objects, and I'm going to show the starters for that.
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.
The typical WHERE condition in SQL relates mostly to some content of the rows you work on. So it needs to be calculated and checked for each row you access. Differently (and that's why I named it STATIC) is a WHERE condition that is independent of the rows you access.
Though the access is quite comfortable (in traditional COS sense) there are 2 main restrictions that hurt:
#1) It is not saved to disk unless your application includes code to save it specifically. #2) It cannot be stored in or exposed through SQL tables there are some more I'll show how to overcome these limits
InterSystems IRIS 2020.1 brings a broad set of improved and new capabilities to help build important applications. In addition to the many significant performance improvements accrued through 2019.1 and 2020.1, we are introducing one of our biggest changes in recent SQL history: the Universal Query Cache. This article provides more context on its impact to SQL-based applications at a technical level.
The 2021.2 release of the InterSystems IRIS Data Platform includes many exciting new features for fast, flexible and secure development of your mission-critical applications. Embedded Python definitely takes the limelight (and for good reason!), but in SQL we've also made a massive step forward towards a more adaptive engine that gathers detailed statistical information about your table data and exploits it to deliver the best query plans. In this brief series of articles, we'll take a closer at three elements that are new in 2021.2 and work together towards this goal, starting with Run Time Plan Choice.
It's hard to figure out the right order to talk about these (you can't imagine how often I've reshuffled them in writing this article!) because they fit together in such a nice way. As such, feel free to go on a limb and read these in random order .
While the classic solution followed rather close the concepts and design of the ancestors Caché / IRIS allows a more modern approach to flexible/multidimensional properties
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!!
IRIS External Table is an InterSystems Community Open Source Project, that allows you to use files, stored in the local file system and cloud object storage such as AWS S3 as SQL Tables.
A VIEW in SQL is basically a prepared SQL statement. It has to be executed and assembled like any other SQL query. MATERIALIZED VIEW means that the content is collected before hands and can be retrieved rather fast. I saw the concept first with my favorite competitor named O* and they made a lot of noise about it. { favorite: because I could win every benchmark against them }
We are glad to announce that DBeaver has supported InterSystems IRIS out-of-the-box since version 7.2.4. You don't need to configure it manually anymore, just find the IRIS icon in the Connections list.
I'm doing a query in SQL and I need to sort my data by some non-repeated field.
Unfortunately, my data is grouped in a way that I cannot guarantee that any column will not have repeated data, so one solution would be to take the row number.
Also, the Cache is not accepting Row_Number () in my querry and I would like to know if there is another solution to return line numbers or some way to add this function to the Cache.
As we all know, Caché is a great database that accomplishes lots of tasks within itself. However, what do you do when you need to access an external database? One way is to use the Caché SQL Gateway via JDBC. In this article, my goal is to answer the following questions to help you familiarize yourself with the technology and debug some common problems.
The related package avoids adding %JSONAdaptor to each class but uses instead SQL functions JSON_OBJECT() to create my JSON objects. With this approach, you can add JSON to any class - even deployed ones - without any need for change or recompiling.
The trigger was the Export of M:N relationships as JSON objects or arrays.
Standard error logs in IRIS / Caché / Ensemble are written global ^ERRORS. As this piece dates back some decades back to previous millennium its structure is far from the typical SQL storage structures.
Every now and then you may encounter a situation where for various reasons ODBC is the only option to access a remote system. Which is sufficient as long as you need to examine or change tables. But you can't directly execute some commands or change some Global.
Special thanks @Anna Golitsyna for inspiring me to publish this.
In the vast and varied SQL database market, InterSystems IRIS stands out as a platform that goes way beyond just SQL, offering a seamless multimodel experience and supporting a rich set of development paradigms. Especially the advanced Object-Relational engine has helped organizations use the best-fit development approach for each facet of their data-intensive workloads, for example ingesting data through Objects and simultaneously querying it through SQL. Persistent Classes correspond to SQL tables, their properties to table columns and business logic is easily accessed using User-Defined Functions or Stored Procedures. In this article, we'll zoom in on a little bit of the magic just below the surface, and discuss how it may affect your development and deployment practices. This is an area of the product where we have plans to evolve and improve, so please don't hesitate to share your views and experiences using the comments section below.
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?
We have received quite a lot of interest in using SQL on FHIR data. As you know, FHIR data is encoded in the form of a complex directed graph, and thus you can not easily query it with traditional SQL queries or business intelligence tools. Some customers have noticed that the "FHIR search tables" in IRIS for Health have flattened part of the FHIR graph, and have tried to use them for analytics. This is an undocumented and unsupported part of IRIS for Health, and can change without notice.
In this article you will have access to the curated base of articles from the InterSystems Developer Community of the most relevant topics to learning InterSystems IRIS. Find top published articles ranked by Machine Learning, Embedded Python, JSON, API and REST Applications, Manage and Configure InterSystems Environments, Docker and Cloud, VSCode, SQL, Analytics/BI, Globals, Security, DevOps, Interoperability, Native API. Learn and Enjoy!
GmOwl is a solution that offers an organized and engaging learning platform. It was developed to cater to the increasing need, for learning tools providing a versatile quiz environment that meets users requirements.
The main objective of GmOwl is to deliver an user experience for individuals participating in quizzes while giving administrators comprehensive control, over content and user engagement.
GmOwl uses Java EE with MVC template, and the InterSystems IRIS database is used to store data. The InterSystems JDBC Driver is used to connect to the database.
This is the second piece in our series on 2021.2 SQL enhancements delivering an adaptive, high-performance SQL experience. In this article, we'll zoom in on the innovations in gathering Table Statistics, which are of course the primary input for the Run Time Plan Choice capability we described in the previous article.
This article is intended to be a simple tutorial on how to create ODBC connections and working with them, since I found starting with them a little bit confused, but I had amazing people to take my hand and walk me through it, and I think everyone deserves that kind of help too. I'm going to divide each little part in sections, so feel free to jump to the one you feel the need to, although I recommend reading everything.
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