#SQL

I worked through the Community for proposals to provide end users
in an easy way with data formatted as EXCEL sheet.

There is a great article Tips & Tricks - SQL to Excel

there's an important message embedded: "EXCEL can interpret HTML tables and display them as usual"

Hi Community,

In this article, I listed 5 useful SQL functions with explanations and query examples 👇🏻

These 5 functions are

• COALESCE
• RANK
• DENSE_RANK
• ROW_NUMBER
• Function to Get Running Totals

So Let us start with COALESCE function

Hi Community,

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

Class Queries in InterSystems IRIS (and Cache, Ensemble, HealthShare) is a useful tool that separates SQL queries from Object Script code. Basically, it works like this: suppose that you want to use the same SQL query with different arguments in several different places.In this case you can avoid code duplication by declaring the query body as a class query and then calling this query by name. This approach is also convenient for custom queries, in which the task of obtaining the next row is defined by a developer. Sounds interesting? Then read on!

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.

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.

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.

Last week, we announced the InterSystems IRIS Data Platform, our new and comprehensive platform for all your data endeavours, whether transactional, analytics or both. We've included many of the features our customers know and loved from Caché and Ensemble, but in this article we'll shed a little more light on one of the new capabilities of the platform: SQL Sharding, a powerful new feature in our scalability story.

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!!

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 .

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.

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 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.

Similar to Caché so also in IRIS, you have ClassQueries available in 2 variants

• Basic class queries, which use the class %SQLQuery and an SQL SELECT statement.
• Custom class queries, which use the class %Query and custom logic to execute, fetch, and close the query.