#SQL

Hi Developers!

There is a recent update came for developer community images of InterSystems IRIS and IRIS For Health.

This release comes with Environment variables support.

Currently 3 variables are supported:

• IRIS_USERNAME=user to create
• IRIS_PASSWORD=with password
• IRIS_NAMESPACE=create namespace if doesn't exist

Here is what you can do - see below.

Start iris with your username and password created:

docker run --rm --name iris-sql -d -p 9091:1972 -p 9092:52773  -e IRIS_PASSWORD=demo -e IRIS_USERNAME=demo intersystemsdc/iris-community

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.

Hi,

If you want to import data from a mySQL export file (exported with mysqldump), you will find here a little script that could help.

Only the INSERT commands in the sql file are executed into Caché. Indices are not computed for better performance.
%NOINDEX, %NOCHECK and %NOLOCK are generated on each INSERT line.

Currently, the file can not contain a "),(" pattern inside the values part of the INSERT command. If this is the case, the line is skipped. This feature may be implemented in the extractValuesList method.

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.

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.

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.

For the upcoming Python contest, I would like to make a small demo, on how to create a simple REST application using Python, which will use IRIS as a database. Using this tools

• FastAPI framework, high performance, easy to learn, fast to code, ready for production
• SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL
• Alembic is a lightweight database migration tool for usage with the SQLAlchemy Database Toolkit for Python.
• Uvicorn is an ASGI web server implementation for Python.

The Data Platforms department here at InterSystems is gearing up for this year's crop of interns, and I for one am very excited to meet them all next week!

We've got folks from top technical colleges with diverse specialties from hard core engineers to pure computer scientists to mathematicians to business professionals. They come from countries around the world like Vietnam, China, and Finland and they all come with impressive backgrounds. We're sure they will do very well this summer.

There is an interesting new feature in the recently announced 2024.1 preview, JSON_TABLE. JSON_TABLE is one of a family of functions introduced by the 2016 version of the SQL Standard (ISO Standard, published in early 2017). It allows for JSON values to be mapped to columns and queried using SQL. JSON_TABLE is valid in the FROM clause of some SQL statements.

The syntax of JSON_TABLE is quite large, allowing for exceptional conditions where the provided JSON values don't match expectations, nested structures and so on.

What do you do if you want to have the ID field have a meaningful name for your application?

Sometimes it comes to pass that when you're making a new table that you want to have the unique row identifier (a.k.a. IDKEY) to be a field that has a name that is meaningful for your data. Moreover, sometimes you want to set this value directly. Caché fully supports this functionality and it works Suppose you have a class Test.Kyle. The data will be stored like so:

^Test.Kyle(IDKEY)=$LB("",Field1,Field2,...,Fieldn)

Hi Community,
In this article, I will introduce my application iris-mlm-explainer

So I know it's been a while, and I hate to let my adoring fans down... just not enough to actually start writing again. But the wait is over and I'm back! Now bask in my beautiful ginger words!

For this series, I am going to look at some common problems we see in the WRC and discuss some common solutions. Of course, even if you find a solution here, you are always welcome to call in and expression you gratitude, or just hear my voice!

This week's common problem: "My query returns no data."

Named parameters can be achieved with SQLAlchemy :

from sqlalchemy import create_engine, text,types,engine

_engine = create_engine('iris+emb:///')

with _engine.connect() as conn:
    rs = conn.execute(text("select :some_private_name"), {"some_private_name": 1})
    print(rs.all())

or with native api

from sqlalchemy import create_engine, text,types,engine

# set URL for SQLAlchemy
url = engine.url.URL.create('iris', username='SuperUser', password='SYS', host='localhost', port=33782, database='FHIRSERVER')

_engine = create_engine(url)

with _engine.connect() as conn:
    rs = conn.execute(text("select :some_private_name"), {"some_private_name": 1})
    print(rs.all())

Hi folks!

Sometimes when we develop a mockup or PoC there is a need for a simple interface that will provide data in IRIS in JSON against SQL queries.

And recently I contributed a simple module that does exactly that:

accepts SQL string and returns the JSON.

How to install? Just call:

zpm "install sql-rest"

If you install it in a namespace X it will setup a /sql endpoint to your system that will accept POST requests with SQL string and will return the result for you for the data available in the namespace X.