We have a rule to disable a user account if they have not logged in for a certain number of days. IRIS Audit database logs many events such as login failures for example. It can be configured to log successful logins as well. We have IRIS clusters with many IRIS instances. I like to run queries against audit data from ALL IRIS instances and identify user accounts which have not logged into ANY IRIS instance.

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Apache Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.

And now it is possible to use with InterSystems IRIS as well.

An online demo is available and it uses IRIS Cloud SQL as a data source.

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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

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Quick Tips: Total Productive Maintenance

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})

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})

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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!!

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Say you have a receiving system that accepts HL7 and provides error messages in field ERR:3.9 in the ACK it returns. You require a different reply code action depending on the error message, however the Reply Code Actions settings for the operation do not provide this level of granularity. One option could be to create a process that takes the ACK and then completes the action you were expecting, however things can get a bit messy if the action is to retry the message, especially when trying to view a message trace.

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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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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.

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This is a simple tutorial on the quickest way I found to create a sample database for any purposes such as testing, making samples for tutorials, etc.

Creating a namespace

  1. Open the terminal
  2. Write the command "D $SYSTEM.SQL.Shell()"
  3. Write "CREATE DATABASE " and the name you want for your namespace.

Now you have a new namespace in a faster way than creating it from the Management Portal - which of course offers way more configuration options.

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· Feb 13 4m read
When to use Columnar Storage

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.

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In this article, we will establish an encrypted JDBC connection between Tableau Desktop and InterSystems IRIS database using a JDBC driver.
While documentation on configuring TLS with Java clients covers all possible topics on establishing an encrypted JDBC connection, configuring it with Tableau might be a little bit tricky, so I decided to write it down.

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Let me introduce my new project, which is irissqlcli, REPL (Read-Eval-Print Loop) for InterSystems IRIS SQL

  • Syntax Highlighting
  • Suggestions (tables, functions)
  • 20+ output formats
  • stdin support
  • Output to files

Install it with pip

pip install irissqlcli

Or run with docker

docker run -it caretdev/irissqlcli irissqlcli iris://_SYSTEM:SYS@host.docker.internal:1972/USER

Connect to IRIS

$ irissqlcli iris://_SYSTEM@localhost:1972/USER -W
Password for _SYSTEM:
Server:  InterSystems IRIS Version 2022.3.0.606 xDBC Protocol Version 65
Version: 0.1.0
[SQL]_SYSTEM@localhost:USER> select $ZVERSION
| Expression_1                                                                                            |
| IRIS for UNIX (Ubuntu Server LTS for ARM64 Containers) 2022.3 (Build 606U) Mon Jan 30 2023 09:05:12 EST |
1 row in set
Time: 0.063s
[SQL]_SYSTEM@localhost:USER> help
| Command  | Shortcut          | Description                                                |
| .exit    | \q                | Exit.                                                      |
| .mode    | \T                | Change the table format used to output results.            |
| .once    | \o [-o] filename  | Append next result to an output file (overwrite using -o). |
| .schemas | \ds               | List schemas.                                              |
| .tables  | \dt [schema]      | List tables.                                               |
| \e       | \e                | Edit command with editor (uses $EDITOR).                   |
| help     | \?                | Show this help.                                            |
| nopager  | \n                | Disable pager, print to stdout.                            |
| notee    | notee             | Stop writing results to an output file.                    |
| pager    | \P [command]      | Set PAGER. Print the query results via PAGER.              |
| prompt   | \R                | Change prompt format.                                      |
| quit     | \q                | Quit.                                                      |
| tee      | tee [-o] filename | Append all results to an output file (overwrite using -o). |
Time: 0.012s

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· Jan 23 2m read
Global-Streams-to-SQL #2

Some technical background information

There is not just one class in this package: rcc.gstream.cls but also rcc.gstreamT.cls

While rcc.gstream works with direct access to the stream globals, the *T version uses
a Process Private Global (PPG) as Temporary storage.
using SELECT * FROM RCC.gstreamT WHERE RCC.useT('^jpgS')=1 and similar.

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· Jan 23 2m read

In general Global Streams are data objects embedded in Classes / Tables.
Using and viewing them with SQL is normally a part of the access to the containing tables.


During debugging or searching for strange or unexpected behavior there could be the need to
get closer to the stored stream. No big problem with direct access to Globals with SMP or Terminal.
But with SQL you are lost.
So my tool provides dynamic access to Global Streams wherever you may need this
Special thanks to @Oliver Wilms for the inspiration for this tool.

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· Jan 10 4m read
Columnar Storage in 2022.3

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.

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I just wrote up a quick sample to help a colleague load data into IRIS from R using RJDBC, and figured it's worth sharing here for future reference.

Ultimately it was pretty simple, aside from IRIS not liking "." in column names; the workaround is to just rename the columns. Someone better at R than me could probably provide some generic approach. smiley

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On the Latest GlobalSummit 2022, InterSystems Introduced Cloud SQL. So, you may have lightweight InterSystems IRIS with access to SQL only. Well, what if you would still need some Interoperability features in the cloud as well? There are various solutions on the market nowadays, which offer a bunch of integration adapters out of the box and can be extended with support from the community. Some time ago, I've implemented an adapter for the Node-RED project, which can be deployed manually everywhere you want. Now I would like to introduce a new integration with my recent discovery, n8n.io

Banner image

n8n.io is a workflow automation platform, that supports over 200 different integrations out of the box and from a community, and now including InterSystems IRIS.

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· Sep 13, 2022 8m read

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.

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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!

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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.

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