A python script to keep your docker iris images in shape ;)

Witout changing your dockerfile or your code you can reduce the size of your image by 50% or more !


Name the builder image builder and the final image final and add this to end of your Dockerfile:

Modify your Dockerfile to use a multi-stage build:

ARG IMAGE=intersystemsdc/irishealth-community:latest
FROM $IMAGE as builder

Add this to end of your Dockerfile:

FROM $IMAGE as final


RUN --mount=type=bind,source=/,target=/builder/root,from=builder \
    cp -f /builder/root/usr/irissys/iris.cpf /usr/irissys/iris.cpf && \
    python3 /irisdev/app/ -c /usr/irissys/iris.cpf -d /builder/root/ 

Boom! You're done!

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· Apr 4, 2020 1m read
Websocket Echo server IRIS

In Caché you had an example of a WebSocket Server in namespace SAMPLES
With IRIS the samples are gone and require additional installation effort.

So I refurbished the code with some useful additions:

  • independent of namespace
  • timeout control from client
  • readable communication log

This contains 2 classes:

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· Feb 26, 2018 2m read
M:N Relationship

If you have worked with Caché Objects,
You know already all about Relationships (one:many , parent:child) ...
But you will not find a word on many:many relationships in the docs.

But I met the question quite often from new adopters of Caché objects:
"Is it possible to implement many:many relationships ?" YES - of course !

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· Apr 4, 2020 2m read
Websocket Client IRIS internal

IRIS 2010.1 brought us a new feature: %Net.WebSocket.Client

As a continuation of my series of WS Clients I just couldn't resist to try it.
Well, this is the result and it was rather simple in the end.

  • After I succeeded in my personal fight against Windows Firewall ;-)

You basically need to prepare 3 classes:

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As we all well know, InterSystems IRIS has an extensive range of tools for improving the scalability of application systems. In particular, much has been done to facilitate the parallel processing of data, including the use of parallelism in SQL query processing and the most attention-grabbing feature of IRIS: sharding. However, many mature developments that started back in Caché and have been carried over into IRIS actively use the multi-model features of this DBMS, which are understood as allowing the coexistence of different data models within a single database. For example, the HIS qMS database contains both semantic relational (electronic medical records) as well as traditional relational (interaction with PACS) and hierarchical data models (laboratory data and integration with other systems). Most of the listed models are implemented using SP.ARM's qWORD tool (a mini-DBMS that is based on direct access to globals). Therefore, unfortunately, it is not possible to use the new capabilities of parallel query processing for scaling, since these queries do not use IRIS SQL access.

Meanwhile, as the size of the database grows, most of the problems inherent to large relational databases become right for non-relational ones. So, this is a major reason why we are interested in parallel data processing as one of the tools that can be used for scaling.

In this article, I would like to discuss those aspects of parallel data processing that I have been dealing with over the years when solving tasks that are rarely mentioned in discussions of Big Data. I am going to be focusing on the technological transformation of databases, or, rather, technologies for transforming databases.

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This formation, accessible on my GitHub, will cover, in half a hour, how to read and write in csv and txt files, insert and get inside the IRIS database and a distant database using Postgres or how to use a FLASK API, all of that using the Interoperability framework using ONLY Python following the PEP8 convention.

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· Apr 26, 2020 5m read
Materialized Views

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 devil }

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Working in support, I usually get asked how many days I should keep journals. Should it be two days or after two backups? More? Less? Why two?

The correct answer (for most of the environments) is that you should keep the journals since the last validated Backup. I.e., until you don't check if a Backup is valid (restoring the file and checking with the Integrity utility), you can't be sure there is a good copy of your data and can't purge the journals safely.

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· Mar 2 4m read
Tutorial - Streams in Pieces

This tutorial is a follow on to Working with %Query
It was displaying the content of the input stream chopped in fixed-size chunks.
But often those streams are structured and have well-defined separators (e.g HL7)
So as a side subject of this tutorial, this chapter shows how to break a stream into PIECES.

It is exactly the same idea as the $PIECE() function for strings with some add-ons.

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Introducing Django

Django is a web framework designed to develop servers and APIs, and deal with databases in a fast, scalable, and secure way. To assure that, Django provides tools not only to create the skeleton of the code but also to update it without worries. It allows developers to see changes almost live, correct mistakes with the debug tool, and treat security with ease.

To understand how Django works, let’s take a look at the image:

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We are excited to announce a new part of InterSystems documentation that makes it easier to upgrade InterSystems IRIS® data platform, InterSystems IRIS® for Health™, or HealthShare® Health Connect. The Upgrade Impact Checklist at shows you all the things you need to consider – and only the things you need to consider – in an upgrade between any two versions.

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Hey Developers,

We'd like to invite you to join our next contest dedicated to creating useful tools to make your fellow developers' lives easier:

🏆 InterSystems Developer Tools Contest 🏆

Submit an application that helps to develop faster, contributes more qualitative code, and helps in testing, deployment, support, or monitoring of your solution with InterSystems IRIS.

Duration: January 23 - February 12, 2023

Prize pool: $13,500

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2 2.2K
· Jul 27, 2020 2m read
ZPMshow, a helper for tired fingers

The offer of ZPM is growing daily and the short names and
acronyms of the offer are sometimes hard to understand and
also hard to type with my lazy fingers.

So I decided to have

  • a listing with the descriptions from the repository,
  • split in short junks to avoid backscroll,
  • controlled forward / backward scrolling,
  • the option to select my packages by number,
  • to install or uninstall with limited typing.

It runs with do ^zpmshow

A snapshot from the screen:

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Released with no formal announcement in IRIS preview release 2019.4 is the /api/monitor service exposing IRIS metrics in Prometheus format. Big news for anyone wanting to use IRIS metrics as part of their monitoring and alerting solution. The API is a component of the new IRIS System Alerting and Monitoring (SAM) solution that will be released in an upcoming version of IRIS.

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Hi all,

When is a Developer Community post not a Developer Community post? When it's just a few sentences wrapped around a link to InterSystems documentation! And what better way to finish up 2021 than by telling you about something cool that's been available since v2020.3? As the ball drops in Times Square, curl up with this:

Repeating Previous Commands

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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.

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