In this series of articles, I'd like to present and discuss several possible approaches toward software development with InterSystems technologies and GitLab. I will cover such topics as:

• Git 101
• Git flow (development process)
• GitLab installation
• GitLab Workflow
• Continuous Delivery
• GitLab installation and configuration
• GitLab CI/CD
• Why containers?
• Containers infrastructure
• CD using containers

In the first article, we covered Git basics, why a high-level understanding of Git concepts is important for modern software development, and how Git can be used to develop software.

In the second article, we covered GitLab Workflow - a complete software life cycle process and Continuous Delivery.

In the third article, we covered GitLab installation and configuration and connecting your environments to GitLab

In the fourth article, we wrote a CD configuration.

In the fifth article, we talked about containers and how (and why) they can be used.

In the sixth article let's discuss main components you'll need to run a continuous delivery pipeline with containers and how they all work together.

In this article, we'll build Continuous Delivery configuration discussed in the previous articles.

Before we start talking about databases and different data models that exist, first we'd better talk about what a database is and how to use it.

A database is an organized collection of data stored and accessed electronically. It is used to store and retrieve structured, semi-structured, or raw data which is often related to a theme or activity.

At the heart of every database lies at least one model used to describe its data. And depending on the model it is based on, a database may have slightly different characteristics and store different types of data.

To write, retrieve, modify, sort, transform or print the information from the database, a software called Database Management System (DBMS) is used.

The size, capacity, and performance of databases and their respective DBMS have increased by several orders of magnitude. It has been made possible by technological advances in various areas, such as processors, computer memory, computer storage, and computer networks. In general, the development of database technology can be divided into four generations based on the data models or structure: navigational, relational, object and post-relational.

In the first article I discussed testing and debugging Caché web applications with external tools. Second part would cover Caché tools.

They are:

• CSP Gateway and Webapp configuration
• CSP Gateway logging
• CSP Gateway tracing
• ISCLOG
• Custom logging
• Session events
• Output to device

Overview

We often run into connectivity problems with HealthShare (HS) deployments in Microsoft Azure that have multiple HealthShare components (instances or namespaces) installed on the same VM, especially when needing to communicate to other HS components while using the Azure Load Balancer (ILB) to provide mirror VIP functionality.  Details on how and why a load balancer is used with database mirroring can be found this community article.

As per Azure Load Balancer documentation, https://docs.microsoft.

1. interoperability-embedded-python

This proof of concept aims to show how the iris interoperability framework can be used with embedded python.

1.1. Table of Contents

• 1. interoperability-embedded-python
  • 1.1. Table of Contents
  • 1.2. Example
  • 1.3. Register a component
• 2. Demo
• 3. Prerequisites
• 4. Installation
  • 4.1. With Docker
  • 4.2. Without Docker
  • 4.3. With ZPM
  • 4.4. With PyPI
    • 4.4.1. Known issues
• 5. How to Run the Sample
  • 5.1. Docker containers
  • 5.2. Management Portal and VSCode
  • 5.3. Open the production
• 6. What's inside the repository
  • 6.1. Dockerfile
  • 6.2. .vscode/settings.json
  • 6.3. .

   _________ ___ ____  
  |__  /  _ \_ _|  _ \ 
    / /| |_) | || |_) |
   / /_|  __/| ||  __/ 
  /____|_|  |___|_|    

Starting in version 2021.1, InterSystems IRIS began shipping with a python runtime in the engine's kernel. However, there was no way to install packages from within the instance. The main draw of python is its enormous package ecosystem. With that in mind, I introduce my side project zpip, a pip wrapper that is callable from the iris terminal.

What is zpip?

Hi Community,
In this article I will demonstrate the usage of InterSystems Embedded Python, We will cover below topics:

• 1-Embedded Python Overview
• 2-Usage of Embedded Python
  • 2.1- Using a Python Library from ObjectScript
  • 2.2- Calling the InterSystems APIs from Python
  • 2.3- Using ObjectScript and Python together

• 3-Using python Built-in Functions
• 4-Python Modules/Libraries 
• 5-Embedded Python Use Cases
  • 5.1- Printing PDF by using python Reportlab Library
  • 5.2- Generating QR code by using Python Qrcode Library
  • 5.3- Get GEO location by using Python Folium Library
  • 5.

csp-log-tutorial

Prerequisites

Make sure you have git installed.

I created a git folder inside the IRIS mgr directory. I right clicked the git folder and chose Git Bash Here from the context menu.

git clone https://github.com/oliverwilms/csp-log-tutorial.git

Clone my csp-log-tutorial GitHub repo if you like to try it out for yourself.

I will describe in this tutorial how I try to use access.log and CSP.log files in webgateway pods to track requests and responses.

My team works with IRIS containers running on Red Hat OpenShift Container Platform (Kubernetes) in AWS.

ZPM is designed to work with applications and modules for InterSystems IRIS Data Platform. It consists of two components, the ZPN Client which is a CLI to manage modules, and The Registry which is a database of modules and meta-information. We can use ZPM to search, install, upgrade, remove and publish modules. With ZPM you can install ObjectScript classes, Frontend applications, Interoperability productions, IRIS BI solutions, IRIS Datasets or any files such as Embedded Python wheels.

On this GitHub you can find all the information on how to use a HuggingFace machine learning / AI model on the IRIS Framework using python.

1. iris-huggingface

Usage of Machine Learning models in IRIS using Python; For text-to-text, text-to-image or image-to-image models.

Here, models as example :

• https://huggingface.co/gpt2

• https://huggingface.co/Jean-Baptiste/camembert-ner

• https://huggingface.co/bert-base-uncased

• https://huggingface.co/facebook/detr-resnet-50

• https://huggingface.co/facebook/detr-resnet-50-panoptic

• 1. iris-huggingface

• 2. Installation

  • 2.1. Starting the Production
  • 2.

I am often asked to review customers' IRIS application performance data to understand if system resources are under or over-provisioned.

This recent example is interesting because it involves an application that has done a "lift and shift" migration of a large IRIS database application to the Cloud. AWS, in this case.

A key takeaway is that once you move to the Cloud, resources can be right-sized over time as needed. You do not have to buy and provision on-premises infrastructure for many years in the future that you expect to grow into.

Continuous monitoring is required. Your application transaction rate will change as your business changes, the application use or the application itself changes. This will change the system resource requirements. Planners should also consider seasonal peaks in activity. Of course, an advantage of the Cloud is resources can be scaled up or down as needed.

For more background information, there are several in-depth posts on AWS and IRIS in the community. A search for "AWS reference" is an excellent place to start. I have also added some helpful links at the end of this post.

AWS services are like Lego blocks, different sizes and shapes can be combined. I have ignored networking, security, and standing up a VPC for this post. I have focused on two of the Lego block components;

• Compute requirements.
• Storage requirements.

In this tutorial, I’d like to talk about Class Queries. To be more precise, about the Queries based on user-written code:

A lot of people disregard this type of query just because they aren’t very comfortable with writing lots of ObjectScript code for the methods or they don’t see how they can use it in their relational apps. But to be honest, for me – it’s one of the coolest inventions for the relational model in IRIS! It lets you expose whatever information you want (not limited to tables of your database) as a relational resultset for a client.

In this article, we’ll build a highly available IRIS configuration using Kubernetes Deployments with distributed persistent storage instead of the “traditional” IRIS mirror pair. This deployment would be able to tolerate infrastructure-related failures, such as node, storage and Availability Zone failures. The described approach greatly reduces the complexity of the deployment at the expense of slightly extended RTO.

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
[SQL]_SYSTEM@localhost:USER>

In this article I'll cover testing and debugging Caché web applications (mainly REST) with external tools. Second part covers Caché tools.

You wrote server-side code and want to test it from a client or already have a web application and it doesn't work. Here comes debugging. In this article I'll go from the easiest to use tools (browser) to the most comprehensive (packet analyzer), but first let's talk a little about most common errors and how they can be resolved.

The Amazon Web Services (AWS) Cloud provides a broad set of infrastructure services, such as compute resources, storage options, and networking that are delivered as a utility: on-demand, available in seconds, with pay-as-you-go pricing. New services can be provisioned quickly, without upfront capital expense. This allows enterprises, start-ups, small and medium-sized businesses, and customers in the public sector to access the building blocks they need to respond quickly to changing business requirements.

Updated: 10-Jan, 2023 

Hi Community!

I think everyone keeps the source code of the project in the repository nowadays: Github, GitLab, bitbucket, etc. Same for InterSystems IRIS projects  check any on Open Exchange.

What do we do every time when start or continue working with a certain repository with InterSystems Data Platform?

We need a local InterSystems IRIS machine, have the environment for the project set up and the source code imported.

So every developer performs the following:

1. Check out the code from repo
2. Install/Run local IRIS installation
3. Create a new namespace/database for a project
4. Import the code into this new namespace
5. Setup all the rest environment
6. Start/continue coding the project 

If you dockerize your repository this steps line could be shortened to this 3 steps:

1. Check out the code from repo
2. Run docker-compose build 
3. Start/continue coding the project 

Profit - no any hands-on for 3-4-5 steps which could take minutes and bring head ache sometime.

You can dockerize (almost) any your InterSystems repo with a few following steps. Let’s go!

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.

Enterprise Monitor is a component of Ensemble and can help organizations monitor multiple productions running on different namespaces within the same instance or namespaces running on multiple instances.

Documentation can be found at:

http://docs.intersystems.com/ens20161/csp/docbook/DocBook.UI.Page.cls?KEY=EMONITOR_all#EMONITOR_enterprise

In Ensemble 2016.1 there were changes made to make this utility work with HealthShare environments.

This article will:

• Show how to set up Enterprise Monitor for HealthShare sites
• Show some features of Enterprise Monitor
• Show some features of Enterprise Message Viewer

For this article, I used the following version of HealthShare:

Cache for Windows (x86-64) 2016.1 (Build 656U) Fri Mar 11 2016 17:42:42 EST [HealthShare Modules:Core:14.02.2415 + Linkage Engine:14.02.2415 + Patient Index:14.02.2415 + Clinical Viewer:14.02.2415 + Active Analytics:14.02.2415]

First of all, what is data anonymization?

According to Wikipedia:

Data anonymization is a type of information sanitization whose intent is privacy protection. It is the process of removing personally identifiable information from data sets, so that the people whom the data describe remain anonymous.

In other words, the data anonymization is a process that retains the data but keeps the source anonymous. Depending on the adopted anonymization technique the data is redacted, masked or substituted.

And that is the purpose of iris-Disguise, to provide a set of anonymization tools.

I'm sure you have met this situation:

• There is a bug in a System that you can't reproduce yourself locally
• You need to run a few lines in the affected instance
• You get full access to System Management Portal
• But there is just  no terminal, nor console, nor access with Studio, Atelier or VSCode
• How to run your few lines for testing ???

Visual Studio Code (VSCode) is the most popular code editor on the market. It was created by Microsoft and distributed as a free IDE. VSCode supports dozens of programming languages, including ObjectScript, Until 2018, Atelier (based on Eclipse). It was considered as one of the main options to develop InterSystems products.

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.

However, you do not have to wait for SAM to start planning and trialling this API to monitor your IRIS instances.

The Art of Mapping Globals to Classes 1 of 3

Looking to breathe new life into an old MUMPS application?  Follow these steps to map your existing globals to classes and expose all that beautiful data to Objects and SQL.

By following the simple steps in this article and the next two you will be able to map all but the craziest globals to Caché classes.  For the crazy ones I will put up a zip file of different mappings I have collected over the years.  This is NOT for new data; if you don’t already have existing global please just use the default storage.

Over the years, I have found myself needing to create multiple HL7 messages based on a single inbound message. Usually these take the form of an order or result from a lab. Each time I have approached it, I have tried to start from scratch under the belief that the previous attempt could have been done better.

Recently, the need arose again and I was able to create a solution that I wasn't ashamed of. My main concern was that I would always either find myself getting buried in a BPL, or use ObjectScript and attempt to edit messages using the SetValueAt Method for the HL7 Message Class.

Hi Developers!

Suppose you have a persistent class with data and you want to have a simple Angular UI for it to view the data and make CRUD operations.

Recently @Alberto Fuentes described how to build Angular UI for your InterSystems IRIS application using RESTForms2. 

In this article, I want to tell you how you can get a simple Angular UI to CRUD and view your InterSystems IRIS class data automatically in less than 5 minutes.

Let's go!

The last days I've work with the great new feature: LOAD DATA With this post I would like to share my first experiences with you. The following points do not contain any order or other evaluation. These are only things that I noticed when using the LOAD DATA command. It should also be noted that these points are based on the IRIS Version 2021.2.0.617 which is a preview release. So it may be that my observations do not apply to newer IRIS versions. But maybe they are helpful for others.

1) The file path is on server side

I have done my first tests via JDBC.

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. This article details how to create new SQL Stored Procedures in InterSystems IRIS Database using Embedded Python.