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.

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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.

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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
[SQL]_SYSTEM@localhost:USER>

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

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.

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Article
· Oct 11, 2022 2m read
ZPM Simple Implementation Cookbook

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.

Today this cookbook will go through 3 sections:

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Article
· Sep 13, 2022 8m read
CI/CD with IRIS SQL

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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Say I've been developing a web application that uses IRIS as the back end. I've been working on it with unauthenticated access. It's getting to the point where I would like to deploy it to users, but first I need to add authentication. Rather than using the default IRIS password authentication, I'd like users to sign in with my organization's Single Sign On, or some other popular identity provider like Google or GitHub. I've read that OpenID Connect is a common authentication standard, and it's supported by IRIS. What is the simplest way to get up and running?

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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.

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Article
· Aug 2, 2022 8m read
Data models in InterSystems IRIS

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.

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What is Web Scraping:

In simple terms, Web scraping, web harvesting, or web data extraction is an automated process of collecting large data(unstructured) from websites. The user can extract all the data on particular sites or the specific data as per the requirement. The data collected can be stored in a structured format for further analysis.

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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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Overview

We started to use Azure Service Bus (ASB) as an enterprise messaging solution 3 years ago. It is being used to publish and consume data between many applications in the organization. Since the data flow is complex, and one application’s data is usually needed in multi applications the “publisher” ---> ”multiple subscribers” model was a great fit. The ASB usage in the organization is dozens of millions of messages per day, while IRIS platform is having around 2-3 million messages/day.

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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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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.

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InterSystems SAM is a great tool to monitor your InterSystems IRIS and InterSystems IRIS For Health clusters on prem or in a cloud environment. This article describes how you can implement a customized alert handler. This is currently an undocumented and most likely an unknown feature of InterSystems SAM. With future releases it will be probably made easier to leverage this useful concept.

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Like hardware hosts, virtual hosts in public and private clouds can develop resource bottlenecks as workloads increase. If you are using and managing InterSystems IRIS instances deployed in public or private clouds, you may have encountered a situation in which addressing performance or other issues requires increasing the capacity of an instance's host (that is, vertically scaling).

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Article
· Mar 17, 2021 3m read
Making the most of $Query

I ran into an interesting ObjectScript use case today with a general solution that I wanted to share.

Use case:

I have a JSON array (specifically, in my case, an array of issues from Jira) that I want to aggregate over a few fields - say, category, priority, and issue type. I then want to flatten the aggregates into a simple list with the total for each of the groups. Of course, for the aggregation, it makes sense to use a local array in the form:

agg(category, priority, type) = total

Such that for each record in the input array I can just:

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Introduction

InterSystems IRIS 2020.1 includes PEX (Production EXtension Framework) to facilitate the development of IRIS Interoperability productions with components written in Java or .NET.

Thanks to PEX, an integration developer with knowledge of Java or .NET can benefit from the power, scalability, and robustness of the InterSystems IRIS Interoperability framework and be productive in no time.

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