Hey everyone!

I recently learnt something new while working with WRC on an issue, and I wanted to share with everyone on the off chance it could help someone else.

Scenario:

Files are being inexplicably written to a folder on your server and, due to the number of files in the folder and general system throughput, it is not possible to work through the files to track down the source.

Introduction

This is a quickstart guide to IRIS for Linux systems administrators who need to be able to support the IRIS DB as well as other normal infrastructure tasks.

IRIS is a DB system from Intersystems. An IRIS DB can hold code (in the form of a Class) or data (in the form of Globals). IRIS DB are Linux files called IRIS.DAT.

1. Don’t use Python built into Mac. Because of System Integrity Protection (SIP) you will not be able to access the libraries that the InterSystems Python binding requires. The Python build into the MAC has baked in what libraries it can use.
2. Install another Python. Don’t put this other ahead of Mac Python on path since this could break things. Apple regards its Python as part of the its OS, and may use that Python in its system operations.
3. This Python will not be installed into the protected area.

There are couple of options to do this.

1. You can extend your class &JSON.Adaptor, it will do it for you with inbuilt logicaldtodisplay methods

2. Create another request class and do your transformations

3. Before calling the json convert to stream methods, transform the object's field with a ZDT.

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I have an extremely simple example where there is a class with 3 properties

When it comes to build an iris image, we can use the cpf merge files.

Here is an cpf merge example:

[Actions]
CreateDatabase:Name=IRISAPP_DATA,Directory=/usr/irissys/mgr/IRISAPP_DATA

CreateDatabase:Name=IRISAPP_CODE,Directory=/usr/irissys/mgr/IRISAPP_CODE

CreateNamespace:Name=IRISAPP,Globals=IRISAPP_DATA,Routines=IRISAPP_CODE,Interop=1

ModifyService:Name=%Service_CallIn,Enabled=1,AutheEnabled=48

CreateApplication:Name=/frn,NameSpace=IRISAPP,DispatchClass=Formation.REST.Dispatch,AutheEnabled=48

ModifyUser:Name=SuperUser,PasswordHash=a31d24aecc0bfe560a7e45bd913ad27c667dc25a75cbfd358c451bb595b6bd52bd25c82cafaa23ca1dd30b3b4947d12d3bb0ffb2a717df29912b743a281f97c1,0a4c463a2fa1e7542b61aa48800091ab688eb0a14bebf536638f411f5454c9343b9aa6402b4694f0a89b624407a5f43f0a38fc35216bb18aab7dc41ef9f056b1,10000,SHA512

Slowness of the 'Source' and 'Target' parameters in the (portal) Message Viewer is caused by the Bitmap indices of Ens.MessageHeader.

Running the 'bitmap index clean up' routine for Ens.MessageHeader will fix the issue.

I checked with WRC and they advised:

Presenter: Anton Umnikov
Task: Identify your slowest SQL queries and tune them for better performance
Approach: Use InterSystems’ query profiling and analysis tools. Discuss how system configuration can affect performance

This session will show you how you identify the weakest link in your application SQL and introduce you to the fine art of tuning those queries. To do this we will take a look at InterSystems query profiling and analysis tools, as well as how system configuration can impact SQL performance.

Problem: Obscurity on how our SQL engine works

Content related to this session, including slides, video and additional learning content can be found here.

When creating custom Business Hosts, it's often necessary to add properties to the class for additional settings that will be used in the initialization or operation of the host. The property name itself isn't always very descriptive, so it's an advantage to have a custom caption display with the field.

In Caché, it was fairly straightforward:

Scenario

You all know Open Exchange (OEX) and the is no need for a detailed explanation.

It consists of a directory with various filters and detail pages for packages.

ISCLOGs are a useful debugging tool provided with IRIS that can help you troubleshoot many different issues. For more information about creating these logs, please see the following IRIS documentation: "Logging".

The demo is based on the raw class descriptions.
The data classes used are Address, Person, Employee, Company
For a more attractive demo, a JSONtoString method by ID was added.

I was approached recently by and end use who wanted to perform analysis of their databases and see how they could save some space by picking data good for deletion without harming the application. As part of investigation, they wanted to know sizes of globals within datasets. This can be achieved by various means but all of them provide data in text form only.

I thought I might be a good tool for database administrators in general - to see global sizes in a graphical way.

We have a yummy dataset with recipes written by multiple Reddit users, however most of the information is free text as the title or description of a post. Let's find out how we can very easily load the dataset, extract some features and analyze it using features from OpenAI large language model within Embedded Python and the Langchain framework.

Presenter: Benjamin De Boe
Task: Extract specialized information from your unstructured data
Approach: Combine InterSystems iKnow technology with third-party and custom text-processing tools

This session explains how you can easily combine ISC, third-party and custom text processing tools to get the broadest insights in your unstructured data.

Content related to this session, including slides, video and additional learning content can be found here.