#Caché

Are you all ready for something you wish you knew ages ago (or, in my case, a DECADE ago)? Open up a portal in your favorite instance and go to:

System Administration->Configuration->Additional Settings->Startup

Scroll down to "Terminal Prompt" and click 'Edit'. This allows you to edit what you see on your terminal prompt. You can change that to my current setting: 8,3,2

What does this do? It adds your instance name for your prompt. So now your prompt can look like:

DEVELOPMENT:USER>

++Update: August 2, 2018

This article provides a reference architecture as a sample for providing robust performing and highly available applications based on InterSystems Technologies that are applicable to Caché, Ensemble, HealthShare, TrakCare, and associated embedded technologies such as DeepSee, iKnow, Zen and Zen Mojo.

Azure has two different deployment models for creating and working with resources: Azure Classic and Azure Resource Manager. The information detailed in this article is based on the Azure Resource Manager model (ARM).

I bet that not everyone familiar with InterSystems Caché knows about Studio extensions for working with the source code. You can actually use the Studio to create your own type of source code, compile it into interpretable (INT) and object code, and sometimes even add code completion support. That is, theoretically, you can make the Studio support any programming language that will be executed by the DBMS just as well as Caché ObjectScript. In this article, I will give you a simple example of writing programs in Caché Studio using a language that resembles JavaScript. If you are interested, please read along.

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.

By now it's a commonplace how to implement a basic REST API in Caché and there is good documentation about it here: REST in Caché

A question that comes up from time to time is:

How can I make a parameter in my REST url optional?

Simply put, is it possible to create a URL map in Caché that maps a URL like this:

Hi,

I'm doing a query in SQL and I need to sort my data by some non-repeated field.

Unfortunately, my data is grouped in a way that I cannot guarantee that any column will not have repeated data, so one solution would be to take the row number.

Also, the Cache is not accepting Row_Number () in my querry and I would like to know if there is another solution to return line numbers or some way to add this function to the Cache.

Best regards.

Write $Piece($SYSTEM.SYS.Horolog(),",",1)

This post is dedicated to the task of monitoring a Caché instance using SNMP. Some users of Caché are probably doing it already in some way or another. Monitoring via SNMP has been supported by the standard Caché package for a long time now, but not all the necessary parameters are available “out of the box”. For example, it would be nice to monitor the number of CSP sessions, get detailed information about the use of the license, particular KPI’s of the system being used and such. After reading this article, you will know how to add your parameters to Caché monitoring using SNMP.

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:

I'm facing a specific sort problem.
There are several thousands of articles sold all over.
Users expect to get a description in local language sorted by their specific collation.

During a major version upgrade it is advisable to recompile the classes and routines of all your namespaces (see Major Version Post-Installation Tasks).

Pouring The Coffee: Creating and scheduling a task

Don't you wish a fresh, hot cup of coffee could be waiting for you right when you get into the office? Let's automate that!

Cache and IRIS come with a built-in Task Manager, which should have a familiar feel to those used to using the Windows task scheduler or using cron on Linux. Your user account will need access to the %Admin_Task resource to use it, and you can access it in the management portal under System Operation -> Task Manager. When first installed, there are roughly 20 types of task that you can schedule.

To request a copy of the examples only – please see me, will email them out for free. pmkadow@gmail.com

InterSystems periodically updates our software release policies and practices to adjust to customer needs.

We're now changing our maintenance release cadence in order to be more predictable for customers and partners, and tweaking a few other areas.