InterSystems Caché globals provide very convenient features for developers. But why are globals so fast and efficient?

Theory

Basically, the Caché database is a catalog having the same name as the database and containing the CACHE.DAT file. On Unix systems, the database can also be an ordinary disk partition.

I have finished my 4th book about Caché and MUMPS. This will probably be my last.

I am deeply grateful and humbled for all the help I have received from this group and the WW Response Center.

You all have something very special going here.

If a picture is worth a thousand words, what's a video worth? Certainly more than typing a post.

Please check out my "Coding talks" on InterSystems Developers YouTube:

1. Analysing InterSystems IRIS System Performance with Yape. Part 1: Installing Yape

https://www.youtube.com/embed/3KClL5zT6MY
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Running Yape in a container.

2. Yape Container SQLite iostat InterSystems

https://www.youtube.com/embed/cuMLSO9NQCM
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Extracting and plotting pButtons data including timeframes and iostat.

InterSystems Data Platforms and performance - Part 5 Monitoring with SNMP

Introduction

The common requirement in many applications is logging of data changes in a database - which data has changed, who changed them and when (audit logging). There are many articles about this question and there are different approaches on how to do that in Caché.

In this article I'll show you how you can easily convert between many different document formats, these to be precise:

• in: html, odt, doc, docx, rtf, sdw, txt...
• out: html, odt, docx, rtf, pdf, txt, tex, xhtml...

and many more, check out this table. We'll use LibreOffice as a conversion engine.

Recently, a partner company started to develop an Angular client for their Cache application. Together, we decided to leverage the power of Caché dynamic objects to exchange JSON encoded data between client and server parts. However, we realized that currently there is a gap in Cache JSON implementation that prevents simple use of traditional registered and persistent classes to exposed their data with the same ease as with XML. I wrote a small JSON adapter, that does the job and bridgers the gap. It's purpose is simple expose data described by a regular Cache class in a one-to-one fashion to a %DynamicObject. On the other hand, when a serialized JSON data comes in, it can be easily deserialized into dynamic object and subsequently bound to regular class by the newly created adapter.

++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).

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>

or "So you just got yelled at by your boss, for sending him an unformatted Hello World webpage"

Our previous lesson ended with us serving a Message value obtained from a Caché REST service to the client, using Angular as a runtime. While there is a lot of moving parts involved in this process, the page is not especially exciting at the moment. Before we can start adding new features, we should take a step back and review our tools.

Healthcare interoperability is instrumental in improving patient care, decreasing healthcare provider costs, and providing a more accurate picture to providers. However, with so many different systems, data is formatted in many different ways. There are many standards that have been created to try to solve this problem, including HL7v2, HL7v3, and CDA but each one has its drawbacks.

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.

Setting the Scene

I was editing the properties of a persistent ObjectScript class the other day and noticed that the storage definition wasn't updating to reflect my latest changes.

In this case, I deleted a property that was no longer needed from the class definition, saved, recompiled, and still saw it in the storage definition.

Migration from Caché to IRIS can be quite a challenge if your code is grown over many years
and probably not so clean structured as you may like it. So you face the need to check your
migrated code against some reference data. A few samples might not be a problem,
but some hundred GB of data for testing might be.

A possible step could be to have your fresh code in IRIS but leave your huge datastore on Caché and connect both environments over ECP. I have created a demo project that gives you the opportunity to try this based on 2 Docker images with IRIS and with Caché connected over ECP.