Introduction

The field test of Caché 2016.2 has been available for quite some time and I would like to focus on one of the substantial features that is new in this version: the document data model. This model is a natural addition to the multiple ways we support for handling data including Objects, Tables and Multidimensional arrays. It makes the platform more flexible and suitable for even more use cases.

The goal of this “DeepSee Troubleshooting Guide” is to help you track down and fix problems in your DeepSee project.

If the problem can’t be fixed by following the guidelines, you will at least have enough information to submit a WRC issue with DeepSee Support and provide all the evidence to us, so we can continue the investigation together and resolve it faster!

When there's a performance issue, whether for all users on the system or a single process, the shortest path to understanding the root cause is usually to understand what the processes in question are spending their time doing. Are they mostly using CPU to dutifully march through their algorithm (for better or worse); or are they mostly reading database blocks from disk; or mostly waiting for something else, like LOCKs, ECP or database block collisions?

This example is extracted from a long-running installation.
The purpose is to have simple monitoring of several servers at a rather primitive level.
Just slightly more intelligent than a raw PING. But still easy to integrate.
It avoids the overkill of information you are often confronted with while you are just
interested in the number of active processes or similar basic figures.
The example shows a basic skeleton that might be easily filled by your real needs.

It consists of 3 sections:

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.

Running a Background Job using the JOB command is a well-known feature.
Using ECP to distribute databases to several servers is also well-known.
But using the combination of both to run a process on a different server
seems to be a rare case.

Sure there are enough other ways to start a remote job, but the special
combination with ECP where the application server starts a process on a
data server without additional networking is worth to be remembered.

Hi Developers!

Often when we develop some library, tool, package, whatever on InterSystems ObjectScript we have a question, how we deploy this package on the target machine?

Also, we often expect that some other libraries already installed, so our package depends on them, and often on some particular version of it.

When you code on javascript, python, etc the role of packages deployment with dependency management takes package manager.

So, I'm pleased to announce that InterSystems ObjectScript Package Manager available!

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.

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.

The 2021.2 release of the InterSystems IRIS Data Platform includes many exciting new features for fast, flexible and secure development of your mission-critical applications. Embedded Python definitely takes the limelight (and for good reason!), but in SQL we've also made a massive step forward towards a more adaptive engine that gathers detailed statistical information about your table data and exploits it to deliver the best query plans. In this brief series of articles, we'll take a closer at three elements that are new in 2021.2 and work together towards this goal, starting with Run Time Plan Choice.

It's hard to figure out the right order to talk about these (you can't imagine how often I've reshuffled them in writing this article!) because they fit together in such a nice way. As such, feel free to go on a limb and read these in random order .

While the classic solution followed rather close the concepts and design of the ancestors
Caché / IRIS allows a more modern approach to flexible/multidimensional properties

Hi developers!

Today I would like to address a subject that has given me a hard time. I am sure this must have been the case for quite a number of you already (so-called “the bottleneck”). Since this is a broad topic, this article will only focus on identifying incoming HTTP requests that could be causing slowness issues. I will also provide you with a small tool I have developed to help identify them.

Our software is becoming more and more complex, processing a large number of requests from different sources, be it front-end or third-party back-end applications. To ensure optimal performance, it is essential to have a logging system capable of taking a few key measurements, such as the response time, the number of global references and the number of lines of code executed for each HTTP response. As part of my work, I get involved in the development of EMR software as well as incident analysis. Since user load comes mostly from HTTP requests (REST API or CSP application), the need to have this type of measurement when generalized slowness issues occur has become obvious.

A demo in traditional style COS

This was written based on a previous trial in .XLS
It is far from being perfect. Rather a challenge for
improvement in all directions (code, interface, ...)
So anyone feel invited to make it better.