Article
· Aug 16, 2023 11m read
Http request response time monitoring

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.

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Contestant

Killer presentations are essential for opening doors, doing business and delighting customers. These types of presentations move away from the traditional and tiring topics and portions of texts, or even pictures of happy people to get closer to the interests of their audience.The best presentations materialize what customers are looking for into something they can see working. Instead of promises, examples in execution and the ability to be very close to what you want to do.

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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?

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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!

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Hello everyone!

We are Longevica (https://www.longevica.com/) Healthtech, a Boston-based healthy aging digital health startup. Longevica was born as a research company back in 2009; we pioneered the screening of chemicals, which would drastically extend the life span. With 1000 screened pharmaceuticals and 20 000 mice experiments, we have identified specific compounds that, if taken daily, could extend life by years. This discovery leads to two questions: how to measure the effect of aging progress in real-time and how to make this a lifelong habit. This led us to the digital health market to create a new company Longevica HealthTech.

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The InterSystems Iris Fhirserver running on a Raspberry Pi Raspberry running as a FHIRserver

Raspberry running as FHIRserver

About a year ago I wrote some articles about the installation of the HAPI FHIRserver on a Raspberry Pi. At that time, I only knew the basics of the FHIR standard, little about the technology behind FHIR-servers and not much more about the Raspberry. By trying, failing, giving up and trying again I learned a lot.

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Article
· Jun 12, 2023 11m read
Examples to work with IRIS from Django

Introducing Django

Django is a web framework designed to develop servers and APIs, and deal with databases in a fast, scalable, and secure way. To assure that, Django provides tools not only to create the skeleton of the code but also to update it without worries. It allows developers to see changes almost live, correct mistakes with the debug tool, and treat security with ease.

To understand how Django works, let’s take a look at the image:

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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.

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Working in support, I usually get asked how many days I should keep journals. Should it be two days or after two backups? More? Less? Why two?

The correct answer (for most of the environments) is that you should keep the journals since the last validated Backup. I.e., until you don't check if a Backup is valid (restoring the file and checking with the Integrity utility), you can't be sure there is a good copy of your data and can't purge the journals safely.

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Article
· Aug 4, 2021 3m read
IRIS Mirror in the cloud (AWS)

I have been working on redesigning a Health Connect production which runs on a mirrored instance of Healthshare 2019. We were told to take advantage of containers. We got to work on IRIS 2020.1 and split the database part from the Interoperability part. We had the IRIS mirror running on EC2 instances and used containers to run IRIS interoperability application. Eventually we decided to run the data tier in containers as well.

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Article
· Jul 27, 2020 2m read
ZPMshow, a helper for tired fingers

The offer of ZPM is growing daily and the short names and
acronyms of the offer are sometimes hard to understand and
also hard to type with my lazy fingers.

So I decided to have

  • a listing with the descriptions from the repository,
  • split in short junks to avoid backscroll,
  • controlled forward / backward scrolling,
  • the option to select my packages by number,
  • to install or uninstall with limited typing.

It runs with do ^zpmshow

A snapshot from the screen:

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YASPE is the successor to YAPE (Yet Another pButtons Extractor). YASPE has been written from the ground up with many internal changes to allow easier maintenance and enhancements.

YASPE functions:

  • Parse and chart InterSystems Caché pButtons and InterSystems IRIS SystemPerformance files for quick performance analysis of Operating System and IRIS metrics.
  • Allow a deeper dive by creating ad-hoc charts and by creating charts combining the Operating System and IRIS metrics with the "Pretty Performance" option.
  • The "System Overview" option saves you from searching your SystemPerformance files for system details or common configuration options.

YASPE is written in Python and is available on GitHub as source code or for Docker containers at:


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++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).

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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.

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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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Article
· Jan 31, 2018 3m read
Container - What is a Container?

Containers

With the launch of InterSystems IRIS Data Platform, we provide our product even in a Docker container. But what is a container?

The fundamental container definition is that of a sandbox for a process.

Containers are software-defined packages that have some similarities to virtual machines (VM) like for example they can be executed.

Containers provide isolation without a full OS emulation. Containers are therefore much lighter than a VM.

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Article
· Apr 4, 2020 1m read
Websocket Echo server IRIS

In Caché you had an example of a WebSocket Server in namespace SAMPLES
With IRIS the samples are gone and require additional installation effort.

So I refurbished the code with some useful additions:

  • independent of namespace
  • timeout control from client
  • readable communication log

This contains 2 classes:

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iris-docker-multi-stage-script

A python script to keep your docker iris images in shape ;)

Witout changing your dockerfile or your code you can reduce the size of your image by 50% or more !

TL;DR

Name the builder image builder and the final image final and add this to end of your Dockerfile:

Modify your Dockerfile to use a multi-stage build:

ARG IMAGE=intersystemsdc/irishealth-community:latest
FROM $IMAGE as builder

Add this to end of your Dockerfile:

FROM $IMAGE as final

ADD --chown=${ISC_PACKAGE_MGRUSER}:${ISC_PACKAGE_IRISGROUP} https://github.com/grongierisc/iris-docker-multi-stage-script/releases/latest/download/copy-data.py /irisdev/app/copy-data.py

RUN --mount=type=bind,source=/,target=/builder/root,from=builder \
    cp -f /builder/root/usr/irissys/iris.cpf /usr/irissys/iris.cpf && \
    python3 /irisdev/app/copy-data.py -c /usr/irissys/iris.cpf -d /builder/root/ 

Boom! You're done!

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