Best Practices recommendations on how to develop, test, deploy and manage solutions on InterSystems Data Platforms better.
Some time ago GitHub, has announced the new feature, GitHub Codespaces. It gives an ability to run VSCode in the browser, with almost the same power as it would run locally on your machine, but also with a power of clouds, so, you are able to choose the machine type with up to 32 CPU cores and 64 GB of RAM.
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Looks impressive, is not it? But how it could help us, to work with projects driven by InterSystems IRIS? Let's have a look, how to configure it for us.
This article is translation of one from Habrahabr InterSystems blog (Russian).
The original post can be found here: https://habrahabr.ru/company/intersystems/blog/251611/
Everyone familiar with InterSystems Ensemble, an integration and application development platform, knows what the Ensemble Workflow subsystem is and how useful it can be for automating human interaction. For those who don’t know Ensemble (and/or Workflow), I will briefly describe its capabilities (others may skip this part and learn how they can use the Workflow interface in Angular.js).
The following steps show you how to display a sample list of metrics available from the /api/monitor service.
In the last post, I gave an overview of the service that exposes IRIS metrics in Prometheus format. The post shows how to set up and run IRIS preview release 2019.4 in a container and then list the metrics.
This post assumes you have Docker installed. If not, go and do that now for your platform :)
Follow the download instructions at Preview Distributions to download the Preview Licence Key and an IRIS Docker image.
InterSystems IRIS 2020.1 includes PEX (Production EXtension Framework) to facilitate the development of IRIS Interoperability productions with components written in Java or .NET.
Thanks to PEX, an integration developer with knowledge of Java or .NET can benefit from the power, scalability, and robustness of the InterSystems IRIS Interoperability framework and be productive in no time.
For an IRIS Interoperability framework expert, PEX makes it easy to enrich integrations with pre-existing Java- or .
Hi Community,
In this article I will explain how to display management portal dashboard by using Python Flask web and Boostrap framework with the help of embedded python
In this article, we will discuss about Orphaned Messages.
Every message body is associated with a message Header which holds the metadata. The Header holds information like source configuration name, target configuration name, time created, time processed, associated message body reference, session information, message body class name, message status. When there are message body records that do not have their corresponding Header records those are called Orphan message bodies. We will discuss possible causes which could end up with orphan message bodies.
There have been some very helpful articles in the community that show how to use Grafana with IRIS (or Cache/Ensemble) by using an intermediate database.
But I wanted to get at IRIS structures directly. In particular, i wanted to access the Cache History monitor data that is accessible by SQL as described here
https://community.intersystems.com/post/apm-using-cach%C3%A9-history-mo…
and didn't want anything between me and the data.
I already had class queries that returned the data i wanted, so i just needed to embed them in a REST class that returned JSON. I haven't included my class Grafana.
** Revised Feb-12, 2018
While this article is about InterSystems IRIS, it also applies to Caché, Ensemble, and HealthShare distributions.
Introduction
Memory is managed in pages. The default page size is 4KB on Linux systems. Red Hat Enterprise Linux 6, SUSE Linux Enterprise Server 11, and Oracle Linux 6 introduced a method to provide an increased page size in 2MB or 1GB sizes depending on system configuration know as HugePages.
At first HugePages required to be assigned at boot time, and if not managed or calculated appropriately could result in wasted resources. As a result various Linux distributions introduced Transparent HugePages with the 2.6.38 kernel as enabled by default. This was meant as a means to automate creating, managing, and using HugePages. Prior kernel versions may have this feature as well however may not be marked as [always] and potentially set to [madvise].
Transparent Huge Pages (THP) is a Linux memory management system that reduces the overhead of Translation Lookaside Buffer (TLB) lookups on machines with large amounts of memory by using larger memory pages. However in current Linux releases THP can only map individual process heap and stack space.
In this article, I would show how you can upload and download files from InterSystems products via http.
The questions about working with files over http arise fairly often on community and I'm usually linking to my FileServer project which demonstrates file upload/download but I'd like to talk a bit more on how we can serve and receive files from InterSystems products.
The topic of for/while loop performance in Caché ObjectScript came up in discussion recently, and I'd like to share some thoughts/best practices with the rest of the community. While this is a basic topic in itself, it's easy to overlook the performance implications of otherwise-reasonable approaches. In short, loops iterating over $ListBuild lists with $ListNext or over a local array with $Order are the fastest options.
As a motivating example, we will consider looping over the pieces of a comma-delimited string.
One of the easiest ways to setup repeatable development environments is to spin up containers for them. I find that when iterating quickly, it was very convenient to host a vscode instance within my development container. Thus, I have created a quick container script to add a browser-based vscode into an IRIS container. This should work for most 2021.1+ containers.
We started to use Azure Service Bus (ASB) as an enterprise messaging solution 3 years ago. It is being used to publish and consume data between many applications in the organization. Since the data flow is complex, and one application’s data is usually needed in multi applications the “publisher” ---> ”multiple subscribers” model was a great fit. The ASB usage in the organization is dozens of millions of messages per day, while IRIS platform is having around 2-3 million messages/day.
Hi, community.
In this article, we will programmatically configure an Apache Web Gateway with Docker using:
We will use two images: one for the Web Gateway and the second one for the IRIS instance.
All necessary files are available in this GitHub repository.
Let’s start with a git clone:
git clone https://github.com/lscalese/docker-webgateway-sample.In this post, I am going to detail how to set up a mirror using SSL, including generating the certificates and keys via the Public Key Infrastructure built in to InterSystems IRIS Data Platform. I did a similar post in the past for Caché, so feel free to check that out here if you are not running InterSystems IRIS. Much like the original, the goal of this is to take you from new installations to a working mirror with SSL, including a primary, backup, and DR async member, along with a mirrored database. I will not go into security recommendations or restricting access to the files. This is meant to just simply get a mirror up and running. Example screenshots are taken on a 2018.1.1 version of IRIS, so yours may look slightly different.
Easy, easy, I'm not promoting a war against the machines in the best sci-fi way to avoid world domination of Ultron or Skynet. Not yet, not yet 🤔
I invite you to challenge the machines through the creation of a very simple game using ObjectScript with embedded Python.
I have to say that I got super excited with the feature of Embedded Python on InterSystems IRIS, it's incredible the bunch of possibilities that opens to create fantastic apps.
Let's build a tic tac toe, the rules are quite simple and I believe that everyone knows how to play.
We are ridiculously good at mastering data. The data is clean, multi-sourced, related and we only publish it with resulting levels of decay that guarantee the data is current. We chose the HL7 Reference Information Model (RIM) to land the data, and enable exchange of the data through Fast Healthcare Interoperability Resources (FHIR®).
We are also a high performing, full stack team, and like to keep our operational resources on task, so managing the underlying infrastructure to host the FHIR® data repository for purposes of ingestion and consumption is not in the cards for us.
As we all know, Caché is a great database that accomplishes lots of tasks within itself. However, what do you do when you need to access an external database? One way is to use the Caché SQL Gateway via JDBC. In this article, my goal is to answer the following questions to help you familiarize yourself with the technology and debug some common problems.
In this article I'll show you how you can easily convert between many different document formats, these to be precise:
and many more, check out this table. We'll use LibreOffice as a conversion engine.
Hi all. Today we are going to install Jupyter Notebook and connect it to Apache Spark and InterSystems IRIS.
Note: I have done the following on Ubuntu 18.04, Python 3.6.5.
If you are looking for well-known, widely-spread and mainly popular among Python users notebook instead of Apache Zeppelin, you should choose Jupyter notebook. Jupyter notebook is a very powerful and great data science tool. it has a big community and a lot of additional software and integrations.
During a major version upgrade it is advisable to recompile the classes and routines of all your namespaces (see Major Version Post-Installation Tasks).
do $system.OBJ.CompileAllNamespaces("u")do ##Class(%Routine).CompileAllNamespaces()
To automate this administration task and keep a log of any errors, below is an example of a class to import and compile into the USER namespace that you can use after each upgrade : admin.utils.cls
Class admin.utils.cls
Your application is deployed and everything is running fine. Great, hi-five! Then out of the blue the phone starts to ring off the hook – it’s users complaining that the application is sometimes ‘slow’. But what does that mean? Sometimes? What tools do you have and what statistics should you be looking at to find and resolve this slowness? Is your system infrastructure up to the task of the user load? What infrastructure design questions should you have asked before you went into production? How can you capacity plan for new hardware with confidence and without over-spec'ing? How can you stop the phone ringing? How could you have stopped it ringing in the first place?
In previous posts I have shown how it is possible to collect historical performance metrics using pButtons. I go to pButtons first because I know it is installed with every Data Platforms instance (Ensemble, Caché, …). However there are other ways to collect, process and display Caché performance metrics in real time either for simple monitoring or more importantly for much more sophisticated operational analytics and capacity planning.
InterSystems IRIS 2019 is going to introduce new and exciting features. One of the areas with new interesting must-to-know things is the API Management.
OpenAPI initiative (https://www.openapis.org/) is the organization supporting a standard specification to define APIs (https://github.com/OAI/OpenAPI-Specification).
In this article, I would like to talk about the spec-first approach to REST API development.
While traditional code-first REST API development goes like this:
Spec-first follows the same steps but reverse. We start with a spec, also doubling as documentation, generate a boilerplate REST app from that and finally write some business logic.
This is advantageous because:
Let's develop our API in a spec-first approach!
The InterSystems IRIS has excellent support for encryption, decryption and hashing operations. Inside the class %SYSTEM.Encryption (https://docs.intersystems.com/iris20212/csp/documatic/%25CSP.Documatic…) there are class methods for the main algorithms on the market.
As you can see, the operations are based on keys and include 3 options:
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.
This formation can mostly be done using copy paste and will guide you through everystep before challenging you with a global exercise.
We are available to answer any question or doubt in the comment of that post, on teams or even by mail at lucas.enard@intersystems.com .
While reviewing our documentation for our ^pButtons (in IRIS renamed as ^SystemPerformance) performance monitoring utility, a customer told me: "I understand all of this, but I wish it could be simpler… easier to define profiles, manage them etc.".
After this session I thought it would be a nice exercise to try and provide some easier human interface for this.
The first step in this was to wrap a class-based API to the existing pButtons routine.
I was also able to add some more "features" like showing what profiles are currently running, their time remaining to run, previously running processes and more.
The next step was to add on top of this API, a REST API class.
With this artifact (a pButtons REST API) in hand, one can go ahead and build a modern UI on top of that.
For example -
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Thanks to @Yuri Marx we have seen a very nice example for DB migration from Postgres to IRIS.
My personal problem is the use of DBeaver as a migration tool.
Especially as one of the strengths of IRIS ( and also Caché) before is the availability of the
SQLgateways that allow access to any external Db as long as for them an access usinig
JDBC or ODBC is available. So I extended the package to demonstrate this.
I ran into an interesting ObjectScript use case today with a general solution that I wanted to share.
I have a JSON array (specifically, in my case, an array of issues from Jira) that I want to aggregate over a few fields - say, category, priority, and issue type. I then want to flatten the aggregates into a simple list with the total for each of the groups.
Recently I needed to generate a Swagger spec from persistent and serial classes, so I'm publishing my code (it's not complete - you still need to hash out the application specifics, but it's a start). It's available here.
Let's say you have these classes:
Classes
