Some changes in IRIS configuration require a restart of IRIS.
This is no big issue as long as I have access to the server command line with sufficient privileges.

In a container, this is not always given.
Stopping IRIS from the terminal/session prompt is no problem.
But the restart after is.  

Note1: container start-stop is no option as it might be removed by option --rm in docker run
Note2: the target is linux (manly in docker).  Windows is excluded

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Question
Robert Cemper · Apr 9, 2021
Moving IRIS db in runtime

 

I'm almost running out of disk space so I want to move 1 DB to a different hard drive.  
It's a rather simple but lengthy action during a shutdown of IRIS.  
But is this somehow possible under runtime in a stand-alone installation?  
I'm looking for kind of a "local drive failover"  

note: Mirror or Shadow is not an option.  

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InterSystems Official
Steven LeBlanc · Aug 21, 2020
Introducing InterSystems Container Registry

I am pleased to announce the availability of InterSystems Container Registry. This provides a new distribution channel for customers to access container-based releases and previews. All Community Edition images are available in a public repository with no login required. All full released images (IRIS, IRIS for Health, Health Connect, System Alerting and Monitoring, InterSystems Cloud Manager) and utility images (such as arbiter, Web Gateway, and PasswordHash) require a login token, generated from your WRC account credentials.

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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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Introduction
Several resources tell us how to run IRIS in a Kubernetes cluster, such as Deploying an InterSystems IRIS Solution on EKS using GitHub Actions and Deploying InterSystems IRIS solution on GKE Using GitHub Actions. These methods work but they require that you create Kubernetes manifests and Helm charts, which might be rather time-consuming.
To simplify IRIS deployment, InterSystems developed an amazing tool called InterSystems Kubernetes Operator (IKO). A number of official resources explain IKO usage in details, such as  New Video: Intersystems IRIS Kubernetes Operator and InterSystems Kubernetes Operator.

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This article is a continuation of Deploying InterSystems IRIS solution on GKE Using GitHub Actions, in which, with the help of GitHub Actions pipeline, our zpm-registry was deployed in a Google Kubernetes cluster created by Terraform. In order not to repeat, we’ll take as a starting point that:

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Hi All,

With this article, I would like to show you how easily and dynamically System Alerting and Monitoring (or SAM for short) can be configured. The use case could be that of a fast and agile CI/CD provisioning pipeline where you want to run your unit-tests but also stress-tests and you would want to quickly be able to see if those tests are successful or how they are stressing the systems and your application (the InterSystems IRIS backend SAM API is extendable for your APM implementation). 

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Imagine you want to see what InterSystems can give you in terms of data analytics. You studied the theory and now you want some practice. Fortunately, InterSystems provides a project that contains some good examples: Samples BI. Start with the README file, skipping anything associated with Docker, and go straight to the step-by-step installation. Launch a virtual instance, install IRIS there, follow the instructions for installing Samples BI, and then impress the boss with beautiful charts and tables. So far so good. 

Inevitably, though, you’ll need to make changes.

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In an earlier article (hope, you’ve read it), we took a look at the CircleCI deployment system, which integrates perfectly with GitHub. Why then would we want to look any further? Well, GitHub has its own CI/CD platform called GitHub Actions, which is worth exploring. With GitHub Actions, you don’t need to rely on some external, albeit cool, service.

In this article we’re going to try using GitHub Actions to deploy the server part of  InterSystems Package Manager, ZPM-registry, on Google Kubernetes Engine (GKE).

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Last time we deployed a simple IRIS application to the Google Cloud. Now we’re going to deploy the same project to Amazon Web Services using its Elastic Kubernetes Service (EKS).

We assume you’ve already forked the IRIS project to your own private repository. It’s called <username>/my-objectscript-rest-docker-template in this article. <root_repo_dir> is its root directory.

Before getting started, install the AWS command-line interface and, for Kubernetes cluster creation, eksctl, a simple CLI utility. For AWS you can try to use aws2, but you’ll need to set aws2 usage in kube config file as described here.

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A lot of developers like to work with Studio and have been looking into source code version control such as GIT or into enabling modern development workflows like CICD or DevOps processes.

This article describe an elementary solution to get you started in CICD and DevOps, even if you are not yet ready to move to Atelier or forth coming VS Code approach which enable client side source code version control.

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Hey Developers,

We're pleased to invite you to join the next InterSystems IRIS 2020.1 Tech Talk: DevOps on June 2nd  at 10:00 AM EDT! 

In this InterSystems IRIS 2020.1 Tech Talk, we focus on DevOps. We'll talk about InterSystems System Alerting and Monitoring, which offers unified cluster monitoring in a single pane for all your InterSystems IRIS instances. It is built on Prometheus and Grafana, two of the most respected open source offerings available.

Next, we'll dive into the InterSystems Kubernetes Operator, a special controller for Kubernetes that streamlines InterSystems IRIS deployments and management. It's the easiest way to deploy an InterSystems IRIS cluster on-prem or in the Cloud, and we'll show how you can configure mirroring, ECP, sharding and compute nodes, and automate it all.

Finally, we'll discuss how to speed test InterSystems IRIS using the open source Ingestion Speed Test. This tool is available on InterSystems Open Exchange for your own testing and benchmarking. 

   

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Most of us are more or less familiar with Docker. Those who use it like it for the way it lets us easily deploy almost any application, play with it, break something and then restore the application with a simple restart of the Docker container.

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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

 

Running Yape in a container.

2. Yape Container SQLite iostat InterSystems

Extracting and plotting pButtons data including timeframes and iostat.

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