Hi Developers,

Writing a script for the application deployment can be very interesting to ensure rapid deployment without forgetting anything.
config-api is a library to help developers to write configuration scripts based on a JSON document.

Implemented features :

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.  

We released a new version of ZPM (Package Manager)

New in ZPM 0.2.14 release:

• Publishing timeout
• Embedded vars usage in module parameters
• Package installation from Github repo
• Transaction support for install, load and publish.

See the details below.

Hi ,

I have a requirement to programmatically fetch and update a Business Process Setting . It's a custom property , added in extended class .

I am trying find a sample, but no luck. 

Could any one help me ? 

Hey Developers,

Please welcome the new session recording from the InterSystems AI+ML Summit 2021:

⏯ AI+ML Summit Convergent Analytics – Cross-Industry Stream

Hi Community,

Please welcome the new video on InterSystems Developers YouTube:

⏯ Extending Interoperability with PEX & Containers

Hello community,

Our organization is looking to expand our Coding Standards to be more in alignment with best practices.

I was wondering if the there are developers here that would not mind sharing any existing documents. Our scope would be this document be applicable to new hires learning Cache all the way up the chain.

Thanks in advance,

-SH

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.

where can I find a list of all command line utilities with a short description?

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.

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. 

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.

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).

Last time we launched an IRIS application in the Google Cloud using its GKE service.

And, although creating a cluster manually (or through gcloud) is easy, the modern Infrastructure-as-Code (IaC) approach advises that the description of the Kubernetes cluster should be stored in the repository as code as well. How to write this code is determined by the tool that’s used for IaC.

In the case of Google Cloud, there are several options, among them Deployment Manager and Terraform. Opinions are divided as to which is better: if you want to learn more, read this Reddit thread Opinions on Terraform vs. Deployment Manager? and the Medium article Comparing GCP Deployment Manager and Terraform.