#Best Practices

Hi Developers!

"objectscript.conn" :{
      "ns": "IRISAPP",
      "active": true,
      "docker-compose": {
        "service": "iris",
        "internalPort": 52773
      }

I want to share with you a nice new feature I came across in a new 0.8 release of VSCode ObjectScript plugin by @Dmitry Maslennikov and CaretDev.

The release comes with a new configuration setting "docker-compose" which solves the issue with ports you need to set up to make your VSCode Editor connect to IRIS. It was not very convenient if you had more than one docker container with IRIS running on the same machine. Now, this is solved!

Read below how it works now.

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.

DeepSeeButtons is available on Open Exchange! This tool will generate a diagnostic report of your DeepSee environment.

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:

• Writing code
• REST-enabling it
• Documenting it (as a REST API)

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:

• You always have relevant and useful documentation for external or frontend developers who want to use your REST API
• Specification created in OAS (Swagger) can be imported into a variety of tools allowing editing, client generation, API Management, Unit Testing and automation or simplification of many other tasks
• Improved API architecture. In code-first approach, API is developed method by method so a developer can easily lose track of the overall API architecture, however with the spec-first developer is forced to interact with an API from the position if API consumer which usually helps with designing cleaner API architecture
• Faster development - as all boilerplate code is automatically generated you won't have to write it, all that's left is developing business logic.
• Faster feedback loops - consumers can get a view of the API immediately and they can easier offer suggestions simply by modifying the spec

Let's develop our API in a spec-first approach!

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 :)

Loading your IRIS Data to your Google Cloud Big Query Data Warehouse and keeping it current can be a hassle with bulky Commercial Third Party Off The Shelf ETL platforms, but made dead simple using the iris2bq utility.

Let's say IRIS is contributing to workload for a Hospital system, routing DICOM images, ingesting HL7 messages, posting FHIR resources, or pushing CCDA's to next provider in a transition of care. Natively, IRIS persists these objects in various stages of the pipeline via the nature of the business processes and anything you included along the way. Lets send that up to Google Big Query to augment and compliment the rest of our Data Warehouse data and ETL (Extract Transform Load) or ELT (Extract Load Transform) to our hearts desire.

A reference architecture diagram may be worth a thousand words, but 3 bullet points may work out a little bit better:

• It exports the data from IRIS into DataFrames
• It saves them into GCS as .avro to keep the schema along the data: this will avoid to specify/create the BigQuery table schema beforehands.
• It starts BigQuery jobs to import those .avro into the respective BigQuery tables you specify.

October 17, 2019

Anton Umnikov
Sr. Cloud Solutions Architect at InterSystems
AWS CSAA, GCP CACE

AWS Glue is a fully managed ETL (extract, transform, and load) service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between various data stores.

InterSystems IRIS 2019.1 has been out for a while and I would like to cover some enhancements for handling JSON which might have gone unnoticed. Dealing with JSON as a serialization format is an important part of building modern applications, especially when you interact with REST endpoints.

Since Caché 2017 the SQL engine has included new set of statistics. These record the number of times a query is executed and the time it takes to run.

There's an easy new way to add certificate authority (CA) certificates to your SSL/TLS configurations on InterSystems IRIS 2019.1 (and 2018.1.2) on Windows and Mac. You can ask IRIS to use the operating system's certificate store by entering:

%OSCertificateStore

in the field for "File containing Trusted Certificate Authority X.509 certificate(s)". Here's an image of how to do this in the portal:

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 %Net.SSH.Session class lets you connect to servers using SSH. It's most commonly used with SFTP, especially in the FTP inbound and outbound adaptors.

In this article, I'm going to give a quick example of how to connect to an SSH server using the class, describe your options for authenticating, and how to debug when things go wrong.

Here's an example of making the connection:

This series of articles would cover Python Gateway for InterSystems Data Platforms. Leverage modern AI/ML tools and execute Python code and more from InterSystems IRIS. This project brings you the power of Python right into your InterSystems IRIS environment:

ObjectScript has at least three ways of handling errors (status codes, exceptions, SQLCODE, etc.). Most of the system code uses statuses but exceptions are easier to handle for a number of reasons. Working with legacy code you spend some time translating between the different techniques. I use these snippets a lot for reference. Hopefully they're useful to others as well.