Article
· May 24 15m read
VIP in GCP

If you're running IRIS in a mirrored configuration for HA in GCP, the question of providing a Mirror VIP (Virtual IP) becomes relevant. Virtual IP offers a way for downstream systems to interact with IRIS using one IP address. Even when a failover happens, downstream systems can reconnect to the same IP address and continue working.

The main issue, when deploying to GCP, is that an IRIS VIP has a requirement of IRIS being essentially a network admin, per the docs.

To get HA, IRIS mirror members must be deployed to different availability zones in one subnet (which is possible in GCP as subnets always span the entire region). One of the solutions might be load balancers, but they, of course, cost extra, and you need to administrate them.

In this article, I would like to provide a way to configure a Mirror VIP without using Load Balancers suggested in most other GCP reference architectures.

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If you are a customer of the new InterSystems IRIS® Cloud SQL and InterSystems IRIS® Cloud IntegratedML® cloud offerings and want access to the metrics of your deployments and send them to your own Observability platform, here is a quick and dirty way to get it done by sending the metrics to Google Cloud Platform Monitoring (formerly StackDriver).

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In this article, we’ll build a highly available IRIS configuration using Kubernetes Deployments with distributed persistent storage instead of the “traditional” IRIS mirror pair. This deployment would be able to tolerate infrastructure-related failures, such as node, storage and Availability Zone failures. The described approach greatly reduces the complexity of the deployment at the expense of slightly extended RTO.

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These days the vast majority of applications are deployed on public cloud services. There are multiple advantages, including the reduction in human and material resources needed, the ability to grow quickly and cheaply, greater availability, reliability, elastic scalability, and options to improve the protection of digital assets. One of the most favored options is the Google Cloud. It lets us deploy our applications using virtual machines (Compute Engine), Docker containers (Cloud Run), or Kubernetes (Kubernetes Engine). The first one does not use Docker.

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Like hardware hosts, virtual hosts in public and private clouds can develop resource bottlenecks as workloads increase. If you are using and managing InterSystems IRIS instances deployed in public or private clouds, you may have encountered a situation in which addressing performance or other issues requires increasing the capacity of an instance's host (that is, vertically scaling).

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Hi Community,

Please welcome the new video on InterSystems Developers YouTube:

Deploying InterSystems IRIS Solutions into Kubernetes Google Cloud

https://www.youtube.com/embed/GrRqrn6eq1c
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Hi colleagues!

Every day Johns Hopkins University publishes new data on coronavirus COVID-19 pandemic status.

I built a simple InterSystems IRIS Analytics dashboard using InterSystems IRIS Community Edition in docker deployed on GCP Kubernetes which shows key measures of the disease outbreak.

This dashboard is an example of how information from CSV could be analyzed with IRIS Analytics and deployed to GCP Kubernetes in a form of InterSystems IRIS Community Edition.

Added the interactive map of the USA:

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Hi Devs!

Last weekend I had been testing the newborn csvgen module and was looking for a CSV file to test - thus I came across an interesting datafile on Data.World with Game of Throne episodes statistics. Death statistics. These folks documented all the murders through all the 8 seasons and noted where, who, from what clan with what weapon had killed another one.

So I imported it and made an IRIS Analytics dashboard.

You Know Nothing, Jon Snow | You Know Nothing, Jon Snow | Know ...

Don't worry, Jon, with this dashboard we can figure out something ). See the details below.

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Hi Community,

The new video from Global Summit 2019 is already on InterSystems Developers YouTube:

Automated InterSystems IRIS Cloud Scaling

https://www.youtube.com/embed/4O2Nr5f_0vo
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Hi Developers,

Enjoy watching the new video on InterSystems Developers YouTube:

Resilient Deployments in the Cloud

https://www.youtube.com/embed/dBDQvFRBlZQ
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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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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.

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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.

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Hi all, New user here. I have an instance of Google Cloud Iris Health Community Edition running and am logged in on terminal. I follow the instructions for Google Cloud IRIS Health Community Edition. How do I get to iris? iris status yields nothing, there is no /bin/iris and sudo docker ps -a shows no containers but there probably is a iris container somewhere on the instance? 'Visit the site' yields ERR_CONNECTION_REFUSED

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Breaking news!

InterSystems just announced the availability of the InterSystems IRIS for Health™ Data Platform across the Amazon Web Services, Google Cloud, and Microsoft Azure marketplaces.

With access to InterSystems unified data platform on all three major cloud providers, developers and customers have flexibility to rapidly build and scale the digital applications driving the future of care on the platform of their choice.

To learn more please follow this link.

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Hi Community!

Please welcome a new video on InterSystems Developers YouTube Channel:

Choosing a Public Cloud

https://www.youtube.com/embed/v-0SROpYqRA
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Google Cloud Platform (GCP) provides a feature rich environment for Infrastructure-as-a-Service (IaaS) as a cloud offering fully capable of supporting all of InterSystems products including the latest InterSystems IRIS Data Platform. Care must be taken, as with any platform or deployment model, to ensure all aspects of an environment are considered such as performance, availability, operations, and management procedures. Specifics of each of those areas will be covered in this article.

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