This distraction, "Meshing IrisClusters" with Cilium ClusterMesh, provides database access using the Enterprise Cache Protocol (ECP) from the Eastern Seaboard of the United States to the MidWest on Google Cloud Platform. Ridiculously powerful setup for national/multi-national setups and though the use cases are a plenty, this particular one is a simple example on how to send ETL/ELT the unemployment office.
Studying for an InterSystems certification? Eliminated your entire staff and need to make good on your commitment to AI to support your workload? New to InterSystems products or building your startup on InterSystems technologies? Onboard these intersystems agents compatible with your model and agentic IDE of choice and speak InterSystems technologies like a protocol droid.
So if you just want to cut to the chase, got get your agents @ https://github.com/sween/iris-certified-agents
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For those of us building InterSystems workloads on Kubernetes, we are definitely spoiled with the InterSystems Kubernetes Operator (IKO) doing the heavy lifting and mirroring on day one. Where us spoiled brats jump up and down is when we try to add additional databases/namespaces when we provision from HealthConnect containers on day two, while others get to utilize HealthShare Mirroring for this task, the prerequisite of mirroring HSSYS out of the gate has been somewhat elusive. Here is example on how you can this powerful feature up and running with the employment of IKO and IrisClusters.
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A simple way to connect, explore, and debug InterSytems Cloud Services over JDBC with sqlline.
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While working with the FHIR to OMOP Service, I've seen good FHIR synthetic data being created using commercial LLM's etc, custom tailored for ConditionOnset with the typical amazement on return, but witnessed some questionable trust first hand on a call. This approach also falls short generating gigantic payloads so I can go back to my interests on the backend and ensure smooth data transition.
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A few days before Kubecon, the external-secrets-operator went GA with 1.0.0 and is set to ride shotgun for Kubernetes Secrets Management and put Vault in the backseat. You can glance at the "Providers" list for the solution and immediatley understand that you can leave the "which Secrets Manager" conversation to others while you do your job utilizing external secrets on your IrisCluster workloads, which by my count with the operator and a single IrisCluster is more than a fistful of secrets of different types, even under a single tenant. So let them sprawl, the secrets managers that is, not the secrets.
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If you're running IRIS in a mirrored IrisCluster for HA in Kubernetes, 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 lead in above was stolen (gaffled, jacked, pilfered) from techniques shared to the community for vips across public clouds with IRIS by @Eduard Lebedyuk ...
Articles: ☁ vip-aws | vip-gcp | vip-azure
This version strives to solve the same challenges for IRIS on Kubernetes when being deployed via MAAS, on prem, and possibly yet to be realized using cloud mechanics with Manged Kubernetes Services.
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Hello cpf fans! This distraction I used the "seed" capability in IRIS to provision an entire IrisCluster mirror, 4 maps wide with compute starting from an IRIS.DAT in a galaxy far far away. This is pretty powerful if you have had a great deal of success with a solution running on a monolithic implementation and want it to scale to the outer rim with Kubernetes and the InterSystems Kubernetes Operator. Even though my midichlorian count is admittely low, I have seen some hardcore CACHE hackers shovel around DATS, compact and shrink and update their ZROUTINES, so this same approach could also be helpful shrinking and securing your containerized workload too. If you squint and feel all living things around you, you can see a glimpse of in place (logical) mirroring in the future as a function of the operator and a migration path to a fully operational mirrored Death Star as the workload matures.
Run InterSystems IRIS inside of Home Assistant, as an add-on. Before you dismiss this article possibly under the guise that this is just a gimmick, Id like you to step back and take a look at how easy it is to launch IRIS based applications using this platform. If you look at Open Exchange, you will see dozens of dozens of applications worthy of launching while they are basically hung out to dry as gitware, and launchable if you want to get into a laptop battle with containerd or Docker. With a simple git repo, and a specification, you can now build your app on IRIS, and make it launchable through a marketplace with limited hassle to your end users. Run it along side Ollama and the LLM/LAM implementations, expose anything in IRIS as a sensor or expose an endpoint for interaction in your IRIS app to interact with anything you've connected to HAOS. Wanna restart an IRIS production with a flick of a physical switch or Assisted AI? You can do it with this add-on, or your own, right alongside the home automation hackers.
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Here is an option for your headspace if you are designing an multi-cluster architecture and the Operator is an FTE to the design. You can run the Operator from a central Kubernetes cluster (A), and point it to another Kubernetes cluster (B), so that when the apply an IrisCluster to B the Operator works remotely on A and plans the cluster accordingly on B. This design keeps some resource heat off the actual workload cluster, spares us some serviceaccounts/rbac and gives us only one operator deployment to worry about so we can concentrate on the IRIS workloads.
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Rancher Government Hauler streamlines deploying and maintaining InterSystems container workloads in air-gapped environments by simplifying how you package and move required assets. It treats container images, Helm charts, and other files as content and collections, letting you fetch, store, and distribute them declaratively or via CLI — without changing your existing workflows. Meaning your charts and what have yous, can have conditionals on your pull locations in Helm values, etc.
If you have been tracking how HealthShare is being deployed via IPM Packages, you can certainly appreciate the adoption of OCI compliance storage for the packages themselves using ORAS... which is core to the Hauler solution.
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If you are in the business of building a robust High Availability, Disaster Recovery or Stamping multiple environments rapidly and in a consistent manner Karmada may just be the engine powering your Cloning Facility.
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KWOK, Kubernetes WithOut Kubelet, is a lightweight tool that simulates nodes and pods—without running real workloads—so you can quickly test and scale IrisCluster behavior, scheduling, and zone assignment. For those of you wondering what value is in this without the IRIS workload, you will quickly realize it when you play with your Desk Toys awaiting nodes and pods to come up or get the bill for provisioning expensive disk behind the pvc's for no other reason than just to validate your topology.
Here we will use it to simulate an IrisCluster and target a topology across 4 zones, implementing high availability mirroring across zones, disaster recovery to an alternate zone, and horizontal ephemeral compute (ecp) to a zone of its own. All of this done locally, suitable for repeatable testing, and a valuable validation check mark on the road to production.
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Though trivial, Id like to go multi-cloud with the stretched IrisCluster for a couple of reasons to socialize the power of Wireguard when it supplies the network for a properly zoned IrisCluster by adding another mirror role to Amazon Web Services in the Western United States based datacenter in Oregon.
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I am giving this distraction the code name "Compliment Sandwich" for a reason yet to be realized, but I'd rather the community go right for the jugular shooting holes in a solution that implements wireguard based connectivity for our workloads in general, as I would like to refine it as a fall project leading up to KubeCon in Atlanta and if I miss the mark, Ill get it done before Amsterdam.
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This part of the OMOP Journey, we reflect before attempting to challenge Scylla on how fortunate we are that InterSystems OMOP transform is built on the Bulk FHIR Export as the source payload. This opens up hands off interoperability with the InterSystems OMOP transform across several FHIR® vendors, this time with the Google Cloud Healthcare API.
Here were going to take 3 laps of your time and demonstrate how I wired up my Racing SIM to IRIS for "As Real Time as It Gets" Metrics reporting. I missed the window for the contest, which happens quite often, but I still ended up 3rd I think in the demo race in the video below.
Below are the technical ingredients for this demonstration for a salad you can post on Instragram.
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This part of the OMOP Journey, we reflect before attempting to challenge Scylla on how fortunate we are that InterSystems OMOP transform is built on the Bulk FHIR Export as the source payload. This opens up hands off interoperability with the InterSystems OMOP transform across several FHIR® vendors, including Amazon Web Services HealthLake.
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Along this OMOP Journey, from the OHDSI book to Achilles, you can begin to understand the power of the OMOP Common Data Model when you see the mix of well written R and SQL deriving results for large scale analytics that are shareable across organizations. I however do not have a third normal form brain and about a month ago on the Journey we employed Databricks Genie to generate sql for us utilizing InterSystems OMOP and Python interoperability.
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Lets zero in on the use of the word professional and put it into some context. It was written by industry experts who wrapped it up in a fee based service with support and some guardrails around flexible options to contribute to its behavior. I feel its an important distinction to either an open or home grown solution (though it may do the same thing) to consider it to scale or provide mission critical value on the other side of it.
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So if you are following from the previous post or dropping in now, let's segway to the world of eBPF applications and take a look at Parca, which builds on our brief investigation of performance bottlenecks using eBPF, but puts a killer app on top of your cluster to monitor all your iris workloads, continually, cluster wide!
Continous Profiling with Parca, IRIS Workloads Cluster Wide
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I attended Cloud Native Security Con in Seattle with full intention of crushing OTEL day, then perusing the subject of security applied to Cloud Native workloads the following days leading up to CTF as a professional excercise. This was happily upended by a new understanding of eBPF, which got my screens, career, workloads, and atitude a much needed upgrade with new approaches to solving workload problems.
So I made it to the eBPF party and have been attending clinic after clinic on the subject ever since, here I would like to "unbox" eBPF as a technical solution, mapped directly to what we do in practice (even if its a bit off), and step through eBPF through my experimentation on supporting InterSystems IRIS Workloads, particularly on Kubernetes, but not necessarily void on standalone workloads.
