One way to optimize query performance is to use query parallelism on a per-query or system-wide basis (a standard feature).
This is a technique for dividing the execution of a particular query among processors on a multi-processor system. The query optimizer will execute parallel processing only if there is a possibility of benefiting from parallel processing. Parallel processing is only applicable to SELECT statements.
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.
"Haul" a Portable Registry for Airgapped IrisClusters
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.
I was facing the same issue as Jerry faced when connecting IRIS to SQL server. My ODBC connection is configured to authenticate via windows authentication.
If one of your packages on OEX receives a review, you get notified by OEX only of YOUR own package. The rating reflects the experience of the reviewer with the status found at the time of review. It is kind of a snapshot and might have changed meanwhile. Reviews by other members of the community are marked by * in the last column.
While working with external languages for IRIS (such as Python and Node.js), one of the first things you must accomplish is making a connection to an IRIS instance.
If one of your packages on OEX receives a review, you get notified by OEX only of YOUR own package. The rating reflects the experience of the reviewer with the status found at the time of review. It is kind of a snapshot and might have changed meanwhile. Reviews by other members of the community are marked by * in the last column.
When I was younger (a question about exactly how much younger is outside the scope of this article), the word “token” meant fun. You see, just a couple of times a year, I would get to go to an arcade and play some amusing video games with my friends.
Mirror Your Database Across the Galaxy with Seeding
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.
Window functions in InterSystems IRIS let you perform powerful analytics — like running totals, rankings, and moving averages — directly in SQL.
They operate over a "window" of rows related to the current row, without collapsing results like GROUP BY.
This means you can write cleaner, faster, and more maintainable queries — no loops, no joins, no temp tables.
In this article let's understand the mechanics of window functions by addressing some common data analisys tasks.
When exporting using the Export() method of the %Library.Global class, if the export format (fourth argument: OutputFormat) is set to 7, "Block format/Caché block format (%GOF)," mapped globals cannot be exported (only globals in the default global database of the namespace are exported). To export mapped globals in "Block format/Caché block format (%GOF)," specify the database directory to which you want to map them in the first parameter of %Library.Global.Export().
as a developer who uses Cache as DB for couple of projects I'm using REST API's every time, so knowing how to consume a resource from REST API and in my opinion its crucial to know how to consume external REST Api's using %Net.HttpRequest because its enables integration with modern web application and serveries and its crucial skill for a backend developer who love and used Cache as DB
When I started my journey with InterSystems IRIS, especially in Interoperability, one of the initial and common questions I had was: how can I run something on an interval or schedule? In this topic, I want to share two simple classes that address this issue. I'm surprised that some similar classes are not located somewhere in EnsLib. Or maybe I didn't search well? Anyway, this topic is not meant to be complex work, just a couple of snippets for beginners.
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