Kubernetes (commonly stylized as k8s) is an open-source container-orchestration system for automating application deployment, scaling, and management. It aims to provide a platform for automating deployment, scaling, and operations of application containers across clusters of hosts. It works with a range of container tools, including Docker.
Monitoring your IRIS deployment is crucial. With the deprecation of System Alert and Monitoring (SAM), a modern, scalable solution is necessary for real-time insights, early issue detection, and operational efficiency. This guide covers setting up Prometheus and Grafana in Kubernetes to monitor InterSystems IRIS effectively.
This guide assumes you already have an IRIS cluster deployed using the InterSystems Kubernetes Operator (IKO), which simplifies deployment, integration and mangement.
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.
eBee Steps with eBPF and InterSystems IRIS Workloads
IAM - InterSystems API Manager is a great tool for monitoring your traffic. If you are trying to use it in your Kubernetes cluster you may have tried doing a deployment similar to this one:
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
In today's rapidly evolving threat landscape, organizations deploying mission-critical applications must implement robust security architectures that protect sensitive data while maintaining high availability and performance. This is especially crucial for enterprises utilizing advanced database management systems like InterSystems IRIS, which often powers applications handling highly sensitive healthcare, financial, or personal data.
The Istio Service Mesh is commonly used to monitor communication between services in applications. The "battle-tested" sidecar mode is its most common implementation. It will add a sidecar container to each pod you have in your namespace that has Istio sidecar injection enabled.
Regardless of whether an instance of IRIS is in the cloud or not, high availability and disaster recovery are always important considerations. While IKO already allows for the use of NodeSelectors to enforce the scheduling of IRISCluster nodes across multiple zones, multi-region k8s clusters are generally not recommended or even supported in the major CSP's managed Kubernetes solutions. However, when discussing HA and DR for IRIS, we may want to have an async member in a completely separate region, or even in a different cloud provider altogether.
All pods are assigned a Quality of Service (QoS). These are 3 levels of priority pods are assigned within a node.
The levels are as following:
1) Guaranteed: High Priority
2) Burstable: Medium Priority
3) BestEffort: Low Priority
It is a way of telling the kubelet what your priorities are on a certain node if resources need to be reclaimed. This great GIF below by Anvesh Muppeda explains it.
InterSystems Kubernetes Operator (IKO) 3.8 is now Generally Available. IKO 3.8 adds new functionality along with numerous bug fixes and security updates. Highlights include:
https://www.youtube.com/embed/GUbe6Iwt9T4 [This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]
This is like my first post in this community. Let's see how this turns out. I have a question about the Intersystems Kubernetes Operator and the deployment of the webgateways.
I am responsible for the hosting and deployment of the apps. For the future we are planning to host our application in a kubernetes cluster. I am using the IKO for this. I am using webgateways, for external access as separate pods. And sidecar containers for internal access, like the management portal.
Just like a knockout punch, without giving the opponent a chance, Kubernetes, as an open source platform, has a universe of opportunities due to its availability (i.e., the ease of finding support, services and tools). It is a platform that can manage jobs and services in containers, which greatly simplifies the configuration and automation of these processes.
But let's justify the title image and give the tool in question the “correct” name: InterSystems Kubernetes Operator.
Kubernetes horizontal pod auto-scaling (HPA) is the key to handle the unpredictable compute workload in healthcare systems. IKO helps orchestrating the IRIS container deployment in Kubernetes including the capability to configure HPA. This demo uses XSLT processing as an example to showcase this type of elasticity.
https://www.youtube.com/embed/npCs5_yX_xg [This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]
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Open Exchange app
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