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

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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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InterSystems IRIS 2024 recently introduced the vector types.
This addition empowers developers to work with vector search, enabling efficient similarity searches, clustering, and a range of other applications.
In this article, we will delve into the intricacies of vector types, explore their applications, and provide practical examples to guide your implementation.

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