Open Data Hub is an open source project providing an end-to-end artificial intelligence and machine learning (AI/ML) platform that runs on Red Hat OpenShift. As we explained in our previous article, we see real potential and value in the Kubeflow project, and we’ve enabled Kubeflow 0.7 on RedHat OpenShift 4.2. Kubeflow installs multiple AI/ML components and requires Istio to control and route service traffic.
As part of the Open Data Hub project, we’ve also integrated Kubeflow with Red Hat OpenShift Service Mesh. In this article, we present Red Hat OpenShift Service Mesh as an alternative to the native Kubeflow Istio installation, especially for users who already have OpenShift Service Mesh installed on their cluster.
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Metrics, traces, and logs might be the Three Pillars of Observability, as you’ve certainly already heard. This mantra helps us focus our mindset around observability, but it is not a religion. “There is so much more data that can help us have insight into our running systems,” said Frederic Branczyk at KubeCon last year.
These three kind of signals do have their specificities, but they also have common denominators that we can generalize. They could all appear on a virtual timeline and they all originate from a workload, so they are timed and sourced, which is a good start for enabling correlation. If there’s anything as important as knowing the signals that a system can emit, it’s knowing the relationships between those signals and being able to correlate one with another, even when they’re not strictly of the same nature. Ultimately, we can postulate that any sort of signal that is timed and sourced is a good candidate for correlation as well, even if we don’t have hard links between them.
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The Istio service mesh is a powerful tool for building a service mesh. If you don’t know about Istio yet, have a look at the Introduction to Istio series of articles or download the ebook Introducing Istio Service Mesh for Microservices.
The power of Istio comes with the cost of some complexity at configuration and runtime. To help this, the Kiali project provides observability of the mesh and the services in the mesh. Kiali visualizes the mesh with its services and workloads. It indicates the health of the mesh and shows hints about applied configuration options. You can then drill in on individual services or settings to view details.
This post describes how to use Kiali to observe what the microservices in your Istio service mesh are doing, validate the Istio configuration, and see any issues.
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