The KEDA Blog
Updates, tutorials, and more
November 4, 2020
A year ago, we were excited to announce our 1.0 release with a core set of scalers, allowing the community to start autoscaling Kubernetes deployments. We were thrilled with the response and encouraged to see many users leveraging KEDA for event driven and serverless scale within any Kubernetes cluster. With KEDA, any container can scale to zero and burst scale based directly on event source metrics. While KEDA was initially started by Microsoft & Red Hat we have always strived to be an open & vendor-neutral project in order to support everybody who wants to scale applications.
September 11, 2020
Today, we are happy to share that our first beta version of KEDA 2.0 is available! 🎊 Highlights With this release, we are shipping majority of our planned features. Here are some highlights: Making scaling more powerful Introduction of ScaledJob (docs) Introduction of Azure Log Analytics scaler (docs) Support for scaling Deployments, Stateful Sets and/or any Custom Resources (docs) Support for scaling on standard resource metrics (CPU/Memory) Support for multiple triggers in a single ScaledObject (docs) Support for scaling to original replica count after deleting ScaledObject (docs) Support for controling scaling behavior of underlying HPA Easier to operate KEDA Introduction of readiness and liveness probes Introduction of Prometheus metrics for Metrics Server (docs) Provide more information when quering KEDA resources with kubectl Extensibility Introduction of External Push scaler (docs) Introduction of Metric API scaler (docs) Provide KEDA client-go library For a full list of changes, we highly recommend going through our changelog!
March 31, 2020
Over the past year, We’ve been contributing to Kubernetes Event-Driven Autoscaling (KEDA), which makes application autoscaling on Kubernetes dead simple. If you have missed it, read about it in our “Exploring Kubernetes-based event-driven autoscaling (KEDA)" blog post. We started the KEDA project to address an essential missing feature in the Kubernetes autoscaling story. Namely, the ability to autoscale on arbitrary metrics. Before KEDA, users were only able to autoscale based on metrics such as memory and CPU usage.