Hi Ron, Thanks for the reply!
> 1 - It seems for flink job using flink operator to realize autoscaling, the > only option to realize autoscaling is to enable the Autoscaler feature, and > KEDA won’t work, right? What is KEDA mean? -> KEDA is a Kubernetes based Event Driven Autoscaler. I found some examples using Flink’s previous Reactive mode + KEDA to realize autoscaling. So if Autoscaler is enabled, is it still necessary to create KEDA resources? I think TaskManager instances are created and destroyed by the Flink JobManager now, and aren’t in a replication controller, so they can't be “scaled up” using traditional Kubernetes techniques like KEDA. Could you please help confirm? Thank you! -------------- > 2 - I noticed from the document that we need to upgrade to flink version of > 1.17 to use Autoscaler. But I also noticed that the updated version for flink > operator is 1.7 now. Shall we upgrade from 1.5.0 to 1.7 to enable Autoscaler? I have checked the flink-kubernetes-operator projection pom for release-1.5 branch, the dependency flink version is 1.16.1. So I recommend you update your flink-kubernetes-operator to 1.6. The latest stable release is 1.6. -> Thank you, so the dependency flink version of flink-kubernetes-operator version-1.6 is 1.17? Our current flink version is 1.16.1, so does it mean we need to: 1 – Update flink version to 1.17 2 – Update flink operator version to 1.6? Could you please help confirm? Thank you! ------------------ Oh I have another question from the email, please have a look: 3 – Could you please provide a list of metrics observed by Autoscaler automatically? * Will it include CPU load, memory, throughput and kafka consumer lag? * Is there any configurations related to kafka consumer lag that we can setup to scale job by making Autoscaler monitor it? Like some threshold? Thanks a lot for the help!! Best, Lijuan
