For example, on Linux/Mac/Windows WSL2 operating systems, you’d use this command to set to 1 second: You can override this default by setting the KEDA_HTTP_DEFAULT_TIMEOUT environment variable to your desired timeout in milliseconds. Each applicable scaler uses its own dedicated HTTP client with its own connection pool, and by default each client is set to time out any HTTP request after 3 seconds.
Some scalers issue HTTP requests to external servers (i.e. Multiple replicas will not improve the performance of KEDA, it could only reduce a downtime during a failover.
The rest will be standing by, which may reduce downtime during a failure. While you can run multiple replicas of our operator, only one operator instance will be active. However, you can only run one active metric server in a Kubernetes cluster serving which has to be the KEDA metric server. You can run multiple replicas of our metrics sever, and it is recommended to add the -enable-aggregator-routing=true CLI flag to the kube-apiserver so that requests sent to our metrics servers are load balanced. Here is an overview of all KEDA deployments and the HA notes: Deployment
#Arangodb high availability full
KEDA does not provide full support for high-availability due to upstream limitations. Only required for Google Cloud because it uses Control Plane → port 6443 on the Pod IP range for communication Required for all platforms because it uses Control Plane → port 443 on the Service IP range communication. Used by Kubernetes API server to get metrics Here is an overview of the required ports that need to be accessible for KEDA to work: Port KEDA requires to be accessible inside the cluster to be able to autoscale. 💡 For more info on CPU and Memory resource units and their meaning, see this link. These are used by default when deploying through YAML. The KEDA runtime require the following resources in a production-ready setup: Deployment
However, maintainers can decide to extend this by supporting more minor versions based on the required CRDs being used but there is no guarantee.Īs a reference, this compatibility matrix shows supported k8s versions per KEDA version: KEDA The supported window of Kubernetes versions with KEDA is known as “N-2” which means that KEDA will provide support for running on N-2 at least.
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