Requirements

Kubernetes

KEDA is designed, tested and supported to be run on any Kubernetes cluster that runs Kubernetes v1.17.0 or above.

Cluster Capacity

The KEDA runtime require the following resources in a production-ready setup:

These are used by default when deploying through YAML.

💡 For more info on CPU and Memory resource units and their meaning, see this link.

Firewall

KEDA requires to be accessible inside the cluster to be able to autoscale.

Here is an overview of the required ports that need to be accessible for KEDA to work:

High Availability

KEDA does not provide support for high-availability due to upstream limitations.

Here is an overview of all KEDA deployments and the supported replicas:

HTTP Timeouts

Some scalers issue HTTP requests to external servers (i.e. cloud services). 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.

You can override this default by setting the KEDA_HTTP_DEFAULT_TIMEOUT environment variable to your desired timeout in milliseconds. For example, on Linux/Mac/Windows WSL2 operating systems, you’d use this command to set to 1 second:

export KEDA_HTTP_DEFAULT_TIMEOUT=1000

And on Windows Powershell, you’d use this command:

$env:KEDA_HTTP_DEFAULT_TIMEOUT=1000

All applicable scalers will use this timeout. Setting a per-scaler timeout is currently unsupported.

Kubernetes Client Parameters

The Kubernetes client config used within KEDA Metrics Adapter can be adjusted by passing the following command-line flags to the binary:

Configure MaxConcurrentReconciles for Controllers

To implement internal controllers KEDA uses controller-runtime project, that enables configuration of MaxConcurrentReconciles property, ie. the maximum number of concurrent reconciles which can be run for a controller.

KEDA Operator exposes properties for specifying MaxConcurrentReconciles for following controllers/reconcilers:

• ScaledObjectReconciler - responsible for watching and managing ScaledObjects, ie. validates input trigger specification, starts scaling logic and manages dependent HPA.
• ScaledJobReconciler - responsible for watching and managing ScaledJobs and dependent Kubernetes Jobs

KEDA Metrics Server exposes property for specifying MaxConcurrentReconciles for MetricsScaledObjectReconciler, that manages Metrics Names exposes by KEDA and which are being consumed by Kubernetes server and HPA controller.

To modify this properties you can set environment variables on both KEDA Operator and Metrics Server Deployments: