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

It uses a CRD (custom resource definition) and the Kubernetes metric server so you will have to use a Kubernetes version which supports these.

💡 Kubernetes v1.16 is supported with KEDA v2.4.0 or below

KEDA allows you to use multiple triggers as part of the same ScaledObject or ScaledJob.

By doing this, your autoscaling becomes better:

• All your autoscaling rules are in one place
• You will not have multiple ScaledObject’s or ScaledJob’s interfering with each other

KEDA will start scaling as soon as when one of the triggers meets the criteria. Horizontal Pod Autoscaler (HPA) will calculate metrics for every scaler and use the highest desired replica count to scale the workload to.

We recommend not to combine using KEDA’s ScaledObject with a Horizontal Pod Autoscaler (HPA) to scale the same workload.

They will compete with each other resulting given KEDA uses Horizontal Pod Autoscaler (HPA) under the hood and will result in odd scaling behavior.

If you are using a Horizontal Pod Autoscaler (HPA) to scale on CPU and/or memory, we recommend using the CPU scaler & Memory scaler scalers instead.

KEDA will scale a container using metrics from a scaler, but unfortunately there is no scaler today for HTTP workloads out-of-the-box.

We do, however, provide some alternative approaches:

• Use our HTTP add-on scaler which is currently in experimental stage (GitHub)
• Use Prometheus scaler to create scale rule based on metrics around HTTP events
  • Read Anirudh Garg’s blog post to learn more.

By default you can’t run KEDA with readOnlyRootFilesystem=true because Metrics adapter generates self-signed certificates during deployment and stores them on the root file system. To overcome this, you can create a secret/configmap with a valid CA, cert and key and then mount it to the Metrics Deployment. To use your certificate, you need to reference it in the container args section, e.g.:

args:
  - '--client-ca-file=/cabundle/service-ca.crt'
  - '--tls-cert-file=/certs/tls.crt'
  - '--tls-private-key-file=/certs/tls.key'

It is also possible to run KEDA with readOnlyRootFilesystem=true by creating an emptyDir volume and mounting it to the path where, by default, metrics server writes its generated cert. The corresponding helm command is:

helm install keda kedacore/keda --namespace keda --set 'volumes.metricsApiServer.extraVolumes[0].name=keda-volume' --set 'volumes.metricsApiServer.extraVolumeMounts[0].name=keda-volume' --set 'volumes.metricsApiServer.extraVolumeMounts[0].mountPath=/apiserver.local.config/certificates/' --set 'securityContext.metricServer.readOnlyRootFilesystem=true'

By default Keda listens on TLS v1.1 and TLSv1.2, with the default Golang ciphersuites. In some environments, these ciphers may be considered less secure, for example CBC ciphers.

As an alternative, you can configure the minimum TLS version to be v1.3 to increase security. Since all modern clients support this version, there should be no impact in most scenarios.

You can set this with args - e.g.:

args:
  - '--tls-min-version=VersionTLS13'

The target metric value is used by the Horizontal Pod Autoscaler (HPA) to make scaling decisions.

The current target value on the Horizontal Pod Autoscaler (HPA) often does not match with the metrics on the system you are scaling on. This is because of how the Horizontal Pod Autoscaler’s (HPA) scaling algorithm works.

By default, KEDA scalers use average metrics (the AverageValue metric type). This means that the HPA will use the average value of the metric between the total amount of pods. As of KEDA v2.7, ScaledObjects also support the Value metric type. You can learn more about it here.

Kubernetes allows you to autoscale based on custom & external metrics which are fundamentally different:

• Custom metrics are metrics that come from applications solely running on the Kubernetes cluster (Prometheus)
• External metrics are metrics that represent the state of an application/service that is running outside of the Kubernetes cluster (AWS, Azure, GCP, Datadog, etc.)

Because KEDA primarily serves metrics for metric sources outside of the Kubernetes cluster, it uses external metrics and not custom metrics.

This is why KEDA registers the v1beta1.external.metrics.k8s.io namespace in the API service. However, this is just an implementation detail as both offer the same functionality.

Read about the different metric APIs or this article by Google Cloud to learn more.

Unfortunately, you cannot do that.

Kubernetes currently only supports one metric server serving external.metrics.k8s.io metrics per cluster. This is because only one API Service can be registered to handle external metrics.

If you want to know what external metric server is currently registered, you can use the following command:

~  kubectl  get APIService/v1beta1.external.metrics.k8s.io
NAME                              SERVICE                                       AVAILABLE   AGE
v1beta1.external.metrics.k8s.io   keda-system/keda-operator-metrics-apiserver   True        457d

Once a new metric server is installed, it will overwrite the existing API Server registration and take over the v1beta1.external.metrics.k8s.io namespace. This will cause the previously installed metric server to be ignored.

There is an open proposal to allow multiple metric servers in the same cluster, but it’s not implemented yet.

Unfortunately, you cannot do that.

This is a limitation that is because Kubernetes does not allow you to run multiple metric servers in the same cluster that serve external metrics.

Also, KEDA does not allow you to share a single metric server across multiple operator installations.

Learn more in the “Can I run multiple metric servers serving external metrics in the same cluster?” FAQ entry.

There are several ways to get involved.

• Pick up an issue to work on. A good place to start might be issues which are marked as Good First Issue or Help Wanted
• We are always looking to add more scalers.
• We are always looking for more samples, documentation, etc.
• Please join us in our weekly standup.

There are a few reasons for this:

• Run functions on-premises (potentially in something like an ‘intelligent edge’ architecture)
• Run functions alongside other Kubernetes apps (maybe in a restricted network, app mesh, custom environment, etc.)
• Run functions outside of Azure (no vendor lock-in)
• Specific need for more control (GPU enabled compute clusters, policies, etc.)