Troubleshooting Latest

How to address commonly encountered KEDA issues

Why is Kubernetes unable to get metrics from KEDA?

If while setting up KEDA, you get an error: ( status FailedDiscoveryCheck with a message: no response from https://ip:443: Get https://ip:443: net/http: request canceled while waiting for connection (Client.Timeout exceeded while awaiting headers).

One of the reason for this can be that you are behind a proxy network.

Before you start

  • Make sure no network policies are blocking traffic

Check the status:

Find the api service name for the service keda/keda-metrics-apiserver:

kubectl get apiservice --all-namespaces

Check for the status of the api service found in previous step:

kubectl get apiservice <apiservicename> -o yaml


kubectl get apiservice -o yaml

If the status is False, then there seems to be an issue and proxy network might be the primary reason for it.

Solution for self-managed Kubernetes cluster:

Find the cluster IP for the keda-metrics-apiserver and keda-operator-metrics:

kubectl get services --all-namespaces

In the /etc/kubernetes/manifests/kube-apiserver.yaml - add the cluster IPs found in the previous step in no_proxy variable.

Reload systemd manager configuration:

sudo systemctl daemon-reload

Restart kubelet:

sudo systemctl restart kubelet

Check the API service status and the pods now. Should work!

Solution for managed Kubernetes services:

In managed Kubernetes services you might solve the issue by updating firewall rules in your cluster.

E.g. in GKE private cluster add port 6443 (kube-apiserver) to allowed ports in master node firewall rules.

Why is my ScaledObject paused?

When KEDA has upstream errors to get scaler source information it will keep the current instance count of the workload unless the fallback section is defined.

This behavior might feel like the autoscaling is not happening, but in reality, it is because of problems related to the scaler source.

You can check if this is your case by reviewing the logs from the KEDA pods where you should see errors in both our Operator and Metrics server. You can also check a status of the ScaledObject (READY and ACTIVE condition) by running following command:

$ kubectl get scaledobject MY-SCALED-OBJECT

Why does Google Kubernetes Engine (GKE) 1.16 fail to fetch external metrics?

If you are running Google Kubernetes Engine (GKE) version 1.16, and are receiving the following error:

unable to fetch metrics from external metrics API: <METRIC> is forbidden: User "system:vpa-recommender" cannot list resource "<METRIC>" in API group "" in the namespace "<NAMESPACE>": RBAC: "external-metrics-reader" not found

You are almost certainly running into a known issue.

The workaround is to recreate the external-metrics-reader role using the following YAML:

kind: ClusterRole
  name: external-metrics-reader
- apiGroups:
  - ""
  - "*"
  - list
  - get
  - watch

The GKE team is currently working on a fix that they expect to have out in version >= 1.16.13.

Why does KEDA operator error with NoCredentialProviders

If you are running KEDA on AWS using IRSA or KIAM for pod identity and seeing the following error messages:

  Type     Reason                      Age                From           Message
  ----     ------                      ----               ----           -------
  Normal   KEDAScalersStarted          31s                keda-operator  Started scalers watch
  Normal   KEDAScaleTargetDeactivated  31s                keda-operator  Deactivated apps/v1.Deployment default/my-event-based-deployment from 1 to 0
  Normal   ScaledObjectReady           13s (x2 over 31s)  keda-operator  ScaledObject is ready for scaling
  Warning  KEDAScalerFailed            1s (x2 over 31s)   keda-operator  NoCredentialProviders: no valid providers in chain. Deprecated.
           For verbose messaging see aws.Config.CredentialsChainVerboseErrors

And the operator logs:

2021-11-02T23:50:29.688Z    ERROR    controller    Reconciler error    {"reconcilerGroup": "", "reconcilerKind": "ScaledObject", "controller": "scaledobject", "name": "my-event-based-deployment-scaledobject", "namespace": "default"
, "error": "error getting scaler for trigger #0: error parsing SQS queue metadata: awsAccessKeyID not found"}

This means hat the KEDA operator is not receiving valid credentials, even before attempting to assume the IAM role associated with the scaleTargetRef.

Some things to check:

  • Ensure the keda-operator deployment has the annotation under deployment.spec.template.metadata not deployment.metadata - if using KIAM
  • Ensure the keda-operator serviceAccount is annotated - if using IRSA
  • Check kiam-server logs, successful provisioning of credentials looks like: kube-system kiam-server-6bb67587bd-2f47p kiam-server {"level":"info","msg":"found role","pod.iam.role":"arn:aws:iam::1234567890:role/my-service-role","pod.ip":"","time":"2021-11-05T03:13:34Z"}.
    • Use grep to filter the kiam-server logs, searching for the keda-operator pod ip.

Why is Helm not able to upgrade to v2.2.1 or above?

Our initial approach to manage CRDs through Helm was not ideal given it didn’t update existing CRDs.

This is a known limitation of Helm:

There is no support at this time for upgrading or deleting CRDs using Helm. This was an explicit decision after much community discussion due to the danger for unintentional data loss. Furthermore, there is currently no community consensus around how to handle CRDs and their lifecycle. As this evolves, Helm will add support for those use cases.

As of v2.2.1 of our Helm chart, we have changed our approach so that we automatically managing the CRDs through our Helm chart.

Due to this transition, it can cause upgrade failures if you started using KEDA before v2.2.1 and will cause errors during upgrades such as the following:

Error: UPGRADE FAILED: rendered manifests contain a resource that already exists. Unable to continue with update: CustomResourceDefinition “” in namespace "" exists and cannot be imported into the current release: invalid ownership metadata; label validation error: missing key “”: must be set to “Helm”; annotation validation error: missing key “”: must be set to “keda”; annotation validation error: missing key “”: must be set to “keda”

In order to fix this, you will need to manually add the following attributes to our CRDs:

  • Helm label
  • keda annotation
  • keda annotation

Future upgrades should work seamlessly.

Why is KEDA API metrics server failing when Istio is installed?

While setting up KEDA, you get an error: ( status FailedDiscoveryCheck and you have Istio installed as service mesh in your cluster.

This can lead to side effects like not being able to delete namespaces in your cluster. You will see an error like:

NamespaceDeletionDiscoveryFailure - Discovery failed for some groups, 1 failing: unable to retrieve the complete list of server APIs: the server is currently unable to handle the request

Check the setup

In the following we assume that KEDA is installed in the namespace keda.

Check the KEDA API service status

Find the api service name for the service keda/keda-metrics-apiserver:

kubectl get apiservice --all-namespaces

Check for the status of the api service found in previous step:

kubectl get apiservice <apiservicename> -o yaml


kubectl get apiservice -o yaml

If the status is False, then there seems to be an issue with the KEDA installation.

Check Istio installation

Check if Istio is installed in your cluster:

kubectl get svc -n istio-system

If Istio is installed you get a result like:

NAME                   TYPE           CLUSTER-IP       EXTERNAL-IP     PORT(S)                                      AGE
istio-ingressgateway   LoadBalancer   15021:31585/TCP,80:31669/TCP,443:30464/TCP   3d
istiod                 ClusterIP   <none>          15010/TCP,15012/TCP,443/TCP,15014/TCP        3d

Check KEDA namespace labels

Check the KEDA namespace labels:

kubectl describe ns keda

If Istio injection is enabled there is no label stating istio-injection=disabled.

In this setup the sidecar injection of Istio prevents the api service of KEDA to work properly.


To prevent the side-car injection of Istio we must label the namespace accordingly. This can be achieved via setting the label istio-injection=disabled to the namespace:

kubectl label namespace keda istio-injection=disabled

Check that the label is set via kubectl describe ns keda

Install KEDA into the namespace keda and re-check the status of the api service which should now have the status True.