Horizontal Pod Autoscaler (HPA) is a component that can automatically scale the number of pods in Kubernetes clusters. This topic describes the common issues that may occur when you use HPA and the solutions.
Issue 1. HPA cannot fetch monitoring metrics
Name: kubernetes-tutorial-deployment Namespace: default Labels: <none> Annotations: <none> CreationTimestamp: Mon, 10 Jun 2019 11:46:48 +0530 Reference: Deployment/kubernetes-tutorial-deployment Metrics: ( current / target ) resource cpu on pods (as a percentage of request): <unknown> / 2% Min replicas: 1 Max replicas: 4 Deployment pods: 1 current / 0 desired Conditions: Type Status Reason Message ---- ------ ------ ------- AbleToScale True SucceededGetScale the HPA controller was able to get the target's current scale ScalingActive False FailedGetResourceMetric the HPA was unable to compute the replica count: unable to get metrics for resource cpu: unable to fetch metrics from resource metrics API: the server is currently unable to handle the request (get pods.metrics.k8s.io) Events: Type Reason Age From Message ---- ------ ---- ---- ------- Warning FailedGetResourceMetric 3m3s (x1009 over 4h18m) horizontal-pod-autoscaler unable to get metrics for resource cpu: unable to fetch metrics from resource metrics API: the server is currently unable to handle the request (get pods.metrics.k8s.io)
- Cause 1: The Ddata source from which metrics are fetched is unavailable.
kubectl top podcommand to check whether metric data of monitored pods is returned. If no metric data is returned, run the
kubectl get apiservicecommand to check whether the metrics-server component is available.The following is an example of the returned data:
NAME SERVICE AVAILABLE AGE v1. Local True 29h v1.admissionregistration.k8s.io Local True 29h v1.apiextensions.k8s.io Local True 29h v1.apps Local True 29h v1.authentication.k8s.io Local True 29h v1.authorization.k8s.io Local True 29h v1.autoscaling Local True 29h v1.batch Local True 29h v1.coordination.k8s.io Local True 29h v1.monitoring.coreos.com Local True 29h v1.networking.k8s.io Local True 29h v1.rbac.authorization.k8s.io Local True 29h v1.scheduling.k8s.io Local True 29h v1.storage.k8s.io Local True 29h v1alpha1.argoproj.io Local True 29h v1alpha1.fedlearner.k8s.io Local True 5h11m v1beta1.admissionregistration.k8s.io Local True 29h v1beta1.alicloud.com Local True 29h v1beta1.apiextensions.k8s.io Local True 29h v1beta1.apps Local True 29h v1beta1.authentication.k8s.io Local True 29h v1beta1.authorization.k8s.io Local True 29h v1beta1.batch Local True 29h v1beta1.certificates.k8s.io Local True 29h v1beta1.coordination.k8s.io Local True 29h v1beta1.events.k8s.io Local True 29h v1beta1.extensions Local True 29h ... [v1beta1.metrics.k8s.io kube-system/metrics-server True 29h] ... v1beta1.networking.k8s.io Local True 29h v1beta1.node.k8s.io Local True 29h v1beta1.policy Local True 29h v1beta1.rbac.authorization.k8s.io Local True 29h v1beta1.scheduling.k8s.io Local True 29h v1beta1.storage.k8s.io Local True 29h v1beta2.apps Local True 29h v2beta1.autoscaling Local True 29h v2beta2.autoscaling Local True 29hIf the apiservice for v1beta1.metrics.k8s.io is not metrics-server deployed in the kube-system namespace, check whether metrics-server is overwritten by Prometheus Operator. If metrics-server is overwritten by Prometheus Operator, use the following YAML template to redeploy metrics-server:
apiVersion: apiregistration.k8s.io/v1beta1 kind: APIService metadata: name: v1beta1.metrics.k8s.io spec: service: name: metrics-server namespace: kube-system group: metrics.k8s.io version: v1beta1 insecureSkipTLSVerify: true groupPriorityMinimum: 100 versionPriority: 100
If no error is found after you have performed the preceding checks, see Troubleshooting for metrics-server.
- Cause 2: Metrics cannot be fetched during a rolling update or scale-out activity.
By default, metrics-server fetches metrics at intervals of one second However, metric-server cannot fetch metrics within a few seconds after a rolling update or scale-out activity. We recommend that you query metrics two seconds after a rolling update or scale-out activity.
- Reason 3: The requests field is not specified for the pod.
HPA automatically obtains the CPU or memory usage by calculating the value of
used resource/requested resourceof the pod. If the requested resource is not specified in the pod configurations, HPA cannot calculate the resource usage. Therefore, you must make sure that the requests field is specified in the pod configurations.
Issue 2: Excessive pods are added by HPA during a rolling update
Add the following configuration to the startup settings. --enable-hpa-rolling-update-skipped=true
Issue 3: HPA does not scale the number of pods when the scaling threshold is exceeded.
HPA may not scale the number of pods even if the CPU or memory usage drops below the scale-in threshold or exceeds the scale-out threshold. HPA also takes other factors into consideration when it scales pods. For example, it checks whether the current scale-out event triggers a scale-in activity or the scale-in event triggers a scale-out activity. This avoids repetitive scaling and prevents unnecessary resource occupation.