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Configuration

Predictive Horizontal Pod Autoscalers have a number of configuration options available.

minReplicas

minReplicas: 2

The lower limit for the number of replicas to which the autoscaler can scale down to. minReplicas is allowed to be 0 if at least one Object or External metric is configured. Scaling is active as long as at least one metric value is available.

Default value: 1.

maxReplicas

maxReplicas: 15

The upper limit for the number of replicas to which the autoscaler can scale up. It cannot be less than minReplicas.

Default value: 10.

syncPeriod

syncPeriod: 10000

Equivalent to --horizontal-pod-autoscaler-sync-period; the frequency with which the PHPA calculates replica counts and scales in milliseconds.

Set in milliseconds.

Default value: 15000 (15 seconds).

Set in milliseconds.

cpuInitializationPeriod

cpuInitializationPeriod: 150

Equivalent to --horizontal-pod-autoscaler-cpu-initialization-period; the period after pod start when CPU samples might be skipped.

Set in seconds.

Default value: 300 (5 minutes).

initialReadinessDelay

initialReadinessDelay: 45

Equivalent to --horizontal-pod-autoscaler-initial-readiness-delay; the period after pod start during which readiness changes will be treated as initial readiness.

Set in seconds.

Default value: 30 (30 seconds).

tolerance

tolerance: 0.25

Equivalent to --horizontal-pod-autoscaler-tolerance; the minimum change (from 1.0) in the desired-to-actual metrics ratio for the horizontal pod autoscaler to consider scaling.

Default value: 0.1.

decisionType

decisionType: mean

Decider on which evaluation to pick if there are multiple models provided.

Possible values:

  • maximum - pick the highest evaluation of the models.
  • minimum - pick the lowest evaluation of the models.
  • mean - calculate the mean number of replicas (rounded to nearest integer) between the models.
  • median - calculate the median number of replicas between the models.

Default value: maximum.

behavior

Scaling behavior to apply.

Intended to be feature equivalent to Kubernetes HPA behavior.

See the Horizontal Pod Autoscaler docs here.

models

List of statistical models to apply. See the models section for details.

metrics

List of metrics to target for evaluating replica counts. See the metrics section for details.