AutoscalingPolicyBasicAlgorithmYarnConfigArgs

data class AutoscalingPolicyBasicAlgorithmYarnConfigArgs(val gracefulDecommissionTimeout: Output<String>, val scaleDownFactor: Output<Double>, val scaleDownMinWorkerFraction: Output<Double>? = null, val scaleUpFactor: Output<Double>, val scaleUpMinWorkerFraction: Output<Double>? = null) : ConvertibleToJava<AutoscalingPolicyBasicAlgorithmYarnConfigArgs>

Constructors

constructor(gracefulDecommissionTimeout: Output<String>, scaleDownFactor: Output<Double>, scaleDownMinWorkerFraction: Output<Double>? = null, scaleUpFactor: Output<Double>, scaleUpMinWorkerFraction: Output<Double>? = null)

Properties

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Timeout for YARN graceful decommissioning of Node Managers. Specifies the duration to wait for jobs to complete before forcefully removing workers (and potentially interrupting jobs). Only applicable to downscaling operations. Bounds: 0s, 1d.

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val scaleDownFactor: Output<Double>

Fraction of average pending memory in the last cooldown period for which to remove workers. A scale-down factor of 1 will result in scaling down so that there is no available memory remaining after the update (more aggressive scaling). A scale-down factor of 0 disables removing workers, which can be beneficial for autoscaling a single job. Bounds: 0.0, 1.0.

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val scaleDownMinWorkerFraction: Output<Double>? = null

Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change. Bounds: 0.0, 1.0. Default: 0.0.

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val scaleUpFactor: Output<Double>

Fraction of average pending memory in the last cooldown period for which to add workers. A scale-up factor of 1.0 will result in scaling up so that there is no pending memory remaining after the update (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling). Bounds: 0.0, 1.0.

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val scaleUpMinWorkerFraction: Output<Double>? = null

Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change. Bounds: 0.0, 1.0. Default: 0.0.

Functions

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open override fun toJava(): AutoscalingPolicyBasicAlgorithmYarnConfigArgs