Cloud Dataproc V1 API - Class Google::Cloud::Dataproc::V1::BasicYarnAutoscalingConfig (v0.11.0)

Reference documentation and code samples for the Cloud Dataproc V1 API class Google::Cloud::Dataproc::V1::BasicYarnAutoscalingConfig.

Basic autoscaling configurations for YARN.

Inherits

  • Object

Extended By

  • Google::Protobuf::MessageExts::ClassMethods

Includes

  • Google::Protobuf::MessageExts

Methods

#graceful_decommission_timeout

def graceful_decommission_timeout() -> ::Google::Protobuf::Duration
Returns
  • (::Google::Protobuf::Duration) — Required. 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].

#graceful_decommission_timeout=

def graceful_decommission_timeout=(value) -> ::Google::Protobuf::Duration
Parameter
  • value (::Google::Protobuf::Duration) — Required. 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].

Returns
  • (::Google::Protobuf::Duration) — Required. 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].

#scale_down_factor

def scale_down_factor() -> ::Float
Returns
  • (::Float) — Required. Fraction of average YARN 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. See How autoscaling works for more information.

    Bounds: [0.0, 1.0].

#scale_down_factor=

def scale_down_factor=(value) -> ::Float
Parameter
  • value (::Float) — Required. Fraction of average YARN 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. See How autoscaling works for more information.

    Bounds: [0.0, 1.0].

Returns
  • (::Float) — Required. Fraction of average YARN 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. See How autoscaling works for more information.

    Bounds: [0.0, 1.0].

#scale_down_min_worker_fraction

def scale_down_min_worker_fraction() -> ::Float
Returns
  • (::Float) — Optional. 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.

#scale_down_min_worker_fraction=

def scale_down_min_worker_fraction=(value) -> ::Float
Parameter
  • value (::Float) — Optional. 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.

Returns
  • (::Float) — Optional. 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.

#scale_up_factor

def scale_up_factor() -> ::Float
Returns
  • (::Float) — Required. Fraction of average YARN 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). See How autoscaling works for more information.

    Bounds: [0.0, 1.0].

#scale_up_factor=

def scale_up_factor=(value) -> ::Float
Parameter
  • value (::Float) — Required. Fraction of average YARN 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). See How autoscaling works for more information.

    Bounds: [0.0, 1.0].

Returns
  • (::Float) — Required. Fraction of average YARN 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). See How autoscaling works for more information.

    Bounds: [0.0, 1.0].

#scale_up_min_worker_fraction

def scale_up_min_worker_fraction() -> ::Float
Returns
  • (::Float) — Optional. 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.

#scale_up_min_worker_fraction=

def scale_up_min_worker_fraction=(value) -> ::Float
Parameter
  • value (::Float) — Optional. 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.

Returns
  • (::Float) — Optional. 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.