com.pulumi.gcp.dataproc.kotlin.inputs.AutoscalingPolicyBasicAlgorithmYarnConfigArgs.kt Maven / Gradle / Ivy

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Build cloud applications and infrastructure by combining the safety and reliability of infrastructure as code with the power of the Kotlin programming language.
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@file:Suppress("NAME_SHADOWING", "DEPRECATION")

package com.pulumi.gcp.dataproc.kotlin.inputs

import com.pulumi.core.Output
import com.pulumi.core.Output.of
import com.pulumi.gcp.dataproc.inputs.AutoscalingPolicyBasicAlgorithmYarnConfigArgs.builder
import com.pulumi.kotlin.ConvertibleToJava
import com.pulumi.kotlin.PulumiNullFieldException
import com.pulumi.kotlin.PulumiTagMarker
import kotlin.Double
import kotlin.String
import kotlin.Suppress
import kotlin.jvm.JvmName

/**
 *
 * @property gracefulDecommissionTimeout 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].
 * @property scaleDownFactor 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].
 * @property scaleDownMinWorkerFraction 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.
 * @property scaleUpFactor 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].
 * @property scaleUpMinWorkerFraction 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.
 */
public data class AutoscalingPolicyBasicAlgorithmYarnConfigArgs(
    public val gracefulDecommissionTimeout: Output,
    public val scaleDownFactor: Output,
    public val scaleDownMinWorkerFraction: Output? = null,
    public val scaleUpFactor: Output,
    public val scaleUpMinWorkerFraction: Output? = null,
) : ConvertibleToJava {
    override fun toJava(): com.pulumi.gcp.dataproc.inputs.AutoscalingPolicyBasicAlgorithmYarnConfigArgs =
        com.pulumi.gcp.dataproc.inputs.AutoscalingPolicyBasicAlgorithmYarnConfigArgs.builder()
            .gracefulDecommissionTimeout(gracefulDecommissionTimeout.applyValue({ args0 -> args0 }))
            .scaleDownFactor(scaleDownFactor.applyValue({ args0 -> args0 }))
            .scaleDownMinWorkerFraction(scaleDownMinWorkerFraction?.applyValue({ args0 -> args0 }))
            .scaleUpFactor(scaleUpFactor.applyValue({ args0 -> args0 }))
            .scaleUpMinWorkerFraction(scaleUpMinWorkerFraction?.applyValue({ args0 -> args0 })).build()
}

/**
 * Builder for [AutoscalingPolicyBasicAlgorithmYarnConfigArgs].
 */
@PulumiTagMarker
public class AutoscalingPolicyBasicAlgorithmYarnConfigArgsBuilder internal constructor() {
    private var gracefulDecommissionTimeout: Output? = null

    private var scaleDownFactor: Output? = null

    private var scaleDownMinWorkerFraction: Output? = null

    private var scaleUpFactor: Output? = null

    private var scaleUpMinWorkerFraction: Output? = null

    /**
     * @param value 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].
     */
    @JvmName("djrikvutfewlvcjv")
    public suspend fun gracefulDecommissionTimeout(`value`: Output) {
        this.gracefulDecommissionTimeout = value
    }

    /**
     * @param value 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].
     */
    @JvmName("eglifgqagylbridc")
    public suspend fun scaleDownFactor(`value`: Output) {
        this.scaleDownFactor = value
    }

    /**
     * @param value 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.
     */
    @JvmName("spmtakdqxbibofrc")
    public suspend fun scaleDownMinWorkerFraction(`value`: Output) {
        this.scaleDownMinWorkerFraction = value
    }

    /**
     * @param value 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].
     */
    @JvmName("pcstroioogpshyoh")
    public suspend fun scaleUpFactor(`value`: Output) {
        this.scaleUpFactor = value
    }

    /**
     * @param value 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.
     */
    @JvmName("nqwcebmatabuxfik")
    public suspend fun scaleUpMinWorkerFraction(`value`: Output) {
        this.scaleUpMinWorkerFraction = value
    }

    /**
     * @param value 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].
     */
    @JvmName("xybfbufvtcaxjjkq")
    public suspend fun gracefulDecommissionTimeout(`value`: String) {
        val toBeMapped = value
        val mapped = toBeMapped.let({ args0 -> of(args0) })
        this.gracefulDecommissionTimeout = mapped
    }

    /**
     * @param value 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].
     */
    @JvmName("vhvktcctqfwfsdbo")
    public suspend fun scaleDownFactor(`value`: Double) {
        val toBeMapped = value
        val mapped = toBeMapped.let({ args0 -> of(args0) })
        this.scaleDownFactor = mapped
    }

    /**
     * @param value 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.
     */
    @JvmName("yigehnvhgxefvtlh")
    public suspend fun scaleDownMinWorkerFraction(`value`: Double?) {
        val toBeMapped = value
        val mapped = toBeMapped?.let({ args0 -> of(args0) })
        this.scaleDownMinWorkerFraction = mapped
    }

    /**
     * @param value 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].
     */
    @JvmName("hmegvygwoydlicdn")
    public suspend fun scaleUpFactor(`value`: Double) {
        val toBeMapped = value
        val mapped = toBeMapped.let({ args0 -> of(args0) })
        this.scaleUpFactor = mapped
    }

    /**
     * @param value 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.
     */
    @JvmName("rfdbykmvrhosvrie")
    public suspend fun scaleUpMinWorkerFraction(`value`: Double?) {
        val toBeMapped = value
        val mapped = toBeMapped?.let({ args0 -> of(args0) })
        this.scaleUpMinWorkerFraction = mapped
    }

    internal fun build(): AutoscalingPolicyBasicAlgorithmYarnConfigArgs =
        AutoscalingPolicyBasicAlgorithmYarnConfigArgs(
            gracefulDecommissionTimeout = gracefulDecommissionTimeout ?: throw
                PulumiNullFieldException("gracefulDecommissionTimeout"),
            scaleDownFactor = scaleDownFactor ?: throw PulumiNullFieldException("scaleDownFactor"),
            scaleDownMinWorkerFraction = scaleDownMinWorkerFraction,
            scaleUpFactor = scaleUpFactor ?: throw PulumiNullFieldException("scaleUpFactor"),
            scaleUpMinWorkerFraction = scaleUpMinWorkerFraction,
        )
}