com.pulumi.gcp.monitoring.kotlin.inputs.SloWindowsBasedSliMetricSumInRangeArgs.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.monitoring.kotlin.inputs

import com.pulumi.core.Output
import com.pulumi.core.Output.of
import com.pulumi.gcp.monitoring.inputs.SloWindowsBasedSliMetricSumInRangeArgs.builder
import com.pulumi.kotlin.ConvertibleToJava
import com.pulumi.kotlin.PulumiNullFieldException
import com.pulumi.kotlin.PulumiTagMarker
import com.pulumi.kotlin.applySuspend
import kotlin.String
import kotlin.Suppress
import kotlin.Unit
import kotlin.jvm.JvmName

/**
 *
 * @property range Range of numerical values. The computed good_service
 * will be the count of values x in the Distribution such
 * that range.min <= x <= range.max. inclusive of min and
 * max. Open ranges can be defined by setting
 * just one of min or max. Summed value `X` should satisfy
 * `range.min <= X <= range.max` for a good window.
 * Structure is documented below.
 * @property timeSeries A [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters)
 * specifying the TimeSeries to use for evaluating window
 * quality. The provided TimeSeries must have
 * ValueType = INT64 or ValueType = DOUBLE and
 * MetricKind = GAUGE.
 * Summed value `X` should satisfy
 * `range.min <= X <= range.max` for a good window.
 */
public data class SloWindowsBasedSliMetricSumInRangeArgs(
    public val range: Output,
    public val timeSeries: Output,
) : ConvertibleToJava {
    override fun toJava(): com.pulumi.gcp.monitoring.inputs.SloWindowsBasedSliMetricSumInRangeArgs =
        com.pulumi.gcp.monitoring.inputs.SloWindowsBasedSliMetricSumInRangeArgs.builder()
            .range(range.applyValue({ args0 -> args0.let({ args0 -> args0.toJava() }) }))
            .timeSeries(timeSeries.applyValue({ args0 -> args0 })).build()
}

/**
 * Builder for [SloWindowsBasedSliMetricSumInRangeArgs].
 */
@PulumiTagMarker
public class SloWindowsBasedSliMetricSumInRangeArgsBuilder internal constructor() {
    private var range: Output? = null

    private var timeSeries: Output? = null

    /**
     * @param value Range of numerical values. The computed good_service
     * will be the count of values x in the Distribution such
     * that range.min <= x <= range.max. inclusive of min and
     * max. Open ranges can be defined by setting
     * just one of min or max. Summed value `X` should satisfy
     * `range.min <= X <= range.max` for a good window.
     * Structure is documented below.
     */
    @JvmName("ddttstyhdmuqtrlu")
    public suspend fun range(`value`: Output) {
        this.range = value
    }

    /**
     * @param value A [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters)
     * specifying the TimeSeries to use for evaluating window
     * quality. The provided TimeSeries must have
     * ValueType = INT64 or ValueType = DOUBLE and
     * MetricKind = GAUGE.
     * Summed value `X` should satisfy
     * `range.min <= X <= range.max` for a good window.
     */
    @JvmName("pxuhweybhyoahlap")
    public suspend fun timeSeries(`value`: Output) {
        this.timeSeries = value
    }

    /**
     * @param value Range of numerical values. The computed good_service
     * will be the count of values x in the Distribution such
     * that range.min <= x <= range.max. inclusive of min and
     * max. Open ranges can be defined by setting
     * just one of min or max. Summed value `X` should satisfy
     * `range.min <= X <= range.max` for a good window.
     * Structure is documented below.
     */
    @JvmName("ihmpfnknwicvsdxs")
    public suspend fun range(`value`: SloWindowsBasedSliMetricSumInRangeRangeArgs) {
        val toBeMapped = value
        val mapped = toBeMapped.let({ args0 -> of(args0) })
        this.range = mapped
    }

    /**
     * @param argument Range of numerical values. The computed good_service
     * will be the count of values x in the Distribution such
     * that range.min <= x <= range.max. inclusive of min and
     * max. Open ranges can be defined by setting
     * just one of min or max. Summed value `X` should satisfy
     * `range.min <= X <= range.max` for a good window.
     * Structure is documented below.
     */
    @JvmName("jwwmmnewdlkmpklg")
    public suspend fun range(argument: suspend SloWindowsBasedSliMetricSumInRangeRangeArgsBuilder.() -> Unit) {
        val toBeMapped = SloWindowsBasedSliMetricSumInRangeRangeArgsBuilder().applySuspend {
            argument()
        }.build()
        val mapped = of(toBeMapped)
        this.range = mapped
    }

    /**
     * @param value A [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters)
     * specifying the TimeSeries to use for evaluating window
     * quality. The provided TimeSeries must have
     * ValueType = INT64 or ValueType = DOUBLE and
     * MetricKind = GAUGE.
     * Summed value `X` should satisfy
     * `range.min <= X <= range.max` for a good window.
     */
    @JvmName("ruppkjrbrrtbihgc")
    public suspend fun timeSeries(`value`: String) {
        val toBeMapped = value
        val mapped = toBeMapped.let({ args0 -> of(args0) })
        this.timeSeries = mapped
    }

    internal fun build(): SloWindowsBasedSliMetricSumInRangeArgs =
        SloWindowsBasedSliMetricSumInRangeArgs(
            range = range ?: throw PulumiNullFieldException("range"),
            timeSeries = timeSeries ?: throw PulumiNullFieldException("timeSeries"),
        )
}