Google Cloud Native v0.32.0, Nov 29 23

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Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi

pulumi/pulumi-google-native

google-native.monitoring/v3.AlertPolicy

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Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi

pulumi/pulumi-google-native

Create AlertPolicy Resource

Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

Constructor syntax

new AlertPolicy(name: string, args?: AlertPolicyArgs, opts?: CustomResourceOptions);

@overload
def AlertPolicy(resource_name: str,
                args: Optional[AlertPolicyArgs] = None,
                opts: Optional[ResourceOptions] = None)

@overload
def AlertPolicy(resource_name: str,
                opts: Optional[ResourceOptions] = None,
                alert_strategy: Optional[AlertStrategyArgs] = None,
                combiner: Optional[AlertPolicyCombiner] = None,
                conditions: Optional[Sequence[ConditionArgs]] = None,
                creation_record: Optional[MutationRecordArgs] = None,
                display_name: Optional[str] = None,
                documentation: Optional[DocumentationArgs] = None,
                enabled: Optional[bool] = None,
                mutation_record: Optional[MutationRecordArgs] = None,
                name: Optional[str] = None,
                notification_channels: Optional[Sequence[str]] = None,
                project: Optional[str] = None,
                severity: Optional[AlertPolicySeverity] = None,
                user_labels: Optional[Mapping[str, str]] = None,
                validity: Optional[StatusArgs] = None)

func NewAlertPolicy(ctx *Context, name string, args *AlertPolicyArgs, opts ...ResourceOption) (*AlertPolicy, error)

public AlertPolicy(string name, AlertPolicyArgs? args = null, CustomResourceOptions? opts = null)

public AlertPolicy(String name, AlertPolicyArgs args)
public AlertPolicy(String name, AlertPolicyArgs args, CustomResourceOptions options)

type: google-native:monitoring/v3:AlertPolicy
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

Parameters

name string: The unique name of the resource.
args AlertPolicyArgs: The arguments to resource properties.
opts CustomResourceOptions: Bag of options to control resource's behavior.

resource_name str: The unique name of the resource.
args AlertPolicyArgs: The arguments to resource properties.
opts ResourceOptions: Bag of options to control resource's behavior.

ctx Context: Context object for the current deployment.
name string: The unique name of the resource.
args AlertPolicyArgs: The arguments to resource properties.
opts ResourceOption: Bag of options to control resource's behavior.

name string: The unique name of the resource.
args AlertPolicyArgs: The arguments to resource properties.
opts CustomResourceOptions: Bag of options to control resource's behavior.

name String: The unique name of the resource.
args AlertPolicyArgs: The arguments to resource properties.
options CustomResourceOptions: Bag of options to control resource's behavior.

Example

The following reference example uses placeholder values for all input properties.

var alertPolicyResource = new GoogleNative.Monitoring.V3.AlertPolicy("alertPolicyResource", new()
{
    AlertStrategy = new GoogleNative.Monitoring.V3.Inputs.AlertStrategyArgs
    {
        AutoClose = "string",
        NotificationChannelStrategy = new[]
        {
            new GoogleNative.Monitoring.V3.Inputs.NotificationChannelStrategyArgs
            {
                NotificationChannelNames = new[]
                {
                    "string",
                },
                RenotifyInterval = "string",
            },
        },
        NotificationRateLimit = new GoogleNative.Monitoring.V3.Inputs.NotificationRateLimitArgs
        {
            Period = "string",
        },
    },
    Combiner = GoogleNative.Monitoring.V3.AlertPolicyCombiner.CombineUnspecified,
    Conditions = new[]
    {
        new GoogleNative.Monitoring.V3.Inputs.ConditionArgs
        {
            ConditionAbsent = new GoogleNative.Monitoring.V3.Inputs.MetricAbsenceArgs
            {
                Filter = "string",
                Aggregations = new[]
                {
                    new GoogleNative.Monitoring.V3.Inputs.AggregationArgs
                    {
                        AlignmentPeriod = "string",
                        CrossSeriesReducer = GoogleNative.Monitoring.V3.AggregationCrossSeriesReducer.ReduceNone,
                        GroupByFields = new[]
                        {
                            "string",
                        },
                        PerSeriesAligner = GoogleNative.Monitoring.V3.AggregationPerSeriesAligner.AlignNone,
                    },
                },
                Duration = "string",
                Trigger = new GoogleNative.Monitoring.V3.Inputs.TriggerArgs
                {
                    Count = 0,
                    Percent = 0,
                },
            },
            ConditionMatchedLog = new GoogleNative.Monitoring.V3.Inputs.LogMatchArgs
            {
                Filter = "string",
                LabelExtractors = 
                {
                    { "string", "string" },
                },
            },
            ConditionMonitoringQueryLanguage = new GoogleNative.Monitoring.V3.Inputs.MonitoringQueryLanguageConditionArgs
            {
                Duration = "string",
                EvaluationMissingData = GoogleNative.Monitoring.V3.MonitoringQueryLanguageConditionEvaluationMissingData.EvaluationMissingDataUnspecified,
                Query = "string",
                Trigger = new GoogleNative.Monitoring.V3.Inputs.TriggerArgs
                {
                    Count = 0,
                    Percent = 0,
                },
            },
            ConditionPrometheusQueryLanguage = new GoogleNative.Monitoring.V3.Inputs.PrometheusQueryLanguageConditionArgs
            {
                Query = "string",
                AlertRule = "string",
                Duration = "string",
                EvaluationInterval = "string",
                Labels = 
                {
                    { "string", "string" },
                },
                RuleGroup = "string",
            },
            ConditionThreshold = new GoogleNative.Monitoring.V3.Inputs.MetricThresholdArgs
            {
                Filter = "string",
                Aggregations = new[]
                {
                    new GoogleNative.Monitoring.V3.Inputs.AggregationArgs
                    {
                        AlignmentPeriod = "string",
                        CrossSeriesReducer = GoogleNative.Monitoring.V3.AggregationCrossSeriesReducer.ReduceNone,
                        GroupByFields = new[]
                        {
                            "string",
                        },
                        PerSeriesAligner = GoogleNative.Monitoring.V3.AggregationPerSeriesAligner.AlignNone,
                    },
                },
                Comparison = GoogleNative.Monitoring.V3.MetricThresholdComparison.ComparisonUnspecified,
                DenominatorAggregations = new[]
                {
                    new GoogleNative.Monitoring.V3.Inputs.AggregationArgs
                    {
                        AlignmentPeriod = "string",
                        CrossSeriesReducer = GoogleNative.Monitoring.V3.AggregationCrossSeriesReducer.ReduceNone,
                        GroupByFields = new[]
                        {
                            "string",
                        },
                        PerSeriesAligner = GoogleNative.Monitoring.V3.AggregationPerSeriesAligner.AlignNone,
                    },
                },
                DenominatorFilter = "string",
                Duration = "string",
                EvaluationMissingData = GoogleNative.Monitoring.V3.MetricThresholdEvaluationMissingData.EvaluationMissingDataUnspecified,
                ForecastOptions = new GoogleNative.Monitoring.V3.Inputs.ForecastOptionsArgs
                {
                    ForecastHorizon = "string",
                },
                ThresholdValue = 0,
                Trigger = new GoogleNative.Monitoring.V3.Inputs.TriggerArgs
                {
                    Count = 0,
                    Percent = 0,
                },
            },
            DisplayName = "string",
            Name = "string",
        },
    },
    CreationRecord = new GoogleNative.Monitoring.V3.Inputs.MutationRecordArgs
    {
        MutateTime = "string",
        MutatedBy = "string",
    },
    DisplayName = "string",
    Documentation = new GoogleNative.Monitoring.V3.Inputs.DocumentationArgs
    {
        Content = "string",
        MimeType = "string",
        Subject = "string",
    },
    Enabled = false,
    MutationRecord = new GoogleNative.Monitoring.V3.Inputs.MutationRecordArgs
    {
        MutateTime = "string",
        MutatedBy = "string",
    },
    Name = "string",
    NotificationChannels = new[]
    {
        "string",
    },
    Project = "string",
    Severity = GoogleNative.Monitoring.V3.AlertPolicySeverity.SeverityUnspecified,
    UserLabels = 
    {
        { "string", "string" },
    },
    Validity = new GoogleNative.Monitoring.V3.Inputs.StatusArgs
    {
        Code = 0,
        Details = new[]
        {
            
            {
                { "string", "string" },
            },
        },
        Message = "string",
    },
});

example, err := monitoringv3.NewAlertPolicy(ctx, "alertPolicyResource", &monitoringv3.AlertPolicyArgs{
AlertStrategy: &monitoring.AlertStrategyArgs{
AutoClose: pulumi.String("string"),
NotificationChannelStrategy: monitoring.NotificationChannelStrategyArray{
&monitoring.NotificationChannelStrategyArgs{
NotificationChannelNames: pulumi.StringArray{
pulumi.String("string"),
},
RenotifyInterval: pulumi.String("string"),
},
},
NotificationRateLimit: &monitoring.NotificationRateLimitArgs{
Period: pulumi.String("string"),
},
},
Combiner: monitoringv3.AlertPolicyCombinerCombineUnspecified,
Conditions: monitoring.ConditionArray{
&monitoring.ConditionArgs{
ConditionAbsent: &monitoring.MetricAbsenceArgs{
Filter: pulumi.String("string"),
Aggregations: monitoring.AggregationArray{
&monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoringv3.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoringv3.AggregationPerSeriesAlignerAlignNone,
},
},
Duration: pulumi.String("string"),
Trigger: &monitoring.TriggerArgs{
Count: pulumi.Int(0),
Percent: pulumi.Float64(0),
},
},
ConditionMatchedLog: &monitoring.LogMatchArgs{
Filter: pulumi.String("string"),
LabelExtractors: pulumi.StringMap{
"string": pulumi.String("string"),
},
},
ConditionMonitoringQueryLanguage: &monitoring.MonitoringQueryLanguageConditionArgs{
Duration: pulumi.String("string"),
EvaluationMissingData: monitoringv3.MonitoringQueryLanguageConditionEvaluationMissingDataEvaluationMissingDataUnspecified,
Query: pulumi.String("string"),
Trigger: &monitoring.TriggerArgs{
Count: pulumi.Int(0),
Percent: pulumi.Float64(0),
},
},
ConditionPrometheusQueryLanguage: &monitoring.PrometheusQueryLanguageConditionArgs{
Query: pulumi.String("string"),
AlertRule: pulumi.String("string"),
Duration: pulumi.String("string"),
EvaluationInterval: pulumi.String("string"),
Labels: pulumi.StringMap{
"string": pulumi.String("string"),
},
RuleGroup: pulumi.String("string"),
},
ConditionThreshold: &monitoring.MetricThresholdArgs{
Filter: pulumi.String("string"),
Aggregations: monitoring.AggregationArray{
&monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoringv3.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoringv3.AggregationPerSeriesAlignerAlignNone,
},
},
Comparison: monitoringv3.MetricThresholdComparisonComparisonUnspecified,
DenominatorAggregations: monitoring.AggregationArray{
&monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoringv3.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoringv3.AggregationPerSeriesAlignerAlignNone,
},
},
DenominatorFilter: pulumi.String("string"),
Duration: pulumi.String("string"),
EvaluationMissingData: monitoringv3.MetricThresholdEvaluationMissingDataEvaluationMissingDataUnspecified,
ForecastOptions: &monitoring.ForecastOptionsArgs{
ForecastHorizon: pulumi.String("string"),
},
ThresholdValue: pulumi.Float64(0),
Trigger: &monitoring.TriggerArgs{
Count: pulumi.Int(0),
Percent: pulumi.Float64(0),
},
},
DisplayName: pulumi.String("string"),
Name: pulumi.String("string"),
},
},
CreationRecord: &monitoring.MutationRecordArgs{
MutateTime: pulumi.String("string"),
MutatedBy: pulumi.String("string"),
},
DisplayName: pulumi.String("string"),
Documentation: &monitoring.DocumentationArgs{
Content: pulumi.String("string"),
MimeType: pulumi.String("string"),
Subject: pulumi.String("string"),
},
Enabled: pulumi.Bool(false),
MutationRecord: &monitoring.MutationRecordArgs{
MutateTime: pulumi.String("string"),
MutatedBy: pulumi.String("string"),
},
Name: pulumi.String("string"),
NotificationChannels: pulumi.StringArray{
pulumi.String("string"),
},
Project: pulumi.String("string"),
Severity: monitoringv3.AlertPolicySeveritySeverityUnspecified,
UserLabels: pulumi.StringMap{
"string": pulumi.String("string"),
},
Validity: &monitoring.StatusArgs{
Code: pulumi.Int(0),
Details: pulumi.StringMapArray{
pulumi.StringMap{
"string": pulumi.String("string"),
},
},
Message: pulumi.String("string"),
},
})

var alertPolicyResource = new AlertPolicy("alertPolicyResource", AlertPolicyArgs.builder()        
    .alertStrategy(AlertStrategyArgs.builder()
        .autoClose("string")
        .notificationChannelStrategy(NotificationChannelStrategyArgs.builder()
            .notificationChannelNames("string")
            .renotifyInterval("string")
            .build())
        .notificationRateLimit(NotificationRateLimitArgs.builder()
            .period("string")
            .build())
        .build())
    .combiner("COMBINE_UNSPECIFIED")
    .conditions(ConditionArgs.builder()
        .conditionAbsent(MetricAbsenceArgs.builder()
            .filter("string")
            .aggregations(AggregationArgs.builder()
                .alignmentPeriod("string")
                .crossSeriesReducer("REDUCE_NONE")
                .groupByFields("string")
                .perSeriesAligner("ALIGN_NONE")
                .build())
            .duration("string")
            .trigger(TriggerArgs.builder()
                .count(0)
                .percent(0)
                .build())
            .build())
        .conditionMatchedLog(LogMatchArgs.builder()
            .filter("string")
            .labelExtractors(Map.of("string", "string"))
            .build())
        .conditionMonitoringQueryLanguage(MonitoringQueryLanguageConditionArgs.builder()
            .duration("string")
            .evaluationMissingData("EVALUATION_MISSING_DATA_UNSPECIFIED")
            .query("string")
            .trigger(TriggerArgs.builder()
                .count(0)
                .percent(0)
                .build())
            .build())
        .conditionPrometheusQueryLanguage(PrometheusQueryLanguageConditionArgs.builder()
            .query("string")
            .alertRule("string")
            .duration("string")
            .evaluationInterval("string")
            .labels(Map.of("string", "string"))
            .ruleGroup("string")
            .build())
        .conditionThreshold(MetricThresholdArgs.builder()
            .filter("string")
            .aggregations(AggregationArgs.builder()
                .alignmentPeriod("string")
                .crossSeriesReducer("REDUCE_NONE")
                .groupByFields("string")
                .perSeriesAligner("ALIGN_NONE")
                .build())
            .comparison("COMPARISON_UNSPECIFIED")
            .denominatorAggregations(AggregationArgs.builder()
                .alignmentPeriod("string")
                .crossSeriesReducer("REDUCE_NONE")
                .groupByFields("string")
                .perSeriesAligner("ALIGN_NONE")
                .build())
            .denominatorFilter("string")
            .duration("string")
            .evaluationMissingData("EVALUATION_MISSING_DATA_UNSPECIFIED")
            .forecastOptions(ForecastOptionsArgs.builder()
                .forecastHorizon("string")
                .build())
            .thresholdValue(0)
            .trigger(TriggerArgs.builder()
                .count(0)
                .percent(0)
                .build())
            .build())
        .displayName("string")
        .name("string")
        .build())
    .creationRecord(MutationRecordArgs.builder()
        .mutateTime("string")
        .mutatedBy("string")
        .build())
    .displayName("string")
    .documentation(DocumentationArgs.builder()
        .content("string")
        .mimeType("string")
        .subject("string")
        .build())
    .enabled(false)
    .mutationRecord(MutationRecordArgs.builder()
        .mutateTime("string")
        .mutatedBy("string")
        .build())
    .name("string")
    .notificationChannels("string")
    .project("string")
    .severity("SEVERITY_UNSPECIFIED")
    .userLabels(Map.of("string", "string"))
    .validity(StatusArgs.builder()
        .code(0)
        .details(Map.of("string", "string"))
        .message("string")
        .build())
    .build());

alert_policy_resource = google_native.monitoring.v3.AlertPolicy("alertPolicyResource",
    alert_strategy=google_native.monitoring.v3.AlertStrategyArgs(
        auto_close="string",
        notification_channel_strategy=[google_native.monitoring.v3.NotificationChannelStrategyArgs(
            notification_channel_names=["string"],
            renotify_interval="string",
        )],
        notification_rate_limit=google_native.monitoring.v3.NotificationRateLimitArgs(
            period="string",
        ),
    ),
    combiner=google_native.monitoring.v3.AlertPolicyCombiner.COMBINE_UNSPECIFIED,
    conditions=[google_native.monitoring.v3.ConditionArgs(
        condition_absent=google_native.monitoring.v3.MetricAbsenceArgs(
            filter="string",
            aggregations=[google_native.monitoring.v3.AggregationArgs(
                alignment_period="string",
                cross_series_reducer=google_native.monitoring.v3.AggregationCrossSeriesReducer.REDUCE_NONE,
                group_by_fields=["string"],
                per_series_aligner=google_native.monitoring.v3.AggregationPerSeriesAligner.ALIGN_NONE,
            )],
            duration="string",
            trigger=google_native.monitoring.v3.TriggerArgs(
                count=0,
                percent=0,
            ),
        ),
        condition_matched_log=google_native.monitoring.v3.LogMatchArgs(
            filter="string",
            label_extractors={
                "string": "string",
            },
        ),
        condition_monitoring_query_language=google_native.monitoring.v3.MonitoringQueryLanguageConditionArgs(
            duration="string",
            evaluation_missing_data=google_native.monitoring.v3.MonitoringQueryLanguageConditionEvaluationMissingData.EVALUATION_MISSING_DATA_UNSPECIFIED,
            query="string",
            trigger=google_native.monitoring.v3.TriggerArgs(
                count=0,
                percent=0,
            ),
        ),
        condition_prometheus_query_language=google_native.monitoring.v3.PrometheusQueryLanguageConditionArgs(
            query="string",
            alert_rule="string",
            duration="string",
            evaluation_interval="string",
            labels={
                "string": "string",
            },
            rule_group="string",
        ),
        condition_threshold=google_native.monitoring.v3.MetricThresholdArgs(
            filter="string",
            aggregations=[google_native.monitoring.v3.AggregationArgs(
                alignment_period="string",
                cross_series_reducer=google_native.monitoring.v3.AggregationCrossSeriesReducer.REDUCE_NONE,
                group_by_fields=["string"],
                per_series_aligner=google_native.monitoring.v3.AggregationPerSeriesAligner.ALIGN_NONE,
            )],
            comparison=google_native.monitoring.v3.MetricThresholdComparison.COMPARISON_UNSPECIFIED,
            denominator_aggregations=[google_native.monitoring.v3.AggregationArgs(
                alignment_period="string",
                cross_series_reducer=google_native.monitoring.v3.AggregationCrossSeriesReducer.REDUCE_NONE,
                group_by_fields=["string"],
                per_series_aligner=google_native.monitoring.v3.AggregationPerSeriesAligner.ALIGN_NONE,
            )],
            denominator_filter="string",
            duration="string",
            evaluation_missing_data=google_native.monitoring.v3.MetricThresholdEvaluationMissingData.EVALUATION_MISSING_DATA_UNSPECIFIED,
            forecast_options=google_native.monitoring.v3.ForecastOptionsArgs(
                forecast_horizon="string",
            ),
            threshold_value=0,
            trigger=google_native.monitoring.v3.TriggerArgs(
                count=0,
                percent=0,
            ),
        ),
        display_name="string",
        name="string",
    )],
    creation_record=google_native.monitoring.v3.MutationRecordArgs(
        mutate_time="string",
        mutated_by="string",
    ),
    display_name="string",
    documentation=google_native.monitoring.v3.DocumentationArgs(
        content="string",
        mime_type="string",
        subject="string",
    ),
    enabled=False,
    mutation_record=google_native.monitoring.v3.MutationRecordArgs(
        mutate_time="string",
        mutated_by="string",
    ),
    name="string",
    notification_channels=["string"],
    project="string",
    severity=google_native.monitoring.v3.AlertPolicySeverity.SEVERITY_UNSPECIFIED,
    user_labels={
        "string": "string",
    },
    validity=google_native.monitoring.v3.StatusArgs(
        code=0,
        details=[{
            "string": "string",
        }],
        message="string",
    ))

const alertPolicyResource = new google_native.monitoring.v3.AlertPolicy("alertPolicyResource", {
    alertStrategy: {
        autoClose: "string",
        notificationChannelStrategy: [{
            notificationChannelNames: ["string"],
            renotifyInterval: "string",
        }],
        notificationRateLimit: {
            period: "string",
        },
    },
    combiner: google_native.monitoring.v3.AlertPolicyCombiner.CombineUnspecified,
    conditions: [{
        conditionAbsent: {
            filter: "string",
            aggregations: [{
                alignmentPeriod: "string",
                crossSeriesReducer: google_native.monitoring.v3.AggregationCrossSeriesReducer.ReduceNone,
                groupByFields: ["string"],
                perSeriesAligner: google_native.monitoring.v3.AggregationPerSeriesAligner.AlignNone,
            }],
            duration: "string",
            trigger: {
                count: 0,
                percent: 0,
            },
        },
        conditionMatchedLog: {
            filter: "string",
            labelExtractors: {
                string: "string",
            },
        },
        conditionMonitoringQueryLanguage: {
            duration: "string",
            evaluationMissingData: google_native.monitoring.v3.MonitoringQueryLanguageConditionEvaluationMissingData.EvaluationMissingDataUnspecified,
            query: "string",
            trigger: {
                count: 0,
                percent: 0,
            },
        },
        conditionPrometheusQueryLanguage: {
            query: "string",
            alertRule: "string",
            duration: "string",
            evaluationInterval: "string",
            labels: {
                string: "string",
            },
            ruleGroup: "string",
        },
        conditionThreshold: {
            filter: "string",
            aggregations: [{
                alignmentPeriod: "string",
                crossSeriesReducer: google_native.monitoring.v3.AggregationCrossSeriesReducer.ReduceNone,
                groupByFields: ["string"],
                perSeriesAligner: google_native.monitoring.v3.AggregationPerSeriesAligner.AlignNone,
            }],
            comparison: google_native.monitoring.v3.MetricThresholdComparison.ComparisonUnspecified,
            denominatorAggregations: [{
                alignmentPeriod: "string",
                crossSeriesReducer: google_native.monitoring.v3.AggregationCrossSeriesReducer.ReduceNone,
                groupByFields: ["string"],
                perSeriesAligner: google_native.monitoring.v3.AggregationPerSeriesAligner.AlignNone,
            }],
            denominatorFilter: "string",
            duration: "string",
            evaluationMissingData: google_native.monitoring.v3.MetricThresholdEvaluationMissingData.EvaluationMissingDataUnspecified,
            forecastOptions: {
                forecastHorizon: "string",
            },
            thresholdValue: 0,
            trigger: {
                count: 0,
                percent: 0,
            },
        },
        displayName: "string",
        name: "string",
    }],
    creationRecord: {
        mutateTime: "string",
        mutatedBy: "string",
    },
    displayName: "string",
    documentation: {
        content: "string",
        mimeType: "string",
        subject: "string",
    },
    enabled: false,
    mutationRecord: {
        mutateTime: "string",
        mutatedBy: "string",
    },
    name: "string",
    notificationChannels: ["string"],
    project: "string",
    severity: google_native.monitoring.v3.AlertPolicySeverity.SeverityUnspecified,
    userLabels: {
        string: "string",
    },
    validity: {
        code: 0,
        details: [{
            string: "string",
        }],
        message: "string",
    },
});

type: google-native:monitoring/v3:AlertPolicy
properties:
    alertStrategy:
        autoClose: string
        notificationChannelStrategy:
            - notificationChannelNames:
                - string
              renotifyInterval: string
        notificationRateLimit:
            period: string
    combiner: COMBINE_UNSPECIFIED
    conditions:
        - conditionAbsent:
            aggregations:
                - alignmentPeriod: string
                  crossSeriesReducer: REDUCE_NONE
                  groupByFields:
                    - string
                  perSeriesAligner: ALIGN_NONE
            duration: string
            filter: string
            trigger:
                count: 0
                percent: 0
          conditionMatchedLog:
            filter: string
            labelExtractors:
                string: string
          conditionMonitoringQueryLanguage:
            duration: string
            evaluationMissingData: EVALUATION_MISSING_DATA_UNSPECIFIED
            query: string
            trigger:
                count: 0
                percent: 0
          conditionPrometheusQueryLanguage:
            alertRule: string
            duration: string
            evaluationInterval: string
            labels:
                string: string
            query: string
            ruleGroup: string
          conditionThreshold:
            aggregations:
                - alignmentPeriod: string
                  crossSeriesReducer: REDUCE_NONE
                  groupByFields:
                    - string
                  perSeriesAligner: ALIGN_NONE
            comparison: COMPARISON_UNSPECIFIED
            denominatorAggregations:
                - alignmentPeriod: string
                  crossSeriesReducer: REDUCE_NONE
                  groupByFields:
                    - string
                  perSeriesAligner: ALIGN_NONE
            denominatorFilter: string
            duration: string
            evaluationMissingData: EVALUATION_MISSING_DATA_UNSPECIFIED
            filter: string
            forecastOptions:
                forecastHorizon: string
            thresholdValue: 0
            trigger:
                count: 0
                percent: 0
          displayName: string
          name: string
    creationRecord:
        mutateTime: string
        mutatedBy: string
    displayName: string
    documentation:
        content: string
        mimeType: string
        subject: string
    enabled: false
    mutationRecord:
        mutateTime: string
        mutatedBy: string
    name: string
    notificationChannels:
        - string
    project: string
    severity: SEVERITY_UNSPECIFIED
    userLabels:
        string: string
    validity:
        code: 0
        details:
            - string: string
        message: string

AlertPolicy Resource Properties

To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

Inputs

The AlertPolicy resource accepts the following input properties:

AlertStrategy Pulumi.GoogleNative.Monitoring.V3.Inputs.AlertStrategy: Control over how this alert policy's notification channels are notified.
Combiner Pulumi.GoogleNative.Monitoring.V3.AlertPolicyCombiner: How to combine the results of multiple conditions to determine if an incident should be opened. If condition_time_series_query_language is present, this must be COMBINE_UNSPECIFIED.
Conditions List<Pulumi.GoogleNative.Monitoring.V3.Inputs.Condition>: A list of conditions for the policy. The conditions are combined by AND or OR according to the combiner field. If the combined conditions evaluate to true, then an incident is created. A policy can have from one to six conditions. If condition_time_series_query_language is present, it must be the only condition. If condition_monitoring_query_language is present, it must be the only condition.
CreationRecord Pulumi.GoogleNative.Monitoring.V3.Inputs.MutationRecord: A read-only record of the creation of the alerting policy. If provided in a call to create or update, this field will be ignored.
DisplayName string: A short name or phrase used to identify the policy in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple policies in the same project. The name is limited to 512 Unicode characters.The convention for the display_name of a PrometheusQueryLanguageCondition is "{rule group name}/{alert name}", where the {rule group name} and {alert name} should be taken from the corresponding Prometheus configuration file. This convention is not enforced. In any case the display_name is not a unique key of the AlertPolicy.
Documentation Pulumi.GoogleNative.Monitoring.V3.Inputs.Documentation: Documentation that is included with notifications and incidents related to this policy. Best practice is for the documentation to include information to help responders understand, mitigate, escalate, and correct the underlying problems detected by the alerting policy. Notification channels that have limited capacity might not show this documentation.
Enabled bool: Whether or not the policy is enabled. On write, the default interpretation if unset is that the policy is enabled. On read, clients should not make any assumption about the state if it has not been populated. The field should always be populated on List and Get operations, unless a field projection has been specified that strips it out.
MutationRecord Pulumi.GoogleNative.Monitoring.V3.Inputs.MutationRecord: A read-only record of the most recent change to the alerting policy. If provided in a call to create or update, this field will be ignored.
Name string: Required if the policy exists. The resource name for this policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID] [ALERT_POLICY_ID] is assigned by Cloud Monitoring when the policy is created. When calling the alertPolicies.create method, do not include the name field in the alerting policy passed as part of the request.
NotificationChannels List<string>: Identifies the notification channels to which notifications should be sent when incidents are opened or closed or when new violations occur on an already opened incident. Each element of this array corresponds to the name field in each of the NotificationChannel objects that are returned from the ListNotificationChannels method. The format of the entries in this field is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
Project string
Severity Pulumi.GoogleNative.Monitoring.V3.AlertPolicySeverity: Optional. The severity of an alert policy indicates how important alerts generated by that policy are. The severity level, if specified, will be displayed on the Incident detail page and in notifications.
UserLabels Dictionary<string, string>: User-supplied key/value data to be used for organizing and identifying the AlertPolicy objects.The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter.Note that Prometheus {alert name} is a valid Prometheus label names (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels), whereas Prometheus {rule group} is an unrestricted UTF-8 string. This means that they cannot be stored as-is in user labels, because they may contain characters that are not allowed in user-label values.
Validity Pulumi.GoogleNative.Monitoring.V3.Inputs.Status: Read-only description of how the alert policy is invalid. This field is only set when the alert policy is invalid. An invalid alert policy will not generate incidents.

AlertStrategy AlertStrategyArgs: Control over how this alert policy's notification channels are notified.
Combiner AlertPolicyCombiner: How to combine the results of multiple conditions to determine if an incident should be opened. If condition_time_series_query_language is present, this must be COMBINE_UNSPECIFIED.
Conditions []ConditionArgs: A list of conditions for the policy. The conditions are combined by AND or OR according to the combiner field. If the combined conditions evaluate to true, then an incident is created. A policy can have from one to six conditions. If condition_time_series_query_language is present, it must be the only condition. If condition_monitoring_query_language is present, it must be the only condition.
CreationRecord MutationRecordArgs: A read-only record of the creation of the alerting policy. If provided in a call to create or update, this field will be ignored.
DisplayName string: A short name or phrase used to identify the policy in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple policies in the same project. The name is limited to 512 Unicode characters.The convention for the display_name of a PrometheusQueryLanguageCondition is "{rule group name}/{alert name}", where the {rule group name} and {alert name} should be taken from the corresponding Prometheus configuration file. This convention is not enforced. In any case the display_name is not a unique key of the AlertPolicy.
Documentation DocumentationArgs: Documentation that is included with notifications and incidents related to this policy. Best practice is for the documentation to include information to help responders understand, mitigate, escalate, and correct the underlying problems detected by the alerting policy. Notification channels that have limited capacity might not show this documentation.
Enabled bool: Whether or not the policy is enabled. On write, the default interpretation if unset is that the policy is enabled. On read, clients should not make any assumption about the state if it has not been populated. The field should always be populated on List and Get operations, unless a field projection has been specified that strips it out.
MutationRecord MutationRecordArgs: A read-only record of the most recent change to the alerting policy. If provided in a call to create or update, this field will be ignored.
Name string: Required if the policy exists. The resource name for this policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID] [ALERT_POLICY_ID] is assigned by Cloud Monitoring when the policy is created. When calling the alertPolicies.create method, do not include the name field in the alerting policy passed as part of the request.
NotificationChannels []string: Identifies the notification channels to which notifications should be sent when incidents are opened or closed or when new violations occur on an already opened incident. Each element of this array corresponds to the name field in each of the NotificationChannel objects that are returned from the ListNotificationChannels method. The format of the entries in this field is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
Project string
Severity AlertPolicySeverity: Optional. The severity of an alert policy indicates how important alerts generated by that policy are. The severity level, if specified, will be displayed on the Incident detail page and in notifications.
UserLabels map[string]string: User-supplied key/value data to be used for organizing and identifying the AlertPolicy objects.The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter.Note that Prometheus {alert name} is a valid Prometheus label names (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels), whereas Prometheus {rule group} is an unrestricted UTF-8 string. This means that they cannot be stored as-is in user labels, because they may contain characters that are not allowed in user-label values.
Validity StatusArgs: Read-only description of how the alert policy is invalid. This field is only set when the alert policy is invalid. An invalid alert policy will not generate incidents.

alertStrategy AlertStrategy: Control over how this alert policy's notification channels are notified.
combiner AlertPolicyCombiner: How to combine the results of multiple conditions to determine if an incident should be opened. If condition_time_series_query_language is present, this must be COMBINE_UNSPECIFIED.
conditions List<Condition>: A list of conditions for the policy. The conditions are combined by AND or OR according to the combiner field. If the combined conditions evaluate to true, then an incident is created. A policy can have from one to six conditions. If condition_time_series_query_language is present, it must be the only condition. If condition_monitoring_query_language is present, it must be the only condition.
creationRecord MutationRecord: A read-only record of the creation of the alerting policy. If provided in a call to create or update, this field will be ignored.
displayName String: A short name or phrase used to identify the policy in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple policies in the same project. The name is limited to 512 Unicode characters.The convention for the display_name of a PrometheusQueryLanguageCondition is "{rule group name}/{alert name}", where the {rule group name} and {alert name} should be taken from the corresponding Prometheus configuration file. This convention is not enforced. In any case the display_name is not a unique key of the AlertPolicy.
documentation Documentation: Documentation that is included with notifications and incidents related to this policy. Best practice is for the documentation to include information to help responders understand, mitigate, escalate, and correct the underlying problems detected by the alerting policy. Notification channels that have limited capacity might not show this documentation.
enabled Boolean: Whether or not the policy is enabled. On write, the default interpretation if unset is that the policy is enabled. On read, clients should not make any assumption about the state if it has not been populated. The field should always be populated on List and Get operations, unless a field projection has been specified that strips it out.
mutationRecord MutationRecord: A read-only record of the most recent change to the alerting policy. If provided in a call to create or update, this field will be ignored.
name String: Required if the policy exists. The resource name for this policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID] [ALERT_POLICY_ID] is assigned by Cloud Monitoring when the policy is created. When calling the alertPolicies.create method, do not include the name field in the alerting policy passed as part of the request.
notificationChannels List<String>: Identifies the notification channels to which notifications should be sent when incidents are opened or closed or when new violations occur on an already opened incident. Each element of this array corresponds to the name field in each of the NotificationChannel objects that are returned from the ListNotificationChannels method. The format of the entries in this field is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
project String
severity AlertPolicySeverity: Optional. The severity of an alert policy indicates how important alerts generated by that policy are. The severity level, if specified, will be displayed on the Incident detail page and in notifications.
userLabels Map<String,String>: User-supplied key/value data to be used for organizing and identifying the AlertPolicy objects.The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter.Note that Prometheus {alert name} is a valid Prometheus label names (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels), whereas Prometheus {rule group} is an unrestricted UTF-8 string. This means that they cannot be stored as-is in user labels, because they may contain characters that are not allowed in user-label values.
validity Status: Read-only description of how the alert policy is invalid. This field is only set when the alert policy is invalid. An invalid alert policy will not generate incidents.

alertStrategy AlertStrategy: Control over how this alert policy's notification channels are notified.
combiner AlertPolicyCombiner: How to combine the results of multiple conditions to determine if an incident should be opened. If condition_time_series_query_language is present, this must be COMBINE_UNSPECIFIED.
conditions Condition[]: A list of conditions for the policy. The conditions are combined by AND or OR according to the combiner field. If the combined conditions evaluate to true, then an incident is created. A policy can have from one to six conditions. If condition_time_series_query_language is present, it must be the only condition. If condition_monitoring_query_language is present, it must be the only condition.
creationRecord MutationRecord: A read-only record of the creation of the alerting policy. If provided in a call to create or update, this field will be ignored.
displayName string: A short name or phrase used to identify the policy in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple policies in the same project. The name is limited to 512 Unicode characters.The convention for the display_name of a PrometheusQueryLanguageCondition is "{rule group name}/{alert name}", where the {rule group name} and {alert name} should be taken from the corresponding Prometheus configuration file. This convention is not enforced. In any case the display_name is not a unique key of the AlertPolicy.
documentation Documentation: Documentation that is included with notifications and incidents related to this policy. Best practice is for the documentation to include information to help responders understand, mitigate, escalate, and correct the underlying problems detected by the alerting policy. Notification channels that have limited capacity might not show this documentation.
enabled boolean: Whether or not the policy is enabled. On write, the default interpretation if unset is that the policy is enabled. On read, clients should not make any assumption about the state if it has not been populated. The field should always be populated on List and Get operations, unless a field projection has been specified that strips it out.
mutationRecord MutationRecord: A read-only record of the most recent change to the alerting policy. If provided in a call to create or update, this field will be ignored.
name string: Required if the policy exists. The resource name for this policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID] [ALERT_POLICY_ID] is assigned by Cloud Monitoring when the policy is created. When calling the alertPolicies.create method, do not include the name field in the alerting policy passed as part of the request.
notificationChannels string[]: Identifies the notification channels to which notifications should be sent when incidents are opened or closed or when new violations occur on an already opened incident. Each element of this array corresponds to the name field in each of the NotificationChannel objects that are returned from the ListNotificationChannels method. The format of the entries in this field is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
project string
severity AlertPolicySeverity: Optional. The severity of an alert policy indicates how important alerts generated by that policy are. The severity level, if specified, will be displayed on the Incident detail page and in notifications.
userLabels {[key: string]: string}: User-supplied key/value data to be used for organizing and identifying the AlertPolicy objects.The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter.Note that Prometheus {alert name} is a valid Prometheus label names (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels), whereas Prometheus {rule group} is an unrestricted UTF-8 string. This means that they cannot be stored as-is in user labels, because they may contain characters that are not allowed in user-label values.
validity Status: Read-only description of how the alert policy is invalid. This field is only set when the alert policy is invalid. An invalid alert policy will not generate incidents.

alert_strategy AlertStrategyArgs: Control over how this alert policy's notification channels are notified.
combiner AlertPolicyCombiner: How to combine the results of multiple conditions to determine if an incident should be opened. If condition_time_series_query_language is present, this must be COMBINE_UNSPECIFIED.
conditions Sequence[ConditionArgs]: A list of conditions for the policy. The conditions are combined by AND or OR according to the combiner field. If the combined conditions evaluate to true, then an incident is created. A policy can have from one to six conditions. If condition_time_series_query_language is present, it must be the only condition. If condition_monitoring_query_language is present, it must be the only condition.
creation_record MutationRecordArgs: A read-only record of the creation of the alerting policy. If provided in a call to create or update, this field will be ignored.
display_name str: A short name or phrase used to identify the policy in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple policies in the same project. The name is limited to 512 Unicode characters.The convention for the display_name of a PrometheusQueryLanguageCondition is "{rule group name}/{alert name}", where the {rule group name} and {alert name} should be taken from the corresponding Prometheus configuration file. This convention is not enforced. In any case the display_name is not a unique key of the AlertPolicy.
documentation DocumentationArgs: Documentation that is included with notifications and incidents related to this policy. Best practice is for the documentation to include information to help responders understand, mitigate, escalate, and correct the underlying problems detected by the alerting policy. Notification channels that have limited capacity might not show this documentation.
enabled bool: Whether or not the policy is enabled. On write, the default interpretation if unset is that the policy is enabled. On read, clients should not make any assumption about the state if it has not been populated. The field should always be populated on List and Get operations, unless a field projection has been specified that strips it out.
mutation_record MutationRecordArgs: A read-only record of the most recent change to the alerting policy. If provided in a call to create or update, this field will be ignored.
name str: Required if the policy exists. The resource name for this policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID] [ALERT_POLICY_ID] is assigned by Cloud Monitoring when the policy is created. When calling the alertPolicies.create method, do not include the name field in the alerting policy passed as part of the request.
notification_channels Sequence[str]: Identifies the notification channels to which notifications should be sent when incidents are opened or closed or when new violations occur on an already opened incident. Each element of this array corresponds to the name field in each of the NotificationChannel objects that are returned from the ListNotificationChannels method. The format of the entries in this field is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
project str
severity AlertPolicySeverity: Optional. The severity of an alert policy indicates how important alerts generated by that policy are. The severity level, if specified, will be displayed on the Incident detail page and in notifications.
user_labels Mapping[str, str]: User-supplied key/value data to be used for organizing and identifying the AlertPolicy objects.The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter.Note that Prometheus {alert name} is a valid Prometheus label names (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels), whereas Prometheus {rule group} is an unrestricted UTF-8 string. This means that they cannot be stored as-is in user labels, because they may contain characters that are not allowed in user-label values.
validity StatusArgs: Read-only description of how the alert policy is invalid. This field is only set when the alert policy is invalid. An invalid alert policy will not generate incidents.

alertStrategy Property Map: Control over how this alert policy's notification channels are notified.
combiner "COMBINE_UNSPECIFIED" | "AND" | "OR" | "AND_WITH_MATCHING_RESOURCE": How to combine the results of multiple conditions to determine if an incident should be opened. If condition_time_series_query_language is present, this must be COMBINE_UNSPECIFIED.
conditions List<Property Map>: A list of conditions for the policy. The conditions are combined by AND or OR according to the combiner field. If the combined conditions evaluate to true, then an incident is created. A policy can have from one to six conditions. If condition_time_series_query_language is present, it must be the only condition. If condition_monitoring_query_language is present, it must be the only condition.
creationRecord Property Map: A read-only record of the creation of the alerting policy. If provided in a call to create or update, this field will be ignored.
displayName String: A short name or phrase used to identify the policy in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple policies in the same project. The name is limited to 512 Unicode characters.The convention for the display_name of a PrometheusQueryLanguageCondition is "{rule group name}/{alert name}", where the {rule group name} and {alert name} should be taken from the corresponding Prometheus configuration file. This convention is not enforced. In any case the display_name is not a unique key of the AlertPolicy.
documentation Property Map: Documentation that is included with notifications and incidents related to this policy. Best practice is for the documentation to include information to help responders understand, mitigate, escalate, and correct the underlying problems detected by the alerting policy. Notification channels that have limited capacity might not show this documentation.
enabled Boolean: Whether or not the policy is enabled. On write, the default interpretation if unset is that the policy is enabled. On read, clients should not make any assumption about the state if it has not been populated. The field should always be populated on List and Get operations, unless a field projection has been specified that strips it out.
mutationRecord Property Map: A read-only record of the most recent change to the alerting policy. If provided in a call to create or update, this field will be ignored.
name String: Required if the policy exists. The resource name for this policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID] [ALERT_POLICY_ID] is assigned by Cloud Monitoring when the policy is created. When calling the alertPolicies.create method, do not include the name field in the alerting policy passed as part of the request.
notificationChannels List<String>: Identifies the notification channels to which notifications should be sent when incidents are opened or closed or when new violations occur on an already opened incident. Each element of this array corresponds to the name field in each of the NotificationChannel objects that are returned from the ListNotificationChannels method. The format of the entries in this field is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
project String
severity "SEVERITY_UNSPECIFIED" | "CRITICAL" | "ERROR" | "WARNING": Optional. The severity of an alert policy indicates how important alerts generated by that policy are. The severity level, if specified, will be displayed on the Incident detail page and in notifications.
userLabels Map<String>: User-supplied key/value data to be used for organizing and identifying the AlertPolicy objects.The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter.Note that Prometheus {alert name} is a valid Prometheus label names (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels), whereas Prometheus {rule group} is an unrestricted UTF-8 string. This means that they cannot be stored as-is in user labels, because they may contain characters that are not allowed in user-label values.
validity Property Map: Read-only description of how the alert policy is invalid. This field is only set when the alert policy is invalid. An invalid alert policy will not generate incidents.

Outputs

All input properties are implicitly available as output properties. Additionally, the AlertPolicy resource produces the following output properties:

Id string: The provider-assigned unique ID for this managed resource.

Id string: The provider-assigned unique ID for this managed resource.

id String: The provider-assigned unique ID for this managed resource.

id string: The provider-assigned unique ID for this managed resource.

id str: The provider-assigned unique ID for this managed resource.

id String: The provider-assigned unique ID for this managed resource.

Supporting Types

Aggregation, AggregationArgs

AlignmentPeriod string: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.
CrossSeriesReducer Pulumi.GoogleNative.Monitoring.V3.AggregationCrossSeriesReducer: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned.
GroupByFields List<string>: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The cross_series_reducer is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains resource.type. Fields not specified in group_by_fields are aggregated away. If group_by_fields is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If cross_series_reducer is not defined, this field is ignored.
PerSeriesAligner Pulumi.GoogleNative.Monitoring.V3.AggregationPerSeriesAligner: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned.

AlignmentPeriod string: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.
CrossSeriesReducer AggregationCrossSeriesReducer: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned.
GroupByFields []string: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The cross_series_reducer is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains resource.type. Fields not specified in group_by_fields are aggregated away. If group_by_fields is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If cross_series_reducer is not defined, this field is ignored.
PerSeriesAligner AggregationPerSeriesAligner: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned.

alignmentPeriod String: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.
crossSeriesReducer AggregationCrossSeriesReducer: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned.
groupByFields List<String>: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The cross_series_reducer is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains resource.type. Fields not specified in group_by_fields are aggregated away. If group_by_fields is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If cross_series_reducer is not defined, this field is ignored.
perSeriesAligner AggregationPerSeriesAligner: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned.

alignmentPeriod string: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.
crossSeriesReducer AggregationCrossSeriesReducer: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned.
groupByFields string[]: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The cross_series_reducer is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains resource.type. Fields not specified in group_by_fields are aggregated away. If group_by_fields is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If cross_series_reducer is not defined, this field is ignored.
perSeriesAligner AggregationPerSeriesAligner: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned.

alignment_period str: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.
cross_series_reducer AggregationCrossSeriesReducer: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned.
group_by_fields Sequence[str]: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The cross_series_reducer is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains resource.type. Fields not specified in group_by_fields are aggregated away. If group_by_fields is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If cross_series_reducer is not defined, this field is ignored.
per_series_aligner AggregationPerSeriesAligner: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned.

alignmentPeriod String: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.
crossSeriesReducer "REDUCE_NONE" | "REDUCE_MEAN" | "REDUCE_MIN" | "REDUCE_MAX" | "REDUCE_SUM" | "REDUCE_STDDEV" | "REDUCE_COUNT" | "REDUCE_COUNT_TRUE" | "REDUCE_COUNT_FALSE" | "REDUCE_FRACTION_TRUE" | "REDUCE_PERCENTILE_99" | "REDUCE_PERCENTILE_95" | "REDUCE_PERCENTILE_50" | "REDUCE_PERCENTILE_05": The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned.
groupByFields List<String>: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The cross_series_reducer is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains resource.type. Fields not specified in group_by_fields are aggregated away. If group_by_fields is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If cross_series_reducer is not defined, this field is ignored.
perSeriesAligner "ALIGN_NONE" | "ALIGN_DELTA" | "ALIGN_RATE" | "ALIGN_INTERPOLATE" | "ALIGN_NEXT_OLDER" | "ALIGN_MIN" | "ALIGN_MAX" | "ALIGN_MEAN" | "ALIGN_COUNT" | "ALIGN_SUM" | "ALIGN_STDDEV" | "ALIGN_COUNT_TRUE" | "ALIGN_COUNT_FALSE" | "ALIGN_FRACTION_TRUE" | "ALIGN_PERCENTILE_99" | "ALIGN_PERCENTILE_95" | "ALIGN_PERCENTILE_50" | "ALIGN_PERCENTILE_05" | "ALIGN_PERCENT_CHANGE": An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned.

AggregationCrossSeriesReducer, AggregationCrossSeriesReducerArgs

ReduceNone: REDUCE_NONENo cross-time series reduction. The output of the Aligner is returned.
ReduceMean: REDUCE_MEANReduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
ReduceMin: REDUCE_MINReduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
ReduceMax: REDUCE_MAXReduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
ReduceSum: REDUCE_SUMReduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
ReduceStddev: REDUCE_STDDEVReduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
ReduceCount: REDUCE_COUNTReduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
ReduceCountTrue: REDUCE_COUNT_TRUEReduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
ReduceCountFalse: REDUCE_COUNT_FALSEReduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
ReduceFractionTrue: REDUCE_FRACTION_TRUEReduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
ReducePercentile99: REDUCE_PERCENTILE_99Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
ReducePercentile95: REDUCE_PERCENTILE_95Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
ReducePercentile50: REDUCE_PERCENTILE_50Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
ReducePercentile05: REDUCE_PERCENTILE_05Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.

AggregationCrossSeriesReducerReduceNone: REDUCE_NONENo cross-time series reduction. The output of the Aligner is returned.
AggregationCrossSeriesReducerReduceMean: REDUCE_MEANReduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
AggregationCrossSeriesReducerReduceMin: REDUCE_MINReduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
AggregationCrossSeriesReducerReduceMax: REDUCE_MAXReduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
AggregationCrossSeriesReducerReduceSum: REDUCE_SUMReduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
AggregationCrossSeriesReducerReduceStddev: REDUCE_STDDEVReduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
AggregationCrossSeriesReducerReduceCount: REDUCE_COUNTReduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
AggregationCrossSeriesReducerReduceCountTrue: REDUCE_COUNT_TRUEReduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
AggregationCrossSeriesReducerReduceCountFalse: REDUCE_COUNT_FALSEReduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
AggregationCrossSeriesReducerReduceFractionTrue: REDUCE_FRACTION_TRUEReduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
AggregationCrossSeriesReducerReducePercentile99: REDUCE_PERCENTILE_99Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
AggregationCrossSeriesReducerReducePercentile95: REDUCE_PERCENTILE_95Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
AggregationCrossSeriesReducerReducePercentile50: REDUCE_PERCENTILE_50Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
AggregationCrossSeriesReducerReducePercentile05: REDUCE_PERCENTILE_05Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.

ReduceNone: REDUCE_NONENo cross-time series reduction. The output of the Aligner is returned.
ReduceMean: REDUCE_MEANReduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
ReduceMin: REDUCE_MINReduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
ReduceMax: REDUCE_MAXReduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
ReduceSum: REDUCE_SUMReduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
ReduceStddev: REDUCE_STDDEVReduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
ReduceCount: REDUCE_COUNTReduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
ReduceCountTrue: REDUCE_COUNT_TRUEReduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
ReduceCountFalse: REDUCE_COUNT_FALSEReduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
ReduceFractionTrue: REDUCE_FRACTION_TRUEReduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
ReducePercentile99: REDUCE_PERCENTILE_99Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
ReducePercentile95: REDUCE_PERCENTILE_95Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
ReducePercentile50: REDUCE_PERCENTILE_50Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
ReducePercentile05: REDUCE_PERCENTILE_05Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.

ReduceNone: REDUCE_NONENo cross-time series reduction. The output of the Aligner is returned.
ReduceMean: REDUCE_MEANReduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
ReduceMin: REDUCE_MINReduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
ReduceMax: REDUCE_MAXReduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
ReduceSum: REDUCE_SUMReduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
ReduceStddev: REDUCE_STDDEVReduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
ReduceCount: REDUCE_COUNTReduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
ReduceCountTrue: REDUCE_COUNT_TRUEReduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
ReduceCountFalse: REDUCE_COUNT_FALSEReduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
ReduceFractionTrue: REDUCE_FRACTION_TRUEReduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
ReducePercentile99: REDUCE_PERCENTILE_99Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
ReducePercentile95: REDUCE_PERCENTILE_95Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
ReducePercentile50: REDUCE_PERCENTILE_50Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
ReducePercentile05: REDUCE_PERCENTILE_05Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.

REDUCE_NONE: REDUCE_NONENo cross-time series reduction. The output of the Aligner is returned.
REDUCE_MEAN: REDUCE_MEANReduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
REDUCE_MIN: REDUCE_MINReduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
REDUCE_MAX: REDUCE_MAXReduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
REDUCE_SUM: REDUCE_SUMReduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
REDUCE_STDDEV: REDUCE_STDDEVReduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
REDUCE_COUNT: REDUCE_COUNTReduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
REDUCE_COUNT_TRUE: REDUCE_COUNT_TRUEReduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
REDUCE_COUNT_FALSE: REDUCE_COUNT_FALSEReduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
REDUCE_FRACTION_TRUE: REDUCE_FRACTION_TRUEReduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
REDUCE_PERCENTILE99: REDUCE_PERCENTILE_99Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
REDUCE_PERCENTILE95: REDUCE_PERCENTILE_95Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
REDUCE_PERCENTILE50: REDUCE_PERCENTILE_50Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
REDUCE_PERCENTILE05: REDUCE_PERCENTILE_05Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.

"REDUCE_NONE": REDUCE_NONENo cross-time series reduction. The output of the Aligner is returned.
"REDUCE_MEAN": REDUCE_MEANReduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
"REDUCE_MIN": REDUCE_MINReduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
"REDUCE_MAX": REDUCE_MAXReduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
"REDUCE_SUM": REDUCE_SUMReduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
"REDUCE_STDDEV": REDUCE_STDDEVReduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
"REDUCE_COUNT": REDUCE_COUNTReduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
"REDUCE_COUNT_TRUE": REDUCE_COUNT_TRUEReduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
"REDUCE_COUNT_FALSE": REDUCE_COUNT_FALSEReduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
"REDUCE_FRACTION_TRUE": REDUCE_FRACTION_TRUEReduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
"REDUCE_PERCENTILE_99": REDUCE_PERCENTILE_99Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
"REDUCE_PERCENTILE_95": REDUCE_PERCENTILE_95Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
"REDUCE_PERCENTILE_50": REDUCE_PERCENTILE_50Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
"REDUCE_PERCENTILE_05": REDUCE_PERCENTILE_05Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.

AggregationPerSeriesAligner, AggregationPerSeriesAlignerArgs

AlignNone: ALIGN_NONENo alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
AlignDelta: ALIGN_DELTAAlign and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
AlignRate: ALIGN_RATEAlign and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.
AlignInterpolate: ALIGN_INTERPOLATEAlign by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
AlignNextOlder: ALIGN_NEXT_OLDERAlign by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
AlignMin: ALIGN_MINAlign the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
AlignMax: ALIGN_MAXAlign the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
AlignMean: ALIGN_MEANAlign the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
AlignCount: ALIGN_COUNTAlign the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
AlignSum: ALIGN_SUMAlign the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
AlignStddev: ALIGN_STDDEVAlign the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
AlignCountTrue: ALIGN_COUNT_TRUEAlign the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
AlignCountFalse: ALIGN_COUNT_FALSEAlign the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
AlignFractionTrue: ALIGN_FRACTION_TRUEAlign the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
AlignPercentile99: ALIGN_PERCENTILE_99Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
AlignPercentile95: ALIGN_PERCENTILE_95Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
AlignPercentile50: ALIGN_PERCENTILE_50Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
AlignPercentile05: ALIGN_PERCENTILE_05Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
AlignPercentChange: ALIGN_PERCENT_CHANGEAlign and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.

AggregationPerSeriesAlignerAlignNone: ALIGN_NONENo alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
AggregationPerSeriesAlignerAlignDelta: ALIGN_DELTAAlign and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
AggregationPerSeriesAlignerAlignRate: ALIGN_RATEAlign and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.
AggregationPerSeriesAlignerAlignInterpolate: ALIGN_INTERPOLATEAlign by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
AggregationPerSeriesAlignerAlignNextOlder: ALIGN_NEXT_OLDERAlign by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
AggregationPerSeriesAlignerAlignMin: ALIGN_MINAlign the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
AggregationPerSeriesAlignerAlignMax: ALIGN_MAXAlign the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
AggregationPerSeriesAlignerAlignMean: ALIGN_MEANAlign the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
AggregationPerSeriesAlignerAlignCount: ALIGN_COUNTAlign the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
AggregationPerSeriesAlignerAlignSum: ALIGN_SUMAlign the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
AggregationPerSeriesAlignerAlignStddev: ALIGN_STDDEVAlign the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
AggregationPerSeriesAlignerAlignCountTrue: ALIGN_COUNT_TRUEAlign the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
AggregationPerSeriesAlignerAlignCountFalse: ALIGN_COUNT_FALSEAlign the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
AggregationPerSeriesAlignerAlignFractionTrue: ALIGN_FRACTION_TRUEAlign the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
AggregationPerSeriesAlignerAlignPercentile99: ALIGN_PERCENTILE_99Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
AggregationPerSeriesAlignerAlignPercentile95: ALIGN_PERCENTILE_95Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
AggregationPerSeriesAlignerAlignPercentile50: ALIGN_PERCENTILE_50Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
AggregationPerSeriesAlignerAlignPercentile05: ALIGN_PERCENTILE_05Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
AggregationPerSeriesAlignerAlignPercentChange: ALIGN_PERCENT_CHANGEAlign and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.

AlignNone: ALIGN_NONENo alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
AlignDelta: ALIGN_DELTAAlign and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
AlignRate: ALIGN_RATEAlign and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.
AlignInterpolate: ALIGN_INTERPOLATEAlign by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
AlignNextOlder: ALIGN_NEXT_OLDERAlign by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
AlignMin: ALIGN_MINAlign the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
AlignMax: ALIGN_MAXAlign the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
AlignMean: ALIGN_MEANAlign the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
AlignCount: ALIGN_COUNTAlign the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
AlignSum: ALIGN_SUMAlign the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
AlignStddev: ALIGN_STDDEVAlign the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
AlignCountTrue: ALIGN_COUNT_TRUEAlign the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
AlignCountFalse: ALIGN_COUNT_FALSEAlign the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
AlignFractionTrue: ALIGN_FRACTION_TRUEAlign the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
AlignPercentile99: ALIGN_PERCENTILE_99Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
AlignPercentile95: ALIGN_PERCENTILE_95Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
AlignPercentile50: ALIGN_PERCENTILE_50Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
AlignPercentile05: ALIGN_PERCENTILE_05Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
AlignPercentChange: ALIGN_PERCENT_CHANGEAlign and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.

AlignNone: ALIGN_NONENo alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
AlignDelta: ALIGN_DELTAAlign and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
AlignRate: ALIGN_RATEAlign and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.
AlignInterpolate: ALIGN_INTERPOLATEAlign by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
AlignNextOlder: ALIGN_NEXT_OLDERAlign by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
AlignMin: ALIGN_MINAlign the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
AlignMax: ALIGN_MAXAlign the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
AlignMean: ALIGN_MEANAlign the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
AlignCount: ALIGN_COUNTAlign the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
AlignSum: ALIGN_SUMAlign the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
AlignStddev: ALIGN_STDDEVAlign the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
AlignCountTrue: ALIGN_COUNT_TRUEAlign the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
AlignCountFalse: ALIGN_COUNT_FALSEAlign the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
AlignFractionTrue: ALIGN_FRACTION_TRUEAlign the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
AlignPercentile99: ALIGN_PERCENTILE_99Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
AlignPercentile95: ALIGN_PERCENTILE_95Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
AlignPercentile50: ALIGN_PERCENTILE_50Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
AlignPercentile05: ALIGN_PERCENTILE_05Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
AlignPercentChange: ALIGN_PERCENT_CHANGEAlign and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.

ALIGN_NONE: ALIGN_NONENo alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
ALIGN_DELTA: ALIGN_DELTAAlign and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
ALIGN_RATE: ALIGN_RATEAlign and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.
ALIGN_INTERPOLATE: ALIGN_INTERPOLATEAlign by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
ALIGN_NEXT_OLDER: ALIGN_NEXT_OLDERAlign by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
ALIGN_MIN: ALIGN_MINAlign the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
ALIGN_MAX: ALIGN_MAXAlign the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
ALIGN_MEAN: ALIGN_MEANAlign the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
ALIGN_COUNT: ALIGN_COUNTAlign the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
ALIGN_SUM: ALIGN_SUMAlign the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
ALIGN_STDDEV: ALIGN_STDDEVAlign the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
ALIGN_COUNT_TRUE: ALIGN_COUNT_TRUEAlign the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
ALIGN_COUNT_FALSE: ALIGN_COUNT_FALSEAlign the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
ALIGN_FRACTION_TRUE: ALIGN_FRACTION_TRUEAlign the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
ALIGN_PERCENTILE99: ALIGN_PERCENTILE_99Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
ALIGN_PERCENTILE95: ALIGN_PERCENTILE_95Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
ALIGN_PERCENTILE50: ALIGN_PERCENTILE_50Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
ALIGN_PERCENTILE05: ALIGN_PERCENTILE_05Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
ALIGN_PERCENT_CHANGE: ALIGN_PERCENT_CHANGEAlign and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.

"ALIGN_NONE": ALIGN_NONENo alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
"ALIGN_DELTA": ALIGN_DELTAAlign and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
"ALIGN_RATE": ALIGN_RATEAlign and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.
"ALIGN_INTERPOLATE": ALIGN_INTERPOLATEAlign by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
"ALIGN_NEXT_OLDER": ALIGN_NEXT_OLDERAlign by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
"ALIGN_MIN": ALIGN_MINAlign the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
"ALIGN_MAX": ALIGN_MAXAlign the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
"ALIGN_MEAN": ALIGN_MEANAlign the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
"ALIGN_COUNT": ALIGN_COUNTAlign the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
"ALIGN_SUM": ALIGN_SUMAlign the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
"ALIGN_STDDEV": ALIGN_STDDEVAlign the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
"ALIGN_COUNT_TRUE": ALIGN_COUNT_TRUEAlign the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
"ALIGN_COUNT_FALSE": ALIGN_COUNT_FALSEAlign the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
"ALIGN_FRACTION_TRUE": ALIGN_FRACTION_TRUEAlign the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
"ALIGN_PERCENTILE_99": ALIGN_PERCENTILE_99Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
"ALIGN_PERCENTILE_95": ALIGN_PERCENTILE_95Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
"ALIGN_PERCENTILE_50": ALIGN_PERCENTILE_50Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
"ALIGN_PERCENTILE_05": ALIGN_PERCENTILE_05Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
"ALIGN_PERCENT_CHANGE": ALIGN_PERCENT_CHANGEAlign and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.

AggregationResponse, AggregationResponseArgs

AlignmentPeriod string: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.
CrossSeriesReducer string: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned.
GroupByFields List<string>: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The cross_series_reducer is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains resource.type. Fields not specified in group_by_fields are aggregated away. If group_by_fields is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If cross_series_reducer is not defined, this field is ignored.
PerSeriesAligner string: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned.

AlignmentPeriod string: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.
CrossSeriesReducer string: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned.
GroupByFields []string: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The cross_series_reducer is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains resource.type. Fields not specified in group_by_fields are aggregated away. If group_by_fields is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If cross_series_reducer is not defined, this field is ignored.
PerSeriesAligner string: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned.

alignmentPeriod String: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.
crossSeriesReducer String: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned.
groupByFields List<String>: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The cross_series_reducer is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains resource.type. Fields not specified in group_by_fields are aggregated away. If group_by_fields is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If cross_series_reducer is not defined, this field is ignored.
perSeriesAligner String: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned.

alignmentPeriod string: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.
crossSeriesReducer string: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned.
groupByFields string[]: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The cross_series_reducer is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains resource.type. Fields not specified in group_by_fields are aggregated away. If group_by_fields is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If cross_series_reducer is not defined, this field is ignored.
perSeriesAligner string: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned.

alignment_period str: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.
cross_series_reducer str: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned.
group_by_fields Sequence[str]: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The cross_series_reducer is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains resource.type. Fields not specified in group_by_fields are aggregated away. If group_by_fields is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If cross_series_reducer is not defined, this field is ignored.
per_series_aligner str: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned.

alignmentPeriod String: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.
crossSeriesReducer String: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned.
groupByFields List<String>: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The cross_series_reducer is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains resource.type. Fields not specified in group_by_fields are aggregated away. If group_by_fields is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If cross_series_reducer is not defined, this field is ignored.
perSeriesAligner String: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned.

AlertPolicyCombiner, AlertPolicyCombinerArgs

CombineUnspecified: COMBINE_UNSPECIFIEDAn unspecified combiner.
And: ANDCombine conditions using the logical AND operator. An incident is created only if all the conditions are met simultaneously. This combiner is satisfied if all conditions are met, even if they are met on completely different resources.
Or: ORCombine conditions using the logical OR operator. An incident is created if any of the listed conditions is met.
AndWithMatchingResource: AND_WITH_MATCHING_RESOURCECombine conditions using logical AND operator, but unlike the regular AND option, an incident is created only if all conditions are met simultaneously on at least one resource.

AlertPolicyCombinerCombineUnspecified: COMBINE_UNSPECIFIEDAn unspecified combiner.
AlertPolicyCombinerAnd: ANDCombine conditions using the logical AND operator. An incident is created only if all the conditions are met simultaneously. This combiner is satisfied if all conditions are met, even if they are met on completely different resources.
AlertPolicyCombinerOr: ORCombine conditions using the logical OR operator. An incident is created if any of the listed conditions is met.
AlertPolicyCombinerAndWithMatchingResource: AND_WITH_MATCHING_RESOURCECombine conditions using logical AND operator, but unlike the regular AND option, an incident is created only if all conditions are met simultaneously on at least one resource.

CombineUnspecified: COMBINE_UNSPECIFIEDAn unspecified combiner.
And: ANDCombine conditions using the logical AND operator. An incident is created only if all the conditions are met simultaneously. This combiner is satisfied if all conditions are met, even if they are met on completely different resources.
Or: ORCombine conditions using the logical OR operator. An incident is created if any of the listed conditions is met.
AndWithMatchingResource: AND_WITH_MATCHING_RESOURCECombine conditions using logical AND operator, but unlike the regular AND option, an incident is created only if all conditions are met simultaneously on at least one resource.

CombineUnspecified: COMBINE_UNSPECIFIEDAn unspecified combiner.
And: ANDCombine conditions using the logical AND operator. An incident is created only if all the conditions are met simultaneously. This combiner is satisfied if all conditions are met, even if they are met on completely different resources.
Or: ORCombine conditions using the logical OR operator. An incident is created if any of the listed conditions is met.
AndWithMatchingResource: AND_WITH_MATCHING_RESOURCECombine conditions using logical AND operator, but unlike the regular AND option, an incident is created only if all conditions are met simultaneously on at least one resource.

COMBINE_UNSPECIFIED: COMBINE_UNSPECIFIEDAn unspecified combiner.
AND_: ANDCombine conditions using the logical AND operator. An incident is created only if all the conditions are met simultaneously. This combiner is satisfied if all conditions are met, even if they are met on completely different resources.
OR_: ORCombine conditions using the logical OR operator. An incident is created if any of the listed conditions is met.
AND_WITH_MATCHING_RESOURCE: AND_WITH_MATCHING_RESOURCECombine conditions using logical AND operator, but unlike the regular AND option, an incident is created only if all conditions are met simultaneously on at least one resource.

"COMBINE_UNSPECIFIED": COMBINE_UNSPECIFIEDAn unspecified combiner.
"AND": ANDCombine conditions using the logical AND operator. An incident is created only if all the conditions are met simultaneously. This combiner is satisfied if all conditions are met, even if they are met on completely different resources.
"OR": ORCombine conditions using the logical OR operator. An incident is created if any of the listed conditions is met.
"AND_WITH_MATCHING_RESOURCE": AND_WITH_MATCHING_RESOURCECombine conditions using logical AND operator, but unlike the regular AND option, an incident is created only if all conditions are met simultaneously on at least one resource.

AlertPolicySeverity, AlertPolicySeverityArgs

SeverityUnspecified: SEVERITY_UNSPECIFIEDNo severity is specified. This is the default value.
Critical: CRITICALThis is the highest severity level. Use this if the problem could cause significant damage or downtime.
Error: ERRORThis is the medium severity level. Use this if the problem could cause minor damage or downtime.
Warning: WARNINGThis is the lowest severity level. Use this if the problem is not causing any damage or downtime, but could potentially lead to a problem in the future.

AlertPolicySeveritySeverityUnspecified: SEVERITY_UNSPECIFIEDNo severity is specified. This is the default value.
AlertPolicySeverityCritical: CRITICALThis is the highest severity level. Use this if the problem could cause significant damage or downtime.
AlertPolicySeverityError: ERRORThis is the medium severity level. Use this if the problem could cause minor damage or downtime.
AlertPolicySeverityWarning: WARNINGThis is the lowest severity level. Use this if the problem is not causing any damage or downtime, but could potentially lead to a problem in the future.

SeverityUnspecified: SEVERITY_UNSPECIFIEDNo severity is specified. This is the default value.
Critical: CRITICALThis is the highest severity level. Use this if the problem could cause significant damage or downtime.
Error: ERRORThis is the medium severity level. Use this if the problem could cause minor damage or downtime.
Warning: WARNINGThis is the lowest severity level. Use this if the problem is not causing any damage or downtime, but could potentially lead to a problem in the future.

SeverityUnspecified: SEVERITY_UNSPECIFIEDNo severity is specified. This is the default value.
Critical: CRITICALThis is the highest severity level. Use this if the problem could cause significant damage or downtime.
Error: ERRORThis is the medium severity level. Use this if the problem could cause minor damage or downtime.
Warning: WARNINGThis is the lowest severity level. Use this if the problem is not causing any damage or downtime, but could potentially lead to a problem in the future.

SEVERITY_UNSPECIFIED: SEVERITY_UNSPECIFIEDNo severity is specified. This is the default value.
CRITICAL: CRITICALThis is the highest severity level. Use this if the problem could cause significant damage or downtime.
ERROR: ERRORThis is the medium severity level. Use this if the problem could cause minor damage or downtime.
WARNING: WARNINGThis is the lowest severity level. Use this if the problem is not causing any damage or downtime, but could potentially lead to a problem in the future.

"SEVERITY_UNSPECIFIED": SEVERITY_UNSPECIFIEDNo severity is specified. This is the default value.
"CRITICAL": CRITICALThis is the highest severity level. Use this if the problem could cause significant damage or downtime.
"ERROR": ERRORThis is the medium severity level. Use this if the problem could cause minor damage or downtime.
"WARNING": WARNINGThis is the lowest severity level. Use this if the problem is not causing any damage or downtime, but could potentially lead to a problem in the future.

AlertStrategy, AlertStrategyArgs

AutoClose string: If an alert policy that was active has no data for this long, any open incidents will close
NotificationChannelStrategy List<Pulumi.GoogleNative.Monitoring.V3.Inputs.NotificationChannelStrategy>: Control how notifications will be sent out, on a per-channel basis.
NotificationRateLimit Pulumi.GoogleNative.Monitoring.V3.Inputs.NotificationRateLimit: Required for alert policies with a LogMatch condition.This limit is not implemented for alert policies that are not log-based.

AutoClose string: If an alert policy that was active has no data for this long, any open incidents will close
NotificationChannelStrategy []NotificationChannelStrategy: Control how notifications will be sent out, on a per-channel basis.
NotificationRateLimit NotificationRateLimit: Required for alert policies with a LogMatch condition.This limit is not implemented for alert policies that are not log-based.

autoClose String: If an alert policy that was active has no data for this long, any open incidents will close
notificationChannelStrategy List<NotificationChannelStrategy>: Control how notifications will be sent out, on a per-channel basis.
notificationRateLimit NotificationRateLimit: Required for alert policies with a LogMatch condition.This limit is not implemented for alert policies that are not log-based.

autoClose string: If an alert policy that was active has no data for this long, any open incidents will close
notificationChannelStrategy NotificationChannelStrategy[]: Control how notifications will be sent out, on a per-channel basis.
notificationRateLimit NotificationRateLimit: Required for alert policies with a LogMatch condition.This limit is not implemented for alert policies that are not log-based.

auto_close str: If an alert policy that was active has no data for this long, any open incidents will close
notification_channel_strategy Sequence[NotificationChannelStrategy]: Control how notifications will be sent out, on a per-channel basis.
notification_rate_limit NotificationRateLimit: Required for alert policies with a LogMatch condition.This limit is not implemented for alert policies that are not log-based.

autoClose String: If an alert policy that was active has no data for this long, any open incidents will close
notificationChannelStrategy List<Property Map>: Control how notifications will be sent out, on a per-channel basis.
notificationRateLimit Property Map: Required for alert policies with a LogMatch condition.This limit is not implemented for alert policies that are not log-based.

AlertStrategyResponse, AlertStrategyResponseArgs

AutoClose string: If an alert policy that was active has no data for this long, any open incidents will close
NotificationChannelStrategy List<Pulumi.GoogleNative.Monitoring.V3.Inputs.NotificationChannelStrategyResponse>: Control how notifications will be sent out, on a per-channel basis.
NotificationRateLimit Pulumi.GoogleNative.Monitoring.V3.Inputs.NotificationRateLimitResponse: Required for alert policies with a LogMatch condition.This limit is not implemented for alert policies that are not log-based.

AutoClose string: If an alert policy that was active has no data for this long, any open incidents will close
NotificationChannelStrategy []NotificationChannelStrategyResponse: Control how notifications will be sent out, on a per-channel basis.
NotificationRateLimit NotificationRateLimitResponse: Required for alert policies with a LogMatch condition.This limit is not implemented for alert policies that are not log-based.

autoClose String: If an alert policy that was active has no data for this long, any open incidents will close
notificationChannelStrategy List<NotificationChannelStrategyResponse>: Control how notifications will be sent out, on a per-channel basis.
notificationRateLimit NotificationRateLimitResponse: Required for alert policies with a LogMatch condition.This limit is not implemented for alert policies that are not log-based.

autoClose string: If an alert policy that was active has no data for this long, any open incidents will close
notificationChannelStrategy NotificationChannelStrategyResponse[]: Control how notifications will be sent out, on a per-channel basis.
notificationRateLimit NotificationRateLimitResponse: Required for alert policies with a LogMatch condition.This limit is not implemented for alert policies that are not log-based.

auto_close str: If an alert policy that was active has no data for this long, any open incidents will close
notification_channel_strategy Sequence[NotificationChannelStrategyResponse]: Control how notifications will be sent out, on a per-channel basis.
notification_rate_limit NotificationRateLimitResponse: Required for alert policies with a LogMatch condition.This limit is not implemented for alert policies that are not log-based.

autoClose String: If an alert policy that was active has no data for this long, any open incidents will close
notificationChannelStrategy List<Property Map>: Control how notifications will be sent out, on a per-channel basis.
notificationRateLimit Property Map: Required for alert policies with a LogMatch condition.This limit is not implemented for alert policies that are not log-based.

Condition, ConditionArgs

ConditionAbsent Pulumi.GoogleNative.Monitoring.V3.Inputs.MetricAbsence: A condition that checks that a time series continues to receive new data points.
ConditionMatchedLog Pulumi.GoogleNative.Monitoring.V3.Inputs.LogMatch: A condition that checks for log messages matching given constraints. If set, no other conditions can be present.
ConditionMonitoringQueryLanguage Pulumi.GoogleNative.Monitoring.V3.Inputs.MonitoringQueryLanguageCondition: A condition that uses the Monitoring Query Language to define alerts.
ConditionPrometheusQueryLanguage Pulumi.GoogleNative.Monitoring.V3.Inputs.PrometheusQueryLanguageCondition: A condition that uses the Prometheus query language to define alerts.
ConditionThreshold Pulumi.GoogleNative.Monitoring.V3.Inputs.MetricThreshold: A condition that compares a time series against a threshold.
DisplayName string: A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple conditions in the same policy.
Name string: Required if the condition exists. The unique resource name for this condition. Its format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[POLICY_ID]/conditions/[CONDITION_ID] [CONDITION_ID] is assigned by Cloud Monitoring when the condition is created as part of a new or updated alerting policy.When calling the alertPolicies.create method, do not include the name field in the conditions of the requested alerting policy. Cloud Monitoring creates the condition identifiers and includes them in the new policy.When calling the alertPolicies.update method to update a policy, including a condition name causes the existing condition to be updated. Conditions without names are added to the updated policy. Existing conditions are deleted if they are not updated.Best practice is to preserve [CONDITION_ID] if you make only small changes, such as those to condition thresholds, durations, or trigger values. Otherwise, treat the change as a new condition and let the existing condition be deleted.

ConditionAbsent MetricAbsence: A condition that checks that a time series continues to receive new data points.
ConditionMatchedLog LogMatch: A condition that checks for log messages matching given constraints. If set, no other conditions can be present.
ConditionMonitoringQueryLanguage MonitoringQueryLanguageCondition: A condition that uses the Monitoring Query Language to define alerts.
ConditionPrometheusQueryLanguage PrometheusQueryLanguageCondition: A condition that uses the Prometheus query language to define alerts.
ConditionThreshold MetricThreshold: A condition that compares a time series against a threshold.
DisplayName string: A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple conditions in the same policy.
Name string: Required if the condition exists. The unique resource name for this condition. Its format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[POLICY_ID]/conditions/[CONDITION_ID] [CONDITION_ID] is assigned by Cloud Monitoring when the condition is created as part of a new or updated alerting policy.When calling the alertPolicies.create method, do not include the name field in the conditions of the requested alerting policy. Cloud Monitoring creates the condition identifiers and includes them in the new policy.When calling the alertPolicies.update method to update a policy, including a condition name causes the existing condition to be updated. Conditions without names are added to the updated policy. Existing conditions are deleted if they are not updated.Best practice is to preserve [CONDITION_ID] if you make only small changes, such as those to condition thresholds, durations, or trigger values. Otherwise, treat the change as a new condition and let the existing condition be deleted.

conditionAbsent MetricAbsence: A condition that checks that a time series continues to receive new data points.
conditionMatchedLog LogMatch: A condition that checks for log messages matching given constraints. If set, no other conditions can be present.
conditionMonitoringQueryLanguage MonitoringQueryLanguageCondition: A condition that uses the Monitoring Query Language to define alerts.
conditionPrometheusQueryLanguage PrometheusQueryLanguageCondition: A condition that uses the Prometheus query language to define alerts.
conditionThreshold MetricThreshold: A condition that compares a time series against a threshold.
displayName String: A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple conditions in the same policy.
name String: Required if the condition exists. The unique resource name for this condition. Its format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[POLICY_ID]/conditions/[CONDITION_ID] [CONDITION_ID] is assigned by Cloud Monitoring when the condition is created as part of a new or updated alerting policy.When calling the alertPolicies.create method, do not include the name field in the conditions of the requested alerting policy. Cloud Monitoring creates the condition identifiers and includes them in the new policy.When calling the alertPolicies.update method to update a policy, including a condition name causes the existing condition to be updated. Conditions without names are added to the updated policy. Existing conditions are deleted if they are not updated.Best practice is to preserve [CONDITION_ID] if you make only small changes, such as those to condition thresholds, durations, or trigger values. Otherwise, treat the change as a new condition and let the existing condition be deleted.

conditionAbsent MetricAbsence: A condition that checks that a time series continues to receive new data points.
conditionMatchedLog LogMatch: A condition that checks for log messages matching given constraints. If set, no other conditions can be present.
conditionMonitoringQueryLanguage MonitoringQueryLanguageCondition: A condition that uses the Monitoring Query Language to define alerts.
conditionPrometheusQueryLanguage PrometheusQueryLanguageCondition: A condition that uses the Prometheus query language to define alerts.
conditionThreshold MetricThreshold: A condition that compares a time series against a threshold.
displayName string: A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple conditions in the same policy.
name string: Required if the condition exists. The unique resource name for this condition. Its format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[POLICY_ID]/conditions/[CONDITION_ID] [CONDITION_ID] is assigned by Cloud Monitoring when the condition is created as part of a new or updated alerting policy.When calling the alertPolicies.create method, do not include the name field in the conditions of the requested alerting policy. Cloud Monitoring creates the condition identifiers and includes them in the new policy.When calling the alertPolicies.update method to update a policy, including a condition name causes the existing condition to be updated. Conditions without names are added to the updated policy. Existing conditions are deleted if they are not updated.Best practice is to preserve [CONDITION_ID] if you make only small changes, such as those to condition thresholds, durations, or trigger values. Otherwise, treat the change as a new condition and let the existing condition be deleted.

condition_absent MetricAbsence: A condition that checks that a time series continues to receive new data points.
condition_matched_log LogMatch: A condition that checks for log messages matching given constraints. If set, no other conditions can be present.
condition_monitoring_query_language MonitoringQueryLanguageCondition: A condition that uses the Monitoring Query Language to define alerts.
condition_prometheus_query_language PrometheusQueryLanguageCondition: A condition that uses the Prometheus query language to define alerts.
condition_threshold MetricThreshold: A condition that compares a time series against a threshold.
display_name str: A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple conditions in the same policy.
name str: Required if the condition exists. The unique resource name for this condition. Its format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[POLICY_ID]/conditions/[CONDITION_ID] [CONDITION_ID] is assigned by Cloud Monitoring when the condition is created as part of a new or updated alerting policy.When calling the alertPolicies.create method, do not include the name field in the conditions of the requested alerting policy. Cloud Monitoring creates the condition identifiers and includes them in the new policy.When calling the alertPolicies.update method to update a policy, including a condition name causes the existing condition to be updated. Conditions without names are added to the updated policy. Existing conditions are deleted if they are not updated.Best practice is to preserve [CONDITION_ID] if you make only small changes, such as those to condition thresholds, durations, or trigger values. Otherwise, treat the change as a new condition and let the existing condition be deleted.

conditionAbsent Property Map: A condition that checks that a time series continues to receive new data points.
conditionMatchedLog Property Map: A condition that checks for log messages matching given constraints. If set, no other conditions can be present.
conditionMonitoringQueryLanguage Property Map: A condition that uses the Monitoring Query Language to define alerts.
conditionPrometheusQueryLanguage Property Map: A condition that uses the Prometheus query language to define alerts.
conditionThreshold Property Map: A condition that compares a time series against a threshold.
displayName String: A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple conditions in the same policy.
name String: Required if the condition exists. The unique resource name for this condition. Its format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[POLICY_ID]/conditions/[CONDITION_ID] [CONDITION_ID] is assigned by Cloud Monitoring when the condition is created as part of a new or updated alerting policy.When calling the alertPolicies.create method, do not include the name field in the conditions of the requested alerting policy. Cloud Monitoring creates the condition identifiers and includes them in the new policy.When calling the alertPolicies.update method to update a policy, including a condition name causes the existing condition to be updated. Conditions without names are added to the updated policy. Existing conditions are deleted if they are not updated.Best practice is to preserve [CONDITION_ID] if you make only small changes, such as those to condition thresholds, durations, or trigger values. Otherwise, treat the change as a new condition and let the existing condition be deleted.

ConditionResponse, ConditionResponseArgs

ConditionAbsent Pulumi.GoogleNative.Monitoring.V3.Inputs.MetricAbsenceResponse: A condition that checks that a time series continues to receive new data points.
ConditionMatchedLog Pulumi.GoogleNative.Monitoring.V3.Inputs.LogMatchResponse: A condition that checks for log messages matching given constraints. If set, no other conditions can be present.
ConditionMonitoringQueryLanguage Pulumi.GoogleNative.Monitoring.V3.Inputs.MonitoringQueryLanguageConditionResponse: A condition that uses the Monitoring Query Language to define alerts.
ConditionPrometheusQueryLanguage Pulumi.GoogleNative.Monitoring.V3.Inputs.PrometheusQueryLanguageConditionResponse: A condition that uses the Prometheus query language to define alerts.
ConditionThreshold Pulumi.GoogleNative.Monitoring.V3.Inputs.MetricThresholdResponse: A condition that compares a time series against a threshold.
DisplayName string: A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple conditions in the same policy.
Name string: Required if the condition exists. The unique resource name for this condition. Its format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[POLICY_ID]/conditions/[CONDITION_ID] [CONDITION_ID] is assigned by Cloud Monitoring when the condition is created as part of a new or updated alerting policy.When calling the alertPolicies.create method, do not include the name field in the conditions of the requested alerting policy. Cloud Monitoring creates the condition identifiers and includes them in the new policy.When calling the alertPolicies.update method to update a policy, including a condition name causes the existing condition to be updated. Conditions without names are added to the updated policy. Existing conditions are deleted if they are not updated.Best practice is to preserve [CONDITION_ID] if you make only small changes, such as those to condition thresholds, durations, or trigger values. Otherwise, treat the change as a new condition and let the existing condition be deleted.

ConditionAbsent MetricAbsenceResponse: A condition that checks that a time series continues to receive new data points.
ConditionMatchedLog LogMatchResponse: A condition that checks for log messages matching given constraints. If set, no other conditions can be present.
ConditionMonitoringQueryLanguage MonitoringQueryLanguageConditionResponse: A condition that uses the Monitoring Query Language to define alerts.
ConditionPrometheusQueryLanguage PrometheusQueryLanguageConditionResponse: A condition that uses the Prometheus query language to define alerts.
ConditionThreshold MetricThresholdResponse: A condition that compares a time series against a threshold.
DisplayName string: A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple conditions in the same policy.
Name string: Required if the condition exists. The unique resource name for this condition. Its format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[POLICY_ID]/conditions/[CONDITION_ID] [CONDITION_ID] is assigned by Cloud Monitoring when the condition is created as part of a new or updated alerting policy.When calling the alertPolicies.create method, do not include the name field in the conditions of the requested alerting policy. Cloud Monitoring creates the condition identifiers and includes them in the new policy.When calling the alertPolicies.update method to update a policy, including a condition name causes the existing condition to be updated. Conditions without names are added to the updated policy. Existing conditions are deleted if they are not updated.Best practice is to preserve [CONDITION_ID] if you make only small changes, such as those to condition thresholds, durations, or trigger values. Otherwise, treat the change as a new condition and let the existing condition be deleted.

conditionAbsent MetricAbsenceResponse: A condition that checks that a time series continues to receive new data points.
conditionMatchedLog LogMatchResponse: A condition that checks for log messages matching given constraints. If set, no other conditions can be present.
conditionMonitoringQueryLanguage MonitoringQueryLanguageConditionResponse: A condition that uses the Monitoring Query Language to define alerts.
conditionPrometheusQueryLanguage PrometheusQueryLanguageConditionResponse: A condition that uses the Prometheus query language to define alerts.
conditionThreshold MetricThresholdResponse: A condition that compares a time series against a threshold.
displayName String: A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple conditions in the same policy.
name String: Required if the condition exists. The unique resource name for this condition. Its format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[POLICY_ID]/conditions/[CONDITION_ID] [CONDITION_ID] is assigned by Cloud Monitoring when the condition is created as part of a new or updated alerting policy.When calling the alertPolicies.create method, do not include the name field in the conditions of the requested alerting policy. Cloud Monitoring creates the condition identifiers and includes them in the new policy.When calling the alertPolicies.update method to update a policy, including a condition name causes the existing condition to be updated. Conditions without names are added to the updated policy. Existing conditions are deleted if they are not updated.Best practice is to preserve [CONDITION_ID] if you make only small changes, such as those to condition thresholds, durations, or trigger values. Otherwise, treat the change as a new condition and let the existing condition be deleted.

conditionAbsent MetricAbsenceResponse: A condition that checks that a time series continues to receive new data points.
conditionMatchedLog LogMatchResponse: A condition that checks for log messages matching given constraints. If set, no other conditions can be present.
conditionMonitoringQueryLanguage MonitoringQueryLanguageConditionResponse: A condition that uses the Monitoring Query Language to define alerts.
conditionPrometheusQueryLanguage PrometheusQueryLanguageConditionResponse: A condition that uses the Prometheus query language to define alerts.
conditionThreshold MetricThresholdResponse: A condition that compares a time series against a threshold.
displayName string: A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple conditions in the same policy.
name string: Required if the condition exists. The unique resource name for this condition. Its format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[POLICY_ID]/conditions/[CONDITION_ID] [CONDITION_ID] is assigned by Cloud Monitoring when the condition is created as part of a new or updated alerting policy.When calling the alertPolicies.create method, do not include the name field in the conditions of the requested alerting policy. Cloud Monitoring creates the condition identifiers and includes them in the new policy.When calling the alertPolicies.update method to update a policy, including a condition name causes the existing condition to be updated. Conditions without names are added to the updated policy. Existing conditions are deleted if they are not updated.Best practice is to preserve [CONDITION_ID] if you make only small changes, such as those to condition thresholds, durations, or trigger values. Otherwise, treat the change as a new condition and let the existing condition be deleted.

condition_absent MetricAbsenceResponse: A condition that checks that a time series continues to receive new data points.
condition_matched_log LogMatchResponse: A condition that checks for log messages matching given constraints. If set, no other conditions can be present.
condition_monitoring_query_language MonitoringQueryLanguageConditionResponse: A condition that uses the Monitoring Query Language to define alerts.
condition_prometheus_query_language PrometheusQueryLanguageConditionResponse: A condition that uses the Prometheus query language to define alerts.
condition_threshold MetricThresholdResponse: A condition that compares a time series against a threshold.
display_name str: A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple conditions in the same policy.
name str: Required if the condition exists. The unique resource name for this condition. Its format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[POLICY_ID]/conditions/[CONDITION_ID] [CONDITION_ID] is assigned by Cloud Monitoring when the condition is created as part of a new or updated alerting policy.When calling the alertPolicies.create method, do not include the name field in the conditions of the requested alerting policy. Cloud Monitoring creates the condition identifiers and includes them in the new policy.When calling the alertPolicies.update method to update a policy, including a condition name causes the existing condition to be updated. Conditions without names are added to the updated policy. Existing conditions are deleted if they are not updated.Best practice is to preserve [CONDITION_ID] if you make only small changes, such as those to condition thresholds, durations, or trigger values. Otherwise, treat the change as a new condition and let the existing condition be deleted.

conditionAbsent Property Map: A condition that checks that a time series continues to receive new data points.
conditionMatchedLog Property Map: A condition that checks for log messages matching given constraints. If set, no other conditions can be present.
conditionMonitoringQueryLanguage Property Map: A condition that uses the Monitoring Query Language to define alerts.
conditionPrometheusQueryLanguage Property Map: A condition that uses the Prometheus query language to define alerts.
conditionThreshold Property Map: A condition that compares a time series against a threshold.
displayName String: A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple conditions in the same policy.
name String: Required if the condition exists. The unique resource name for this condition. Its format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[POLICY_ID]/conditions/[CONDITION_ID] [CONDITION_ID] is assigned by Cloud Monitoring when the condition is created as part of a new or updated alerting policy.When calling the alertPolicies.create method, do not include the name field in the conditions of the requested alerting policy. Cloud Monitoring creates the condition identifiers and includes them in the new policy.When calling the alertPolicies.update method to update a policy, including a condition name causes the existing condition to be updated. Conditions without names are added to the updated policy. Existing conditions are deleted if they are not updated.Best practice is to preserve [CONDITION_ID] if you make only small changes, such as those to condition thresholds, durations, or trigger values. Otherwise, treat the change as a new condition and let the existing condition be deleted.

Documentation, DocumentationArgs

Content string: The body of the documentation, interpreted according to mime_type. The content may not exceed 8,192 Unicode characters and may not exceed more than 10,240 bytes when encoded in UTF-8 format, whichever is smaller. This text can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables).
MimeType string: The format of the content field. Presently, only the value "text/markdown" is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
Subject string: Optional. The subject line of the notification. The subject line may not exceed 10,240 bytes. In notifications generated by this policy, the contents of the subject line after variable expansion will be truncated to 255 bytes or shorter at the latest UTF-8 character boundary. The 255-byte limit is recommended by this thread (https://stackoverflow.com/questions/1592291/what-is-the-email-subject-length-limit). It is both the limit imposed by some third-party ticketing products and it is common to define textual fields in databases as VARCHAR(255).The contents of the subject line can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). If this field is missing or empty, a default subject line will be generated.

Content string: The body of the documentation, interpreted according to mime_type. The content may not exceed 8,192 Unicode characters and may not exceed more than 10,240 bytes when encoded in UTF-8 format, whichever is smaller. This text can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables).
MimeType string: The format of the content field. Presently, only the value "text/markdown" is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
Subject string: Optional. The subject line of the notification. The subject line may not exceed 10,240 bytes. In notifications generated by this policy, the contents of the subject line after variable expansion will be truncated to 255 bytes or shorter at the latest UTF-8 character boundary. The 255-byte limit is recommended by this thread (https://stackoverflow.com/questions/1592291/what-is-the-email-subject-length-limit). It is both the limit imposed by some third-party ticketing products and it is common to define textual fields in databases as VARCHAR(255).The contents of the subject line can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). If this field is missing or empty, a default subject line will be generated.

content String: The body of the documentation, interpreted according to mime_type. The content may not exceed 8,192 Unicode characters and may not exceed more than 10,240 bytes when encoded in UTF-8 format, whichever is smaller. This text can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables).
mimeType String: The format of the content field. Presently, only the value "text/markdown" is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
subject String: Optional. The subject line of the notification. The subject line may not exceed 10,240 bytes. In notifications generated by this policy, the contents of the subject line after variable expansion will be truncated to 255 bytes or shorter at the latest UTF-8 character boundary. The 255-byte limit is recommended by this thread (https://stackoverflow.com/questions/1592291/what-is-the-email-subject-length-limit). It is both the limit imposed by some third-party ticketing products and it is common to define textual fields in databases as VARCHAR(255).The contents of the subject line can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). If this field is missing or empty, a default subject line will be generated.

content string: The body of the documentation, interpreted according to mime_type. The content may not exceed 8,192 Unicode characters and may not exceed more than 10,240 bytes when encoded in UTF-8 format, whichever is smaller. This text can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables).
mimeType string: The format of the content field. Presently, only the value "text/markdown" is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
subject string: Optional. The subject line of the notification. The subject line may not exceed 10,240 bytes. In notifications generated by this policy, the contents of the subject line after variable expansion will be truncated to 255 bytes or shorter at the latest UTF-8 character boundary. The 255-byte limit is recommended by this thread (https://stackoverflow.com/questions/1592291/what-is-the-email-subject-length-limit). It is both the limit imposed by some third-party ticketing products and it is common to define textual fields in databases as VARCHAR(255).The contents of the subject line can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). If this field is missing or empty, a default subject line will be generated.

content str: The body of the documentation, interpreted according to mime_type. The content may not exceed 8,192 Unicode characters and may not exceed more than 10,240 bytes when encoded in UTF-8 format, whichever is smaller. This text can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables).
mime_type str: The format of the content field. Presently, only the value "text/markdown" is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
subject str: Optional. The subject line of the notification. The subject line may not exceed 10,240 bytes. In notifications generated by this policy, the contents of the subject line after variable expansion will be truncated to 255 bytes or shorter at the latest UTF-8 character boundary. The 255-byte limit is recommended by this thread (https://stackoverflow.com/questions/1592291/what-is-the-email-subject-length-limit). It is both the limit imposed by some third-party ticketing products and it is common to define textual fields in databases as VARCHAR(255).The contents of the subject line can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). If this field is missing or empty, a default subject line will be generated.

content String: The body of the documentation, interpreted according to mime_type. The content may not exceed 8,192 Unicode characters and may not exceed more than 10,240 bytes when encoded in UTF-8 format, whichever is smaller. This text can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables).
mimeType String: The format of the content field. Presently, only the value "text/markdown" is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
subject String: Optional. The subject line of the notification. The subject line may not exceed 10,240 bytes. In notifications generated by this policy, the contents of the subject line after variable expansion will be truncated to 255 bytes or shorter at the latest UTF-8 character boundary. The 255-byte limit is recommended by this thread (https://stackoverflow.com/questions/1592291/what-is-the-email-subject-length-limit). It is both the limit imposed by some third-party ticketing products and it is common to define textual fields in databases as VARCHAR(255).The contents of the subject line can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). If this field is missing or empty, a default subject line will be generated.

DocumentationResponse, DocumentationResponseArgs

Content string: The body of the documentation, interpreted according to mime_type. The content may not exceed 8,192 Unicode characters and may not exceed more than 10,240 bytes when encoded in UTF-8 format, whichever is smaller. This text can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables).
MimeType string: The format of the content field. Presently, only the value "text/markdown" is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
Subject string: Optional. The subject line of the notification. The subject line may not exceed 10,240 bytes. In notifications generated by this policy, the contents of the subject line after variable expansion will be truncated to 255 bytes or shorter at the latest UTF-8 character boundary. The 255-byte limit is recommended by this thread (https://stackoverflow.com/questions/1592291/what-is-the-email-subject-length-limit). It is both the limit imposed by some third-party ticketing products and it is common to define textual fields in databases as VARCHAR(255).The contents of the subject line can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). If this field is missing or empty, a default subject line will be generated.

Content string: The body of the documentation, interpreted according to mime_type. The content may not exceed 8,192 Unicode characters and may not exceed more than 10,240 bytes when encoded in UTF-8 format, whichever is smaller. This text can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables).
MimeType string: The format of the content field. Presently, only the value "text/markdown" is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
Subject string: Optional. The subject line of the notification. The subject line may not exceed 10,240 bytes. In notifications generated by this policy, the contents of the subject line after variable expansion will be truncated to 255 bytes or shorter at the latest UTF-8 character boundary. The 255-byte limit is recommended by this thread (https://stackoverflow.com/questions/1592291/what-is-the-email-subject-length-limit). It is both the limit imposed by some third-party ticketing products and it is common to define textual fields in databases as VARCHAR(255).The contents of the subject line can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). If this field is missing or empty, a default subject line will be generated.

content String: The body of the documentation, interpreted according to mime_type. The content may not exceed 8,192 Unicode characters and may not exceed more than 10,240 bytes when encoded in UTF-8 format, whichever is smaller. This text can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables).
mimeType String: The format of the content field. Presently, only the value "text/markdown" is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
subject String: Optional. The subject line of the notification. The subject line may not exceed 10,240 bytes. In notifications generated by this policy, the contents of the subject line after variable expansion will be truncated to 255 bytes or shorter at the latest UTF-8 character boundary. The 255-byte limit is recommended by this thread (https://stackoverflow.com/questions/1592291/what-is-the-email-subject-length-limit). It is both the limit imposed by some third-party ticketing products and it is common to define textual fields in databases as VARCHAR(255).The contents of the subject line can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). If this field is missing or empty, a default subject line will be generated.

content string: The body of the documentation, interpreted according to mime_type. The content may not exceed 8,192 Unicode characters and may not exceed more than 10,240 bytes when encoded in UTF-8 format, whichever is smaller. This text can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables).
mimeType string: The format of the content field. Presently, only the value "text/markdown" is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
subject string: Optional. The subject line of the notification. The subject line may not exceed 10,240 bytes. In notifications generated by this policy, the contents of the subject line after variable expansion will be truncated to 255 bytes or shorter at the latest UTF-8 character boundary. The 255-byte limit is recommended by this thread (https://stackoverflow.com/questions/1592291/what-is-the-email-subject-length-limit). It is both the limit imposed by some third-party ticketing products and it is common to define textual fields in databases as VARCHAR(255).The contents of the subject line can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). If this field is missing or empty, a default subject line will be generated.

content str: The body of the documentation, interpreted according to mime_type. The content may not exceed 8,192 Unicode characters and may not exceed more than 10,240 bytes when encoded in UTF-8 format, whichever is smaller. This text can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables).
mime_type str: The format of the content field. Presently, only the value "text/markdown" is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
subject str: Optional. The subject line of the notification. The subject line may not exceed 10,240 bytes. In notifications generated by this policy, the contents of the subject line after variable expansion will be truncated to 255 bytes or shorter at the latest UTF-8 character boundary. The 255-byte limit is recommended by this thread (https://stackoverflow.com/questions/1592291/what-is-the-email-subject-length-limit). It is both the limit imposed by some third-party ticketing products and it is common to define textual fields in databases as VARCHAR(255).The contents of the subject line can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). If this field is missing or empty, a default subject line will be generated.

content String: The body of the documentation, interpreted according to mime_type. The content may not exceed 8,192 Unicode characters and may not exceed more than 10,240 bytes when encoded in UTF-8 format, whichever is smaller. This text can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables).
mimeType String: The format of the content field. Presently, only the value "text/markdown" is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
subject String: Optional. The subject line of the notification. The subject line may not exceed 10,240 bytes. In notifications generated by this policy, the contents of the subject line after variable expansion will be truncated to 255 bytes or shorter at the latest UTF-8 character boundary. The 255-byte limit is recommended by this thread (https://stackoverflow.com/questions/1592291/what-is-the-email-subject-length-limit). It is both the limit imposed by some third-party ticketing products and it is common to define textual fields in databases as VARCHAR(255).The contents of the subject line can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). If this field is missing or empty, a default subject line will be generated.

ForecastOptions, ForecastOptionsArgs

ForecastHorizon string: The length of time into the future to forecast whether a time series will violate the threshold. If the predicted value is found to violate the threshold, and the violation is observed in all forecasts made for the configured duration, then the time series is considered to be failing. The forecast horizon can range from 1 hour to 60 hours.

ForecastHorizon string: The length of time into the future to forecast whether a time series will violate the threshold. If the predicted value is found to violate the threshold, and the violation is observed in all forecasts made for the configured duration, then the time series is considered to be failing. The forecast horizon can range from 1 hour to 60 hours.

forecastHorizon String: The length of time into the future to forecast whether a time series will violate the threshold. If the predicted value is found to violate the threshold, and the violation is observed in all forecasts made for the configured duration, then the time series is considered to be failing. The forecast horizon can range from 1 hour to 60 hours.

forecastHorizon string: The length of time into the future to forecast whether a time series will violate the threshold. If the predicted value is found to violate the threshold, and the violation is observed in all forecasts made for the configured duration, then the time series is considered to be failing. The forecast horizon can range from 1 hour to 60 hours.

forecast_horizon str: The length of time into the future to forecast whether a time series will violate the threshold. If the predicted value is found to violate the threshold, and the violation is observed in all forecasts made for the configured duration, then the time series is considered to be failing. The forecast horizon can range from 1 hour to 60 hours.

forecastHorizon String: The length of time into the future to forecast whether a time series will violate the threshold. If the predicted value is found to violate the threshold, and the violation is observed in all forecasts made for the configured duration, then the time series is considered to be failing. The forecast horizon can range from 1 hour to 60 hours.

ForecastOptionsResponse, ForecastOptionsResponseArgs

ForecastHorizon string: The length of time into the future to forecast whether a time series will violate the threshold. If the predicted value is found to violate the threshold, and the violation is observed in all forecasts made for the configured duration, then the time series is considered to be failing. The forecast horizon can range from 1 hour to 60 hours.

ForecastHorizon string: The length of time into the future to forecast whether a time series will violate the threshold. If the predicted value is found to violate the threshold, and the violation is observed in all forecasts made for the configured duration, then the time series is considered to be failing. The forecast horizon can range from 1 hour to 60 hours.

forecastHorizon String: The length of time into the future to forecast whether a time series will violate the threshold. If the predicted value is found to violate the threshold, and the violation is observed in all forecasts made for the configured duration, then the time series is considered to be failing. The forecast horizon can range from 1 hour to 60 hours.

forecastHorizon string: The length of time into the future to forecast whether a time series will violate the threshold. If the predicted value is found to violate the threshold, and the violation is observed in all forecasts made for the configured duration, then the time series is considered to be failing. The forecast horizon can range from 1 hour to 60 hours.

forecast_horizon str: The length of time into the future to forecast whether a time series will violate the threshold. If the predicted value is found to violate the threshold, and the violation is observed in all forecasts made for the configured duration, then the time series is considered to be failing. The forecast horizon can range from 1 hour to 60 hours.

forecastHorizon String: The length of time into the future to forecast whether a time series will violate the threshold. If the predicted value is found to violate the threshold, and the violation is observed in all forecasts made for the configured duration, then the time series is considered to be failing. The forecast horizon can range from 1 hour to 60 hours.

LogMatch, LogMatchArgs

Filter string: A logs-based filter. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries) for how this filter should be constructed.
LabelExtractors Dictionary<string, string>: Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that match filter. Each combination of extracted values is treated as a separate rule for the purposes of triggering notifications. Label keys and corresponding values can be used in notifications generated by this condition.Please see the documentation on logs-based metric valueExtractors (https://cloud.google.com/logging/docs/reference/v2/rest/v2/projects.metrics#LogMetric.FIELDS.value_extractor) for syntax and examples.

Filter string: A logs-based filter. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries) for how this filter should be constructed.
LabelExtractors map[string]string: Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that match filter. Each combination of extracted values is treated as a separate rule for the purposes of triggering notifications. Label keys and corresponding values can be used in notifications generated by this condition.Please see the documentation on logs-based metric valueExtractors (https://cloud.google.com/logging/docs/reference/v2/rest/v2/projects.metrics#LogMetric.FIELDS.value_extractor) for syntax and examples.

filter String: A logs-based filter. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries) for how this filter should be constructed.
labelExtractors Map<String,String>: Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that match filter. Each combination of extracted values is treated as a separate rule for the purposes of triggering notifications. Label keys and corresponding values can be used in notifications generated by this condition.Please see the documentation on logs-based metric valueExtractors (https://cloud.google.com/logging/docs/reference/v2/rest/v2/projects.metrics#LogMetric.FIELDS.value_extractor) for syntax and examples.

filter string: A logs-based filter. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries) for how this filter should be constructed.
labelExtractors {[key: string]: string}: Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that match filter. Each combination of extracted values is treated as a separate rule for the purposes of triggering notifications. Label keys and corresponding values can be used in notifications generated by this condition.Please see the documentation on logs-based metric valueExtractors (https://cloud.google.com/logging/docs/reference/v2/rest/v2/projects.metrics#LogMetric.FIELDS.value_extractor) for syntax and examples.

filter str: A logs-based filter. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries) for how this filter should be constructed.
label_extractors Mapping[str, str]: Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that match filter. Each combination of extracted values is treated as a separate rule for the purposes of triggering notifications. Label keys and corresponding values can be used in notifications generated by this condition.Please see the documentation on logs-based metric valueExtractors (https://cloud.google.com/logging/docs/reference/v2/rest/v2/projects.metrics#LogMetric.FIELDS.value_extractor) for syntax and examples.

filter String: A logs-based filter. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries) for how this filter should be constructed.
labelExtractors Map<String>: Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that match filter. Each combination of extracted values is treated as a separate rule for the purposes of triggering notifications. Label keys and corresponding values can be used in notifications generated by this condition.Please see the documentation on logs-based metric valueExtractors (https://cloud.google.com/logging/docs/reference/v2/rest/v2/projects.metrics#LogMetric.FIELDS.value_extractor) for syntax and examples.

LogMatchResponse, LogMatchResponseArgs

Filter string: A logs-based filter. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries) for how this filter should be constructed.
LabelExtractors Dictionary<string, string>: Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that match filter. Each combination of extracted values is treated as a separate rule for the purposes of triggering notifications. Label keys and corresponding values can be used in notifications generated by this condition.Please see the documentation on logs-based metric valueExtractors (https://cloud.google.com/logging/docs/reference/v2/rest/v2/projects.metrics#LogMetric.FIELDS.value_extractor) for syntax and examples.

Filter string: A logs-based filter. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries) for how this filter should be constructed.
LabelExtractors map[string]string: Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that match filter. Each combination of extracted values is treated as a separate rule for the purposes of triggering notifications. Label keys and corresponding values can be used in notifications generated by this condition.Please see the documentation on logs-based metric valueExtractors (https://cloud.google.com/logging/docs/reference/v2/rest/v2/projects.metrics#LogMetric.FIELDS.value_extractor) for syntax and examples.

filter String: A logs-based filter. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries) for how this filter should be constructed.
labelExtractors Map<String,String>: Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that match filter. Each combination of extracted values is treated as a separate rule for the purposes of triggering notifications. Label keys and corresponding values can be used in notifications generated by this condition.Please see the documentation on logs-based metric valueExtractors (https://cloud.google.com/logging/docs/reference/v2/rest/v2/projects.metrics#LogMetric.FIELDS.value_extractor) for syntax and examples.

filter string: A logs-based filter. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries) for how this filter should be constructed.
labelExtractors {[key: string]: string}: Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that match filter. Each combination of extracted values is treated as a separate rule for the purposes of triggering notifications. Label keys and corresponding values can be used in notifications generated by this condition.Please see the documentation on logs-based metric valueExtractors (https://cloud.google.com/logging/docs/reference/v2/rest/v2/projects.metrics#LogMetric.FIELDS.value_extractor) for syntax and examples.

filter str: A logs-based filter. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries) for how this filter should be constructed.
label_extractors Mapping[str, str]: Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that match filter. Each combination of extracted values is treated as a separate rule for the purposes of triggering notifications. Label keys and corresponding values can be used in notifications generated by this condition.Please see the documentation on logs-based metric valueExtractors (https://cloud.google.com/logging/docs/reference/v2/rest/v2/projects.metrics#LogMetric.FIELDS.value_extractor) for syntax and examples.

filter String: A logs-based filter. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries) for how this filter should be constructed.
labelExtractors Map<String>: Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that match filter. Each combination of extracted values is treated as a separate rule for the purposes of triggering notifications. Label keys and corresponding values can be used in notifications generated by this condition.Please see the documentation on logs-based metric valueExtractors (https://cloud.google.com/logging/docs/reference/v2/rest/v2/projects.metrics#LogMetric.FIELDS.value_extractor) for syntax and examples.

MetricAbsence, MetricAbsenceArgs

Filter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
Aggregations List<Pulumi.GoogleNative.Monitoring.V3.Inputs.Aggregation>: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
Duration string: The amount of time that a time series must fail to report new data to be considered failing. The minimum value of this field is 120 seconds. Larger values that are a multiple of a minute--for example, 240 or 300 seconds--are supported. If an invalid value is given, an error will be returned. The Duration.nanos field is ignored.
Trigger Pulumi.GoogleNative.Monitoring.V3.Inputs.Trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations.

Filter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
Aggregations []Aggregation: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
Duration string: The amount of time that a time series must fail to report new data to be considered failing. The minimum value of this field is 120 seconds. Larger values that are a multiple of a minute--for example, 240 or 300 seconds--are supported. If an invalid value is given, an error will be returned. The Duration.nanos field is ignored.
Trigger Trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations.

filter String: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
aggregations List<Aggregation>: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
duration String: The amount of time that a time series must fail to report new data to be considered failing. The minimum value of this field is 120 seconds. Larger values that are a multiple of a minute--for example, 240 or 300 seconds--are supported. If an invalid value is given, an error will be returned. The Duration.nanos field is ignored.
trigger Trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations.

filter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
aggregations Aggregation[]: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
duration string: The amount of time that a time series must fail to report new data to be considered failing. The minimum value of this field is 120 seconds. Larger values that are a multiple of a minute--for example, 240 or 300 seconds--are supported. If an invalid value is given, an error will be returned. The Duration.nanos field is ignored.
trigger Trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations.

filter str: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
aggregations Sequence[Aggregation]: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
duration str: The amount of time that a time series must fail to report new data to be considered failing. The minimum value of this field is 120 seconds. Larger values that are a multiple of a minute--for example, 240 or 300 seconds--are supported. If an invalid value is given, an error will be returned. The Duration.nanos field is ignored.
trigger Trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations.

filter String: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
aggregations List<Property Map>: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
duration String: The amount of time that a time series must fail to report new data to be considered failing. The minimum value of this field is 120 seconds. Larger values that are a multiple of a minute--for example, 240 or 300 seconds--are supported. If an invalid value is given, an error will be returned. The Duration.nanos field is ignored.
trigger Property Map: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations.

MetricAbsenceResponse, MetricAbsenceResponseArgs

Aggregations List<Pulumi.GoogleNative.Monitoring.V3.Inputs.AggregationResponse>: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
Duration string: The amount of time that a time series must fail to report new data to be considered failing. The minimum value of this field is 120 seconds. Larger values that are a multiple of a minute--for example, 240 or 300 seconds--are supported. If an invalid value is given, an error will be returned. The Duration.nanos field is ignored.
Filter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
Trigger Pulumi.GoogleNative.Monitoring.V3.Inputs.TriggerResponse: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations.

Aggregations []AggregationResponse: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
Duration string: The amount of time that a time series must fail to report new data to be considered failing. The minimum value of this field is 120 seconds. Larger values that are a multiple of a minute--for example, 240 or 300 seconds--are supported. If an invalid value is given, an error will be returned. The Duration.nanos field is ignored.
Filter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
Trigger TriggerResponse: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations.

aggregations List<AggregationResponse>: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
duration String: The amount of time that a time series must fail to report new data to be considered failing. The minimum value of this field is 120 seconds. Larger values that are a multiple of a minute--for example, 240 or 300 seconds--are supported. If an invalid value is given, an error will be returned. The Duration.nanos field is ignored.
filter String: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
trigger TriggerResponse: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations.

aggregations AggregationResponse[]: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
duration string: The amount of time that a time series must fail to report new data to be considered failing. The minimum value of this field is 120 seconds. Larger values that are a multiple of a minute--for example, 240 or 300 seconds--are supported. If an invalid value is given, an error will be returned. The Duration.nanos field is ignored.
filter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
trigger TriggerResponse: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations.

aggregations Sequence[AggregationResponse]: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
duration str: The amount of time that a time series must fail to report new data to be considered failing. The minimum value of this field is 120 seconds. Larger values that are a multiple of a minute--for example, 240 or 300 seconds--are supported. If an invalid value is given, an error will be returned. The Duration.nanos field is ignored.
filter str: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
trigger TriggerResponse: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations.

aggregations List<Property Map>: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
duration String: The amount of time that a time series must fail to report new data to be considered failing. The minimum value of this field is 120 seconds. Larger values that are a multiple of a minute--for example, 240 or 300 seconds--are supported. If an invalid value is given, an error will be returned. The Duration.nanos field is ignored.
filter String: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
trigger Property Map: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations.

MetricThreshold, MetricThresholdArgs

Filter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
Aggregations List<Pulumi.GoogleNative.Monitoring.V3.Inputs.Aggregation>: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
Comparison Pulumi.GoogleNative.Monitoring.V3.MetricThresholdComparison: The comparison to apply between the time series (indicated by filter and aggregation) and the threshold (indicated by threshold_value). The comparison is applied on each time series, with the time series on the left-hand side and the threshold on the right-hand side.Only COMPARISON_LT and COMPARISON_GT are supported currently.
DenominatorAggregations List<Pulumi.GoogleNative.Monitoring.V3.Inputs.Aggregation>: Specifies the alignment of data points in individual time series selected by denominatorFilter as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources).When computing ratios, the aggregations and denominator_aggregations fields must use the same alignment period and produce time series that have the same periodicity and labels.
DenominatorFilter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies a time series that should be used as the denominator of a ratio that will be compared with the threshold. If a denominator_filter is specified, the time series specified by the filter field will be used as the numerator.The filter must specify the metric type and optionally may contain restrictions on resource type, resource labels, and metric labels. This field may not exceed 2048 Unicode characters in length.
Duration string: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
EvaluationMissingData Pulumi.GoogleNative.Monitoring.V3.MetricThresholdEvaluationMissingData: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
ForecastOptions Pulumi.GoogleNative.Monitoring.V3.Inputs.ForecastOptions: When this field is present, the MetricThreshold condition forecasts whether the time series is predicted to violate the threshold within the forecast_horizon. When this field is not set, the MetricThreshold tests the current value of the timeseries against the threshold.
ThresholdValue double: A value against which to compare the time series.
Trigger Pulumi.GoogleNative.Monitoring.V3.Inputs.Trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

Filter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
Aggregations []Aggregation: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
Comparison MetricThresholdComparison: The comparison to apply between the time series (indicated by filter and aggregation) and the threshold (indicated by threshold_value). The comparison is applied on each time series, with the time series on the left-hand side and the threshold on the right-hand side.Only COMPARISON_LT and COMPARISON_GT are supported currently.
DenominatorAggregations []Aggregation: Specifies the alignment of data points in individual time series selected by denominatorFilter as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources).When computing ratios, the aggregations and denominator_aggregations fields must use the same alignment period and produce time series that have the same periodicity and labels.
DenominatorFilter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies a time series that should be used as the denominator of a ratio that will be compared with the threshold. If a denominator_filter is specified, the time series specified by the filter field will be used as the numerator.The filter must specify the metric type and optionally may contain restrictions on resource type, resource labels, and metric labels. This field may not exceed 2048 Unicode characters in length.
Duration string: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
EvaluationMissingData MetricThresholdEvaluationMissingData: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
ForecastOptions ForecastOptions: When this field is present, the MetricThreshold condition forecasts whether the time series is predicted to violate the threshold within the forecast_horizon. When this field is not set, the MetricThreshold tests the current value of the timeseries against the threshold.
ThresholdValue float64: A value against which to compare the time series.
Trigger Trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

filter String: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
aggregations List<Aggregation>: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
comparison MetricThresholdComparison: The comparison to apply between the time series (indicated by filter and aggregation) and the threshold (indicated by threshold_value). The comparison is applied on each time series, with the time series on the left-hand side and the threshold on the right-hand side.Only COMPARISON_LT and COMPARISON_GT are supported currently.
denominatorAggregations List<Aggregation>: Specifies the alignment of data points in individual time series selected by denominatorFilter as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources).When computing ratios, the aggregations and denominator_aggregations fields must use the same alignment period and produce time series that have the same periodicity and labels.
denominatorFilter String: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies a time series that should be used as the denominator of a ratio that will be compared with the threshold. If a denominator_filter is specified, the time series specified by the filter field will be used as the numerator.The filter must specify the metric type and optionally may contain restrictions on resource type, resource labels, and metric labels. This field may not exceed 2048 Unicode characters in length.
duration String: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
evaluationMissingData MetricThresholdEvaluationMissingData: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
forecastOptions ForecastOptions: When this field is present, the MetricThreshold condition forecasts whether the time series is predicted to violate the threshold within the forecast_horizon. When this field is not set, the MetricThreshold tests the current value of the timeseries against the threshold.
thresholdValue Double: A value against which to compare the time series.
trigger Trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

filter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
aggregations Aggregation[]: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
comparison MetricThresholdComparison: The comparison to apply between the time series (indicated by filter and aggregation) and the threshold (indicated by threshold_value). The comparison is applied on each time series, with the time series on the left-hand side and the threshold on the right-hand side.Only COMPARISON_LT and COMPARISON_GT are supported currently.
denominatorAggregations Aggregation[]: Specifies the alignment of data points in individual time series selected by denominatorFilter as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources).When computing ratios, the aggregations and denominator_aggregations fields must use the same alignment period and produce time series that have the same periodicity and labels.
denominatorFilter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies a time series that should be used as the denominator of a ratio that will be compared with the threshold. If a denominator_filter is specified, the time series specified by the filter field will be used as the numerator.The filter must specify the metric type and optionally may contain restrictions on resource type, resource labels, and metric labels. This field may not exceed 2048 Unicode characters in length.
duration string: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
evaluationMissingData MetricThresholdEvaluationMissingData: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
forecastOptions ForecastOptions: When this field is present, the MetricThreshold condition forecasts whether the time series is predicted to violate the threshold within the forecast_horizon. When this field is not set, the MetricThreshold tests the current value of the timeseries against the threshold.
thresholdValue number: A value against which to compare the time series.
trigger Trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

filter str: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
aggregations Sequence[Aggregation]: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
comparison MetricThresholdComparison: The comparison to apply between the time series (indicated by filter and aggregation) and the threshold (indicated by threshold_value). The comparison is applied on each time series, with the time series on the left-hand side and the threshold on the right-hand side.Only COMPARISON_LT and COMPARISON_GT are supported currently.
denominator_aggregations Sequence[Aggregation]: Specifies the alignment of data points in individual time series selected by denominatorFilter as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources).When computing ratios, the aggregations and denominator_aggregations fields must use the same alignment period and produce time series that have the same periodicity and labels.
denominator_filter str: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies a time series that should be used as the denominator of a ratio that will be compared with the threshold. If a denominator_filter is specified, the time series specified by the filter field will be used as the numerator.The filter must specify the metric type and optionally may contain restrictions on resource type, resource labels, and metric labels. This field may not exceed 2048 Unicode characters in length.
duration str: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
evaluation_missing_data MetricThresholdEvaluationMissingData: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
forecast_options ForecastOptions: When this field is present, the MetricThreshold condition forecasts whether the time series is predicted to violate the threshold within the forecast_horizon. When this field is not set, the MetricThreshold tests the current value of the timeseries against the threshold.
threshold_value float: A value against which to compare the time series.
trigger Trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

filter String: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
aggregations List<Property Map>: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
comparison "COMPARISON_UNSPECIFIED" | "COMPARISON_GT" | "COMPARISON_GE" | "COMPARISON_LT" | "COMPARISON_LE" | "COMPARISON_EQ" | "COMPARISON_NE": The comparison to apply between the time series (indicated by filter and aggregation) and the threshold (indicated by threshold_value). The comparison is applied on each time series, with the time series on the left-hand side and the threshold on the right-hand side.Only COMPARISON_LT and COMPARISON_GT are supported currently.
denominatorAggregations List<Property Map>: Specifies the alignment of data points in individual time series selected by denominatorFilter as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources).When computing ratios, the aggregations and denominator_aggregations fields must use the same alignment period and produce time series that have the same periodicity and labels.
denominatorFilter String: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies a time series that should be used as the denominator of a ratio that will be compared with the threshold. If a denominator_filter is specified, the time series specified by the filter field will be used as the numerator.The filter must specify the metric type and optionally may contain restrictions on resource type, resource labels, and metric labels. This field may not exceed 2048 Unicode characters in length.
duration String: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
evaluationMissingData "EVALUATION_MISSING_DATA_UNSPECIFIED" | "EVALUATION_MISSING_DATA_INACTIVE" | "EVALUATION_MISSING_DATA_ACTIVE" | "EVALUATION_MISSING_DATA_NO_OP": A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
forecastOptions Property Map: When this field is present, the MetricThreshold condition forecasts whether the time series is predicted to violate the threshold within the forecast_horizon. When this field is not set, the MetricThreshold tests the current value of the timeseries against the threshold.
thresholdValue Number: A value against which to compare the time series.
trigger Property Map: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

MetricThresholdComparison, MetricThresholdComparisonArgs

ComparisonUnspecified: COMPARISON_UNSPECIFIEDNo ordering relationship is specified.
ComparisonGt: COMPARISON_GTTrue if the left argument is greater than the right argument.
ComparisonGe: COMPARISON_GETrue if the left argument is greater than or equal to the right argument.
ComparisonLt: COMPARISON_LTTrue if the left argument is less than the right argument.
ComparisonLe: COMPARISON_LETrue if the left argument is less than or equal to the right argument.
ComparisonEq: COMPARISON_EQTrue if the left argument is equal to the right argument.
ComparisonNe: COMPARISON_NETrue if the left argument is not equal to the right argument.

MetricThresholdComparisonComparisonUnspecified: COMPARISON_UNSPECIFIEDNo ordering relationship is specified.
MetricThresholdComparisonComparisonGt: COMPARISON_GTTrue if the left argument is greater than the right argument.
MetricThresholdComparisonComparisonGe: COMPARISON_GETrue if the left argument is greater than or equal to the right argument.
MetricThresholdComparisonComparisonLt: COMPARISON_LTTrue if the left argument is less than the right argument.
MetricThresholdComparisonComparisonLe: COMPARISON_LETrue if the left argument is less than or equal to the right argument.
MetricThresholdComparisonComparisonEq: COMPARISON_EQTrue if the left argument is equal to the right argument.
MetricThresholdComparisonComparisonNe: COMPARISON_NETrue if the left argument is not equal to the right argument.

ComparisonUnspecified: COMPARISON_UNSPECIFIEDNo ordering relationship is specified.
ComparisonGt: COMPARISON_GTTrue if the left argument is greater than the right argument.
ComparisonGe: COMPARISON_GETrue if the left argument is greater than or equal to the right argument.
ComparisonLt: COMPARISON_LTTrue if the left argument is less than the right argument.
ComparisonLe: COMPARISON_LETrue if the left argument is less than or equal to the right argument.
ComparisonEq: COMPARISON_EQTrue if the left argument is equal to the right argument.
ComparisonNe: COMPARISON_NETrue if the left argument is not equal to the right argument.

ComparisonUnspecified: COMPARISON_UNSPECIFIEDNo ordering relationship is specified.
ComparisonGt: COMPARISON_GTTrue if the left argument is greater than the right argument.
ComparisonGe: COMPARISON_GETrue if the left argument is greater than or equal to the right argument.
ComparisonLt: COMPARISON_LTTrue if the left argument is less than the right argument.
ComparisonLe: COMPARISON_LETrue if the left argument is less than or equal to the right argument.
ComparisonEq: COMPARISON_EQTrue if the left argument is equal to the right argument.
ComparisonNe: COMPARISON_NETrue if the left argument is not equal to the right argument.

COMPARISON_UNSPECIFIED: COMPARISON_UNSPECIFIEDNo ordering relationship is specified.
COMPARISON_GT: COMPARISON_GTTrue if the left argument is greater than the right argument.
COMPARISON_GE: COMPARISON_GETrue if the left argument is greater than or equal to the right argument.
COMPARISON_LT: COMPARISON_LTTrue if the left argument is less than the right argument.
COMPARISON_LE: COMPARISON_LETrue if the left argument is less than or equal to the right argument.
COMPARISON_EQ: COMPARISON_EQTrue if the left argument is equal to the right argument.
COMPARISON_NE: COMPARISON_NETrue if the left argument is not equal to the right argument.

"COMPARISON_UNSPECIFIED": COMPARISON_UNSPECIFIEDNo ordering relationship is specified.
"COMPARISON_GT": COMPARISON_GTTrue if the left argument is greater than the right argument.
"COMPARISON_GE": COMPARISON_GETrue if the left argument is greater than or equal to the right argument.
"COMPARISON_LT": COMPARISON_LTTrue if the left argument is less than the right argument.
"COMPARISON_LE": COMPARISON_LETrue if the left argument is less than or equal to the right argument.
"COMPARISON_EQ": COMPARISON_EQTrue if the left argument is equal to the right argument.
"COMPARISON_NE": COMPARISON_NETrue if the left argument is not equal to the right argument.

MetricThresholdEvaluationMissingData, MetricThresholdEvaluationMissingDataArgs

EvaluationMissingDataUnspecified: EVALUATION_MISSING_DATA_UNSPECIFIEDAn unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.
EvaluationMissingDataInactive: EVALUATION_MISSING_DATA_INACTIVEIf there is no data to evaluate the condition, then evaluate the condition as false.
EvaluationMissingDataActive: EVALUATION_MISSING_DATA_ACTIVEIf there is no data to evaluate the condition, then evaluate the condition as true.
EvaluationMissingDataNoOp: EVALUATION_MISSING_DATA_NO_OPDo not evaluate the condition to any value if there is no data.

MetricThresholdEvaluationMissingDataEvaluationMissingDataUnspecified: EVALUATION_MISSING_DATA_UNSPECIFIEDAn unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.
MetricThresholdEvaluationMissingDataEvaluationMissingDataInactive: EVALUATION_MISSING_DATA_INACTIVEIf there is no data to evaluate the condition, then evaluate the condition as false.
MetricThresholdEvaluationMissingDataEvaluationMissingDataActive: EVALUATION_MISSING_DATA_ACTIVEIf there is no data to evaluate the condition, then evaluate the condition as true.
MetricThresholdEvaluationMissingDataEvaluationMissingDataNoOp: EVALUATION_MISSING_DATA_NO_OPDo not evaluate the condition to any value if there is no data.

EvaluationMissingDataUnspecified: EVALUATION_MISSING_DATA_UNSPECIFIEDAn unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.
EvaluationMissingDataInactive: EVALUATION_MISSING_DATA_INACTIVEIf there is no data to evaluate the condition, then evaluate the condition as false.
EvaluationMissingDataActive: EVALUATION_MISSING_DATA_ACTIVEIf there is no data to evaluate the condition, then evaluate the condition as true.
EvaluationMissingDataNoOp: EVALUATION_MISSING_DATA_NO_OPDo not evaluate the condition to any value if there is no data.

EvaluationMissingDataUnspecified: EVALUATION_MISSING_DATA_UNSPECIFIEDAn unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.
EvaluationMissingDataInactive: EVALUATION_MISSING_DATA_INACTIVEIf there is no data to evaluate the condition, then evaluate the condition as false.
EvaluationMissingDataActive: EVALUATION_MISSING_DATA_ACTIVEIf there is no data to evaluate the condition, then evaluate the condition as true.
EvaluationMissingDataNoOp: EVALUATION_MISSING_DATA_NO_OPDo not evaluate the condition to any value if there is no data.

EVALUATION_MISSING_DATA_UNSPECIFIED: EVALUATION_MISSING_DATA_UNSPECIFIEDAn unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.
EVALUATION_MISSING_DATA_INACTIVE: EVALUATION_MISSING_DATA_INACTIVEIf there is no data to evaluate the condition, then evaluate the condition as false.
EVALUATION_MISSING_DATA_ACTIVE: EVALUATION_MISSING_DATA_ACTIVEIf there is no data to evaluate the condition, then evaluate the condition as true.
EVALUATION_MISSING_DATA_NO_OP: EVALUATION_MISSING_DATA_NO_OPDo not evaluate the condition to any value if there is no data.

"EVALUATION_MISSING_DATA_UNSPECIFIED": EVALUATION_MISSING_DATA_UNSPECIFIEDAn unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.
"EVALUATION_MISSING_DATA_INACTIVE": EVALUATION_MISSING_DATA_INACTIVEIf there is no data to evaluate the condition, then evaluate the condition as false.
"EVALUATION_MISSING_DATA_ACTIVE": EVALUATION_MISSING_DATA_ACTIVEIf there is no data to evaluate the condition, then evaluate the condition as true.
"EVALUATION_MISSING_DATA_NO_OP": EVALUATION_MISSING_DATA_NO_OPDo not evaluate the condition to any value if there is no data.

MetricThresholdResponse, MetricThresholdResponseArgs

Aggregations List<Pulumi.GoogleNative.Monitoring.V3.Inputs.AggregationResponse>: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
Comparison string: The comparison to apply between the time series (indicated by filter and aggregation) and the threshold (indicated by threshold_value). The comparison is applied on each time series, with the time series on the left-hand side and the threshold on the right-hand side.Only COMPARISON_LT and COMPARISON_GT are supported currently.
DenominatorAggregations List<Pulumi.GoogleNative.Monitoring.V3.Inputs.AggregationResponse>: Specifies the alignment of data points in individual time series selected by denominatorFilter as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources).When computing ratios, the aggregations and denominator_aggregations fields must use the same alignment period and produce time series that have the same periodicity and labels.
DenominatorFilter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies a time series that should be used as the denominator of a ratio that will be compared with the threshold. If a denominator_filter is specified, the time series specified by the filter field will be used as the numerator.The filter must specify the metric type and optionally may contain restrictions on resource type, resource labels, and metric labels. This field may not exceed 2048 Unicode characters in length.
Duration string: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
EvaluationMissingData string: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
Filter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
ForecastOptions Pulumi.GoogleNative.Monitoring.V3.Inputs.ForecastOptionsResponse: When this field is present, the MetricThreshold condition forecasts whether the time series is predicted to violate the threshold within the forecast_horizon. When this field is not set, the MetricThreshold tests the current value of the timeseries against the threshold.
ThresholdValue double: A value against which to compare the time series.
Trigger Pulumi.GoogleNative.Monitoring.V3.Inputs.TriggerResponse: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

Aggregations []AggregationResponse: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
Comparison string: The comparison to apply between the time series (indicated by filter and aggregation) and the threshold (indicated by threshold_value). The comparison is applied on each time series, with the time series on the left-hand side and the threshold on the right-hand side.Only COMPARISON_LT and COMPARISON_GT are supported currently.
DenominatorAggregations []AggregationResponse: Specifies the alignment of data points in individual time series selected by denominatorFilter as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources).When computing ratios, the aggregations and denominator_aggregations fields must use the same alignment period and produce time series that have the same periodicity and labels.
DenominatorFilter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies a time series that should be used as the denominator of a ratio that will be compared with the threshold. If a denominator_filter is specified, the time series specified by the filter field will be used as the numerator.The filter must specify the metric type and optionally may contain restrictions on resource type, resource labels, and metric labels. This field may not exceed 2048 Unicode characters in length.
Duration string: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
EvaluationMissingData string: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
Filter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
ForecastOptions ForecastOptionsResponse: When this field is present, the MetricThreshold condition forecasts whether the time series is predicted to violate the threshold within the forecast_horizon. When this field is not set, the MetricThreshold tests the current value of the timeseries against the threshold.
ThresholdValue float64: A value against which to compare the time series.
Trigger TriggerResponse: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

aggregations List<AggregationResponse>: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
comparison String: The comparison to apply between the time series (indicated by filter and aggregation) and the threshold (indicated by threshold_value). The comparison is applied on each time series, with the time series on the left-hand side and the threshold on the right-hand side.Only COMPARISON_LT and COMPARISON_GT are supported currently.
denominatorAggregations List<AggregationResponse>: Specifies the alignment of data points in individual time series selected by denominatorFilter as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources).When computing ratios, the aggregations and denominator_aggregations fields must use the same alignment period and produce time series that have the same periodicity and labels.
denominatorFilter String: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies a time series that should be used as the denominator of a ratio that will be compared with the threshold. If a denominator_filter is specified, the time series specified by the filter field will be used as the numerator.The filter must specify the metric type and optionally may contain restrictions on resource type, resource labels, and metric labels. This field may not exceed 2048 Unicode characters in length.
duration String: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
evaluationMissingData String: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
filter String: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
forecastOptions ForecastOptionsResponse: When this field is present, the MetricThreshold condition forecasts whether the time series is predicted to violate the threshold within the forecast_horizon. When this field is not set, the MetricThreshold tests the current value of the timeseries against the threshold.
thresholdValue Double: A value against which to compare the time series.
trigger TriggerResponse: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

aggregations AggregationResponse[]: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
comparison string: The comparison to apply between the time series (indicated by filter and aggregation) and the threshold (indicated by threshold_value). The comparison is applied on each time series, with the time series on the left-hand side and the threshold on the right-hand side.Only COMPARISON_LT and COMPARISON_GT are supported currently.
denominatorAggregations AggregationResponse[]: Specifies the alignment of data points in individual time series selected by denominatorFilter as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources).When computing ratios, the aggregations and denominator_aggregations fields must use the same alignment period and produce time series that have the same periodicity and labels.
denominatorFilter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies a time series that should be used as the denominator of a ratio that will be compared with the threshold. If a denominator_filter is specified, the time series specified by the filter field will be used as the numerator.The filter must specify the metric type and optionally may contain restrictions on resource type, resource labels, and metric labels. This field may not exceed 2048 Unicode characters in length.
duration string: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
evaluationMissingData string: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
filter string: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
forecastOptions ForecastOptionsResponse: When this field is present, the MetricThreshold condition forecasts whether the time series is predicted to violate the threshold within the forecast_horizon. When this field is not set, the MetricThreshold tests the current value of the timeseries against the threshold.
thresholdValue number: A value against which to compare the time series.
trigger TriggerResponse: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

aggregations Sequence[AggregationResponse]: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
comparison str: The comparison to apply between the time series (indicated by filter and aggregation) and the threshold (indicated by threshold_value). The comparison is applied on each time series, with the time series on the left-hand side and the threshold on the right-hand side.Only COMPARISON_LT and COMPARISON_GT are supported currently.
denominator_aggregations Sequence[AggregationResponse]: Specifies the alignment of data points in individual time series selected by denominatorFilter as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources).When computing ratios, the aggregations and denominator_aggregations fields must use the same alignment period and produce time series that have the same periodicity and labels.
denominator_filter str: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies a time series that should be used as the denominator of a ratio that will be compared with the threshold. If a denominator_filter is specified, the time series specified by the filter field will be used as the numerator.The filter must specify the metric type and optionally may contain restrictions on resource type, resource labels, and metric labels. This field may not exceed 2048 Unicode characters in length.
duration str: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
evaluation_missing_data str: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
filter str: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
forecast_options ForecastOptionsResponse: When this field is present, the MetricThreshold condition forecasts whether the time series is predicted to violate the threshold within the forecast_horizon. When this field is not set, the MetricThreshold tests the current value of the timeseries against the threshold.
threshold_value float: A value against which to compare the time series.
trigger TriggerResponse: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

aggregations List<Property Map>: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
comparison String: The comparison to apply between the time series (indicated by filter and aggregation) and the threshold (indicated by threshold_value). The comparison is applied on each time series, with the time series on the left-hand side and the threshold on the right-hand side.Only COMPARISON_LT and COMPARISON_GT are supported currently.
denominatorAggregations List<Property Map>: Specifies the alignment of data points in individual time series selected by denominatorFilter as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources).When computing ratios, the aggregations and denominator_aggregations fields must use the same alignment period and produce time series that have the same periodicity and labels.
denominatorFilter String: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies a time series that should be used as the denominator of a ratio that will be compared with the threshold. If a denominator_filter is specified, the time series specified by the filter field will be used as the numerator.The filter must specify the metric type and optionally may contain restrictions on resource type, resource labels, and metric labels. This field may not exceed 2048 Unicode characters in length.
duration String: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
evaluationMissingData String: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
filter String: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length.
forecastOptions Property Map: When this field is present, the MetricThreshold condition forecasts whether the time series is predicted to violate the threshold within the forecast_horizon. When this field is not set, the MetricThreshold tests the current value of the timeseries against the threshold.
thresholdValue Number: A value against which to compare the time series.
trigger Property Map: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

MonitoringQueryLanguageCondition, MonitoringQueryLanguageConditionArgs

Duration string: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
EvaluationMissingData Pulumi.GoogleNative.Monitoring.V3.MonitoringQueryLanguageConditionEvaluationMissingData: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
Query string: Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
Trigger Pulumi.GoogleNative.Monitoring.V3.Inputs.Trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

Duration string: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
EvaluationMissingData MonitoringQueryLanguageConditionEvaluationMissingData: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
Query string: Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
Trigger Trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

duration String: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
evaluationMissingData MonitoringQueryLanguageConditionEvaluationMissingData: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
query String: Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
trigger Trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

duration string: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
evaluationMissingData MonitoringQueryLanguageConditionEvaluationMissingData: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
query string: Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
trigger Trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

duration str: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
evaluation_missing_data MonitoringQueryLanguageConditionEvaluationMissingData: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
query str: Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
trigger Trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

duration String: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
evaluationMissingData "EVALUATION_MISSING_DATA_UNSPECIFIED" | "EVALUATION_MISSING_DATA_INACTIVE" | "EVALUATION_MISSING_DATA_ACTIVE" | "EVALUATION_MISSING_DATA_NO_OP": A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
query String: Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
trigger Property Map: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

MonitoringQueryLanguageConditionEvaluationMissingData, MonitoringQueryLanguageConditionEvaluationMissingDataArgs

EvaluationMissingDataUnspecified: EVALUATION_MISSING_DATA_UNSPECIFIEDAn unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.
EvaluationMissingDataInactive: EVALUATION_MISSING_DATA_INACTIVEIf there is no data to evaluate the condition, then evaluate the condition as false.
EvaluationMissingDataActive: EVALUATION_MISSING_DATA_ACTIVEIf there is no data to evaluate the condition, then evaluate the condition as true.
EvaluationMissingDataNoOp: EVALUATION_MISSING_DATA_NO_OPDo not evaluate the condition to any value if there is no data.

MonitoringQueryLanguageConditionEvaluationMissingDataEvaluationMissingDataUnspecified: EVALUATION_MISSING_DATA_UNSPECIFIEDAn unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.
MonitoringQueryLanguageConditionEvaluationMissingDataEvaluationMissingDataInactive: EVALUATION_MISSING_DATA_INACTIVEIf there is no data to evaluate the condition, then evaluate the condition as false.
MonitoringQueryLanguageConditionEvaluationMissingDataEvaluationMissingDataActive: EVALUATION_MISSING_DATA_ACTIVEIf there is no data to evaluate the condition, then evaluate the condition as true.
MonitoringQueryLanguageConditionEvaluationMissingDataEvaluationMissingDataNoOp: EVALUATION_MISSING_DATA_NO_OPDo not evaluate the condition to any value if there is no data.

EvaluationMissingDataUnspecified: EVALUATION_MISSING_DATA_UNSPECIFIEDAn unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.
EvaluationMissingDataInactive: EVALUATION_MISSING_DATA_INACTIVEIf there is no data to evaluate the condition, then evaluate the condition as false.
EvaluationMissingDataActive: EVALUATION_MISSING_DATA_ACTIVEIf there is no data to evaluate the condition, then evaluate the condition as true.
EvaluationMissingDataNoOp: EVALUATION_MISSING_DATA_NO_OPDo not evaluate the condition to any value if there is no data.

EvaluationMissingDataUnspecified: EVALUATION_MISSING_DATA_UNSPECIFIEDAn unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.
EvaluationMissingDataInactive: EVALUATION_MISSING_DATA_INACTIVEIf there is no data to evaluate the condition, then evaluate the condition as false.
EvaluationMissingDataActive: EVALUATION_MISSING_DATA_ACTIVEIf there is no data to evaluate the condition, then evaluate the condition as true.
EvaluationMissingDataNoOp: EVALUATION_MISSING_DATA_NO_OPDo not evaluate the condition to any value if there is no data.

EVALUATION_MISSING_DATA_UNSPECIFIED: EVALUATION_MISSING_DATA_UNSPECIFIEDAn unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.
EVALUATION_MISSING_DATA_INACTIVE: EVALUATION_MISSING_DATA_INACTIVEIf there is no data to evaluate the condition, then evaluate the condition as false.
EVALUATION_MISSING_DATA_ACTIVE: EVALUATION_MISSING_DATA_ACTIVEIf there is no data to evaluate the condition, then evaluate the condition as true.
EVALUATION_MISSING_DATA_NO_OP: EVALUATION_MISSING_DATA_NO_OPDo not evaluate the condition to any value if there is no data.

"EVALUATION_MISSING_DATA_UNSPECIFIED": EVALUATION_MISSING_DATA_UNSPECIFIEDAn unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.
"EVALUATION_MISSING_DATA_INACTIVE": EVALUATION_MISSING_DATA_INACTIVEIf there is no data to evaluate the condition, then evaluate the condition as false.
"EVALUATION_MISSING_DATA_ACTIVE": EVALUATION_MISSING_DATA_ACTIVEIf there is no data to evaluate the condition, then evaluate the condition as true.
"EVALUATION_MISSING_DATA_NO_OP": EVALUATION_MISSING_DATA_NO_OPDo not evaluate the condition to any value if there is no data.

MonitoringQueryLanguageConditionResponse, MonitoringQueryLanguageConditionResponseArgs

Duration string: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
EvaluationMissingData string: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
Query string: Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
Trigger Pulumi.GoogleNative.Monitoring.V3.Inputs.TriggerResponse: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

Duration string: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
EvaluationMissingData string: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
Query string: Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
Trigger TriggerResponse: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

duration String: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
evaluationMissingData String: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
query String: Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
trigger TriggerResponse: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

duration string: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
evaluationMissingData string: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
query string: Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
trigger TriggerResponse: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

duration str: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
evaluation_missing_data str: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
query str: Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
trigger TriggerResponse: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

duration String: The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.
evaluationMissingData String: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
query String: Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
trigger Property Map: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified.

MutationRecord, MutationRecordArgs

MutateTime string: When the change occurred.
MutatedBy string: The email address of the user making the change.

MutateTime string: When the change occurred.
MutatedBy string: The email address of the user making the change.

mutateTime String: When the change occurred.
mutatedBy String: The email address of the user making the change.

mutateTime string: When the change occurred.
mutatedBy string: The email address of the user making the change.

mutate_time str: When the change occurred.
mutated_by str: The email address of the user making the change.

mutateTime String: When the change occurred.
mutatedBy String: The email address of the user making the change.

MutationRecordResponse, MutationRecordResponseArgs

MutateTime string: When the change occurred.
MutatedBy string: The email address of the user making the change.

MutateTime string: When the change occurred.
MutatedBy string: The email address of the user making the change.

mutateTime String: When the change occurred.
mutatedBy String: The email address of the user making the change.

mutateTime string: When the change occurred.
mutatedBy string: The email address of the user making the change.

mutate_time str: When the change occurred.
mutated_by str: The email address of the user making the change.

mutateTime String: When the change occurred.
mutatedBy String: The email address of the user making the change.

NotificationChannelStrategy, NotificationChannelStrategyArgs

NotificationChannelNames List<string>: The full REST resource name for the notification channels that these settings apply to. Each of these correspond to the name field in one of the NotificationChannel objects referenced in the notification_channels field of this AlertPolicy. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
RenotifyInterval string: The frequency at which to send reminder notifications for open incidents.

NotificationChannelNames []string: The full REST resource name for the notification channels that these settings apply to. Each of these correspond to the name field in one of the NotificationChannel objects referenced in the notification_channels field of this AlertPolicy. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
RenotifyInterval string: The frequency at which to send reminder notifications for open incidents.

notificationChannelNames List<String>: The full REST resource name for the notification channels that these settings apply to. Each of these correspond to the name field in one of the NotificationChannel objects referenced in the notification_channels field of this AlertPolicy. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
renotifyInterval String: The frequency at which to send reminder notifications for open incidents.

notificationChannelNames string[]: The full REST resource name for the notification channels that these settings apply to. Each of these correspond to the name field in one of the NotificationChannel objects referenced in the notification_channels field of this AlertPolicy. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
renotifyInterval string: The frequency at which to send reminder notifications for open incidents.

notification_channel_names Sequence[str]: The full REST resource name for the notification channels that these settings apply to. Each of these correspond to the name field in one of the NotificationChannel objects referenced in the notification_channels field of this AlertPolicy. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
renotify_interval str: The frequency at which to send reminder notifications for open incidents.

notificationChannelNames List<String>: The full REST resource name for the notification channels that these settings apply to. Each of these correspond to the name field in one of the NotificationChannel objects referenced in the notification_channels field of this AlertPolicy. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
renotifyInterval String: The frequency at which to send reminder notifications for open incidents.

NotificationChannelStrategyResponse, NotificationChannelStrategyResponseArgs

NotificationChannelNames List<string>: The full REST resource name for the notification channels that these settings apply to. Each of these correspond to the name field in one of the NotificationChannel objects referenced in the notification_channels field of this AlertPolicy. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
RenotifyInterval string: The frequency at which to send reminder notifications for open incidents.

NotificationChannelNames []string: The full REST resource name for the notification channels that these settings apply to. Each of these correspond to the name field in one of the NotificationChannel objects referenced in the notification_channels field of this AlertPolicy. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
RenotifyInterval string: The frequency at which to send reminder notifications for open incidents.

notificationChannelNames List<String>: The full REST resource name for the notification channels that these settings apply to. Each of these correspond to the name field in one of the NotificationChannel objects referenced in the notification_channels field of this AlertPolicy. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
renotifyInterval String: The frequency at which to send reminder notifications for open incidents.

notificationChannelNames string[]: The full REST resource name for the notification channels that these settings apply to. Each of these correspond to the name field in one of the NotificationChannel objects referenced in the notification_channels field of this AlertPolicy. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
renotifyInterval string: The frequency at which to send reminder notifications for open incidents.

notification_channel_names Sequence[str]: The full REST resource name for the notification channels that these settings apply to. Each of these correspond to the name field in one of the NotificationChannel objects referenced in the notification_channels field of this AlertPolicy. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
renotify_interval str: The frequency at which to send reminder notifications for open incidents.

notificationChannelNames List<String>: The full REST resource name for the notification channels that these settings apply to. Each of these correspond to the name field in one of the NotificationChannel objects referenced in the notification_channels field of this AlertPolicy. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
renotifyInterval String: The frequency at which to send reminder notifications for open incidents.

NotificationRateLimit, NotificationRateLimitArgs

Period string: Not more than one notification per period.

Period string: Not more than one notification per period.

period String: Not more than one notification per period.

period string: Not more than one notification per period.

period str: Not more than one notification per period.

period String: Not more than one notification per period.

NotificationRateLimitResponse, NotificationRateLimitResponseArgs

Period string: Not more than one notification per period.

Period string: Not more than one notification per period.

period String: Not more than one notification per period.

period string: Not more than one notification per period.

period str: Not more than one notification per period.

period String: Not more than one notification per period.

PrometheusQueryLanguageCondition, PrometheusQueryLanguageConditionArgs

Query string: The PromQL expression to evaluate. Every evaluation cycle this expression is evaluated at the current time, and all resultant time series become pending/firing alerts. This field must not be empty.
AlertRule string: Optional. The alerting rule name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must be a valid Prometheus label name (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). This field may not exceed 2048 Unicode characters in length.
Duration string: Optional. Alerts are considered firing once their PromQL expression was evaluated to be "true" for this long. Alerts whose PromQL expression was not evaluated to be "true" for long enough are considered pending. Must be a non-negative duration or missing. This field is optional. Its default value is zero.
EvaluationInterval string: Optional. How often this rule should be evaluated. Must be a positive multiple of 30 seconds or missing. This field is optional. Its default value is 30 seconds. If this PrometheusQueryLanguageCondition was generated from a Prometheus alerting rule, then this value should be taken from the enclosing rule group.
Labels Dictionary<string, string>: Optional. Labels to add to or overwrite in the PromQL query result. Label names must be valid (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). Label values can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). The only available variable names are the names of the labels in the PromQL result, including "name" and "value". "labels" may be empty.
RuleGroup string: Optional. The rule group name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must contain a valid UTF-8 string. This field may not exceed 2048 Unicode characters in length.

Query string: The PromQL expression to evaluate. Every evaluation cycle this expression is evaluated at the current time, and all resultant time series become pending/firing alerts. This field must not be empty.
AlertRule string: Optional. The alerting rule name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must be a valid Prometheus label name (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). This field may not exceed 2048 Unicode characters in length.
Duration string: Optional. Alerts are considered firing once their PromQL expression was evaluated to be "true" for this long. Alerts whose PromQL expression was not evaluated to be "true" for long enough are considered pending. Must be a non-negative duration or missing. This field is optional. Its default value is zero.
EvaluationInterval string: Optional. How often this rule should be evaluated. Must be a positive multiple of 30 seconds or missing. This field is optional. Its default value is 30 seconds. If this PrometheusQueryLanguageCondition was generated from a Prometheus alerting rule, then this value should be taken from the enclosing rule group.
Labels map[string]string: Optional. Labels to add to or overwrite in the PromQL query result. Label names must be valid (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). Label values can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). The only available variable names are the names of the labels in the PromQL result, including "name" and "value". "labels" may be empty.
RuleGroup string: Optional. The rule group name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must contain a valid UTF-8 string. This field may not exceed 2048 Unicode characters in length.

query String: The PromQL expression to evaluate. Every evaluation cycle this expression is evaluated at the current time, and all resultant time series become pending/firing alerts. This field must not be empty.
alertRule String: Optional. The alerting rule name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must be a valid Prometheus label name (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). This field may not exceed 2048 Unicode characters in length.
duration String: Optional. Alerts are considered firing once their PromQL expression was evaluated to be "true" for this long. Alerts whose PromQL expression was not evaluated to be "true" for long enough are considered pending. Must be a non-negative duration or missing. This field is optional. Its default value is zero.
evaluationInterval String: Optional. How often this rule should be evaluated. Must be a positive multiple of 30 seconds or missing. This field is optional. Its default value is 30 seconds. If this PrometheusQueryLanguageCondition was generated from a Prometheus alerting rule, then this value should be taken from the enclosing rule group.
labels Map<String,String>: Optional. Labels to add to or overwrite in the PromQL query result. Label names must be valid (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). Label values can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). The only available variable names are the names of the labels in the PromQL result, including "name" and "value". "labels" may be empty.
ruleGroup String: Optional. The rule group name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must contain a valid UTF-8 string. This field may not exceed 2048 Unicode characters in length.

query string: The PromQL expression to evaluate. Every evaluation cycle this expression is evaluated at the current time, and all resultant time series become pending/firing alerts. This field must not be empty.
alertRule string: Optional. The alerting rule name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must be a valid Prometheus label name (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). This field may not exceed 2048 Unicode characters in length.
duration string: Optional. Alerts are considered firing once their PromQL expression was evaluated to be "true" for this long. Alerts whose PromQL expression was not evaluated to be "true" for long enough are considered pending. Must be a non-negative duration or missing. This field is optional. Its default value is zero.
evaluationInterval string: Optional. How often this rule should be evaluated. Must be a positive multiple of 30 seconds or missing. This field is optional. Its default value is 30 seconds. If this PrometheusQueryLanguageCondition was generated from a Prometheus alerting rule, then this value should be taken from the enclosing rule group.
labels {[key: string]: string}: Optional. Labels to add to or overwrite in the PromQL query result. Label names must be valid (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). Label values can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). The only available variable names are the names of the labels in the PromQL result, including "name" and "value". "labels" may be empty.
ruleGroup string: Optional. The rule group name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must contain a valid UTF-8 string. This field may not exceed 2048 Unicode characters in length.

query str: The PromQL expression to evaluate. Every evaluation cycle this expression is evaluated at the current time, and all resultant time series become pending/firing alerts. This field must not be empty.
alert_rule str: Optional. The alerting rule name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must be a valid Prometheus label name (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). This field may not exceed 2048 Unicode characters in length.
duration str: Optional. Alerts are considered firing once their PromQL expression was evaluated to be "true" for this long. Alerts whose PromQL expression was not evaluated to be "true" for long enough are considered pending. Must be a non-negative duration or missing. This field is optional. Its default value is zero.
evaluation_interval str: Optional. How often this rule should be evaluated. Must be a positive multiple of 30 seconds or missing. This field is optional. Its default value is 30 seconds. If this PrometheusQueryLanguageCondition was generated from a Prometheus alerting rule, then this value should be taken from the enclosing rule group.
labels Mapping[str, str]: Optional. Labels to add to or overwrite in the PromQL query result. Label names must be valid (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). Label values can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). The only available variable names are the names of the labels in the PromQL result, including "name" and "value". "labels" may be empty.
rule_group str: Optional. The rule group name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must contain a valid UTF-8 string. This field may not exceed 2048 Unicode characters in length.

query String: The PromQL expression to evaluate. Every evaluation cycle this expression is evaluated at the current time, and all resultant time series become pending/firing alerts. This field must not be empty.
alertRule String: Optional. The alerting rule name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must be a valid Prometheus label name (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). This field may not exceed 2048 Unicode characters in length.
duration String: Optional. Alerts are considered firing once their PromQL expression was evaluated to be "true" for this long. Alerts whose PromQL expression was not evaluated to be "true" for long enough are considered pending. Must be a non-negative duration or missing. This field is optional. Its default value is zero.
evaluationInterval String: Optional. How often this rule should be evaluated. Must be a positive multiple of 30 seconds or missing. This field is optional. Its default value is 30 seconds. If this PrometheusQueryLanguageCondition was generated from a Prometheus alerting rule, then this value should be taken from the enclosing rule group.
labels Map<String>: Optional. Labels to add to or overwrite in the PromQL query result. Label names must be valid (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). Label values can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). The only available variable names are the names of the labels in the PromQL result, including "name" and "value". "labels" may be empty.
ruleGroup String: Optional. The rule group name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must contain a valid UTF-8 string. This field may not exceed 2048 Unicode characters in length.

PrometheusQueryLanguageConditionResponse, PrometheusQueryLanguageConditionResponseArgs

AlertRule string: Optional. The alerting rule name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must be a valid Prometheus label name (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). This field may not exceed 2048 Unicode characters in length.
Duration string: Optional. Alerts are considered firing once their PromQL expression was evaluated to be "true" for this long. Alerts whose PromQL expression was not evaluated to be "true" for long enough are considered pending. Must be a non-negative duration or missing. This field is optional. Its default value is zero.
EvaluationInterval string: Optional. How often this rule should be evaluated. Must be a positive multiple of 30 seconds or missing. This field is optional. Its default value is 30 seconds. If this PrometheusQueryLanguageCondition was generated from a Prometheus alerting rule, then this value should be taken from the enclosing rule group.
Labels Dictionary<string, string>: Optional. Labels to add to or overwrite in the PromQL query result. Label names must be valid (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). Label values can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). The only available variable names are the names of the labels in the PromQL result, including "name" and "value". "labels" may be empty.
Query string: The PromQL expression to evaluate. Every evaluation cycle this expression is evaluated at the current time, and all resultant time series become pending/firing alerts. This field must not be empty.
RuleGroup string: Optional. The rule group name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must contain a valid UTF-8 string. This field may not exceed 2048 Unicode characters in length.

AlertRule string: Optional. The alerting rule name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must be a valid Prometheus label name (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). This field may not exceed 2048 Unicode characters in length.
Duration string: Optional. Alerts are considered firing once their PromQL expression was evaluated to be "true" for this long. Alerts whose PromQL expression was not evaluated to be "true" for long enough are considered pending. Must be a non-negative duration or missing. This field is optional. Its default value is zero.
EvaluationInterval string: Optional. How often this rule should be evaluated. Must be a positive multiple of 30 seconds or missing. This field is optional. Its default value is 30 seconds. If this PrometheusQueryLanguageCondition was generated from a Prometheus alerting rule, then this value should be taken from the enclosing rule group.
Labels map[string]string: Optional. Labels to add to or overwrite in the PromQL query result. Label names must be valid (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). Label values can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). The only available variable names are the names of the labels in the PromQL result, including "name" and "value". "labels" may be empty.
Query string: The PromQL expression to evaluate. Every evaluation cycle this expression is evaluated at the current time, and all resultant time series become pending/firing alerts. This field must not be empty.
RuleGroup string: Optional. The rule group name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must contain a valid UTF-8 string. This field may not exceed 2048 Unicode characters in length.

alertRule String: Optional. The alerting rule name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must be a valid Prometheus label name (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). This field may not exceed 2048 Unicode characters in length.
duration String: Optional. Alerts are considered firing once their PromQL expression was evaluated to be "true" for this long. Alerts whose PromQL expression was not evaluated to be "true" for long enough are considered pending. Must be a non-negative duration or missing. This field is optional. Its default value is zero.
evaluationInterval String: Optional. How often this rule should be evaluated. Must be a positive multiple of 30 seconds or missing. This field is optional. Its default value is 30 seconds. If this PrometheusQueryLanguageCondition was generated from a Prometheus alerting rule, then this value should be taken from the enclosing rule group.
labels Map<String,String>: Optional. Labels to add to or overwrite in the PromQL query result. Label names must be valid (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). Label values can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). The only available variable names are the names of the labels in the PromQL result, including "name" and "value". "labels" may be empty.
query String: The PromQL expression to evaluate. Every evaluation cycle this expression is evaluated at the current time, and all resultant time series become pending/firing alerts. This field must not be empty.
ruleGroup String: Optional. The rule group name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must contain a valid UTF-8 string. This field may not exceed 2048 Unicode characters in length.

alertRule string: Optional. The alerting rule name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must be a valid Prometheus label name (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). This field may not exceed 2048 Unicode characters in length.
duration string: Optional. Alerts are considered firing once their PromQL expression was evaluated to be "true" for this long. Alerts whose PromQL expression was not evaluated to be "true" for long enough are considered pending. Must be a non-negative duration or missing. This field is optional. Its default value is zero.
evaluationInterval string: Optional. How often this rule should be evaluated. Must be a positive multiple of 30 seconds or missing. This field is optional. Its default value is 30 seconds. If this PrometheusQueryLanguageCondition was generated from a Prometheus alerting rule, then this value should be taken from the enclosing rule group.
labels {[key: string]: string}: Optional. Labels to add to or overwrite in the PromQL query result. Label names must be valid (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). Label values can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). The only available variable names are the names of the labels in the PromQL result, including "name" and "value". "labels" may be empty.
query string: The PromQL expression to evaluate. Every evaluation cycle this expression is evaluated at the current time, and all resultant time series become pending/firing alerts. This field must not be empty.
ruleGroup string: Optional. The rule group name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must contain a valid UTF-8 string. This field may not exceed 2048 Unicode characters in length.

alert_rule str: Optional. The alerting rule name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must be a valid Prometheus label name (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). This field may not exceed 2048 Unicode characters in length.
duration str: Optional. Alerts are considered firing once their PromQL expression was evaluated to be "true" for this long. Alerts whose PromQL expression was not evaluated to be "true" for long enough are considered pending. Must be a non-negative duration or missing. This field is optional. Its default value is zero.
evaluation_interval str: Optional. How often this rule should be evaluated. Must be a positive multiple of 30 seconds or missing. This field is optional. Its default value is 30 seconds. If this PrometheusQueryLanguageCondition was generated from a Prometheus alerting rule, then this value should be taken from the enclosing rule group.
labels Mapping[str, str]: Optional. Labels to add to or overwrite in the PromQL query result. Label names must be valid (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). Label values can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). The only available variable names are the names of the labels in the PromQL result, including "name" and "value". "labels" may be empty.
query str: The PromQL expression to evaluate. Every evaluation cycle this expression is evaluated at the current time, and all resultant time series become pending/firing alerts. This field must not be empty.
rule_group str: Optional. The rule group name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must contain a valid UTF-8 string. This field may not exceed 2048 Unicode characters in length.

alertRule String: Optional. The alerting rule name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must be a valid Prometheus label name (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). This field may not exceed 2048 Unicode characters in length.
duration String: Optional. Alerts are considered firing once their PromQL expression was evaluated to be "true" for this long. Alerts whose PromQL expression was not evaluated to be "true" for long enough are considered pending. Must be a non-negative duration or missing. This field is optional. Its default value is zero.
evaluationInterval String: Optional. How often this rule should be evaluated. Must be a positive multiple of 30 seconds or missing. This field is optional. Its default value is 30 seconds. If this PrometheusQueryLanguageCondition was generated from a Prometheus alerting rule, then this value should be taken from the enclosing rule group.
labels Map<String>: Optional. Labels to add to or overwrite in the PromQL query result. Label names must be valid (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). Label values can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables). The only available variable names are the names of the labels in the PromQL result, including "name" and "value". "labels" may be empty.
query String: The PromQL expression to evaluate. Every evaluation cycle this expression is evaluated at the current time, and all resultant time series become pending/firing alerts. This field must not be empty.
ruleGroup String: Optional. The rule group name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must contain a valid UTF-8 string. This field may not exceed 2048 Unicode characters in length.

Status, StatusArgs

Code int: The status code, which should be an enum value of google.rpc.Code.
Details List<ImmutableDictionary<string, string>>: A list of messages that carry the error details. There is a common set of message types for APIs to use.
Message string: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

Code int: The status code, which should be an enum value of google.rpc.Code.
Details []map[string]string: A list of messages that carry the error details. There is a common set of message types for APIs to use.
Message string: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code Integer: The status code, which should be an enum value of google.rpc.Code.
details List<Map<String,String>>: A list of messages that carry the error details. There is a common set of message types for APIs to use.
message String: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code number: The status code, which should be an enum value of google.rpc.Code.
details {[key: string]: string}[]: A list of messages that carry the error details. There is a common set of message types for APIs to use.
message string: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code int: The status code, which should be an enum value of google.rpc.Code.
details Sequence[Mapping[str, str]]: A list of messages that carry the error details. There is a common set of message types for APIs to use.
message str: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code Number: The status code, which should be an enum value of google.rpc.Code.
details List<Map<String>>: A list of messages that carry the error details. There is a common set of message types for APIs to use.
message String: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

StatusResponse, StatusResponseArgs

Code int: The status code, which should be an enum value of google.rpc.Code.
Details List<ImmutableDictionary<string, string>>: A list of messages that carry the error details. There is a common set of message types for APIs to use.
Message string: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

Code int: The status code, which should be an enum value of google.rpc.Code.
Details []map[string]string: A list of messages that carry the error details. There is a common set of message types for APIs to use.
Message string: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code Integer: The status code, which should be an enum value of google.rpc.Code.
details List<Map<String,String>>: A list of messages that carry the error details. There is a common set of message types for APIs to use.
message String: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code number: The status code, which should be an enum value of google.rpc.Code.
details {[key: string]: string}[]: A list of messages that carry the error details. There is a common set of message types for APIs to use.
message string: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code int: The status code, which should be an enum value of google.rpc.Code.
details Sequence[Mapping[str, str]]: A list of messages that carry the error details. There is a common set of message types for APIs to use.
message str: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code Number: The status code, which should be an enum value of google.rpc.Code.
details List<Map<String>>: A list of messages that carry the error details. There is a common set of message types for APIs to use.
message String: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

Trigger, TriggerArgs

Count int: The absolute number of time series that must fail the predicate for the condition to be triggered.
Percent double: The percentage of time series that must fail the predicate for the condition to be triggered.

Count int: The absolute number of time series that must fail the predicate for the condition to be triggered.
Percent float64: The percentage of time series that must fail the predicate for the condition to be triggered.

count Integer: The absolute number of time series that must fail the predicate for the condition to be triggered.
percent Double: The percentage of time series that must fail the predicate for the condition to be triggered.

count number: The absolute number of time series that must fail the predicate for the condition to be triggered.
percent number: The percentage of time series that must fail the predicate for the condition to be triggered.

count int: The absolute number of time series that must fail the predicate for the condition to be triggered.
percent float: The percentage of time series that must fail the predicate for the condition to be triggered.

count Number: The absolute number of time series that must fail the predicate for the condition to be triggered.
percent Number: The percentage of time series that must fail the predicate for the condition to be triggered.

TriggerResponse, TriggerResponseArgs

Count int: The absolute number of time series that must fail the predicate for the condition to be triggered.
Percent double: The percentage of time series that must fail the predicate for the condition to be triggered.

Count int: The absolute number of time series that must fail the predicate for the condition to be triggered.
Percent float64: The percentage of time series that must fail the predicate for the condition to be triggered.

count Integer: The absolute number of time series that must fail the predicate for the condition to be triggered.
percent Double: The percentage of time series that must fail the predicate for the condition to be triggered.

count number: The absolute number of time series that must fail the predicate for the condition to be triggered.
percent number: The percentage of time series that must fail the predicate for the condition to be triggered.

count int: The absolute number of time series that must fail the predicate for the condition to be triggered.
percent float: The percentage of time series that must fail the predicate for the condition to be triggered.

count Number: The absolute number of time series that must fail the predicate for the condition to be triggered.
percent Number: The percentage of time series that must fail the predicate for the condition to be triggered.

Package Details

Repository: Google Cloud Native pulumi/pulumi-google-native
License: Apache-2.0

Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi

pulumi/pulumi-google-native

google-native.monitoring/v3.AlertPolicy

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Create AlertPolicy Resource

Constructor syntax

Parameters

Example

AlertPolicy Resource Properties

Inputs

Outputs

Supporting Types

Aggregation, AggregationArgs

AggregationCrossSeriesReducer, AggregationCrossSeriesReducerArgs

AggregationPerSeriesAligner, AggregationPerSeriesAlignerArgs

AggregationResponse, AggregationResponseArgs

AlertPolicyCombiner, AlertPolicyCombinerArgs

AlertPolicySeverity, AlertPolicySeverityArgs

AlertStrategy, AlertStrategyArgs

AlertStrategyResponse, AlertStrategyResponseArgs

Condition, ConditionArgs

ConditionResponse, ConditionResponseArgs

Documentation, DocumentationArgs

DocumentationResponse, DocumentationResponseArgs

ForecastOptions, ForecastOptionsArgs

ForecastOptionsResponse, ForecastOptionsResponseArgs

LogMatch, LogMatchArgs

LogMatchResponse, LogMatchResponseArgs

MetricAbsence, MetricAbsenceArgs

MetricAbsenceResponse, MetricAbsenceResponseArgs

MetricThreshold, MetricThresholdArgs

MetricThresholdComparison, MetricThresholdComparisonArgs

MetricThresholdEvaluationMissingData, MetricThresholdEvaluationMissingDataArgs

MetricThresholdResponse, MetricThresholdResponseArgs

MonitoringQueryLanguageCondition, MonitoringQueryLanguageConditionArgs

MonitoringQueryLanguageConditionEvaluationMissingData, MonitoringQueryLanguageConditionEvaluationMissingDataArgs

MonitoringQueryLanguageConditionResponse, MonitoringQueryLanguageConditionResponseArgs

MutationRecord, MutationRecordArgs

MutationRecordResponse, MutationRecordResponseArgs

NotificationChannelStrategy, NotificationChannelStrategyArgs

NotificationChannelStrategyResponse, NotificationChannelStrategyResponseArgs

NotificationRateLimit, NotificationRateLimitArgs

NotificationRateLimitResponse, NotificationRateLimitResponseArgs

PrometheusQueryLanguageCondition, PrometheusQueryLanguageConditionArgs

PrometheusQueryLanguageConditionResponse, PrometheusQueryLanguageConditionResponseArgs

Status, StatusArgs

StatusResponse, StatusResponseArgs

Trigger, TriggerArgs

TriggerResponse, TriggerResponseArgs

Package Details

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