AlertPolicy

Creates a new alerting policy.Design your application to single-thread API calls that modify the state of alerting policies in a single project. This includes calls to CreateAlertPolicy, DeleteAlertPolicy and UpdateAlertPolicy.

Create AlertPolicy Resource

new AlertPolicy(name: string, args?: AlertPolicyArgs, opts?: CustomResourceOptions);
@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,
                user_labels: Optional[Mapping[str, str]] = None,
                validity: Optional[StatusArgs] = None)
@overload
def AlertPolicy(resource_name: str,
                args: Optional[AlertPolicyArgs] = None,
                opts: Optional[ResourceOptions] = 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.

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.

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.AlertStrategyArgs

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.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.

CreationRecord Pulumi.GoogleNative.Monitoring.V3.Inputs.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.

Documentation Pulumi.GoogleNative.Monitoring.V3.Inputs.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 Pulumi.GoogleNative.Monitoring.V3.Inputs.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 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
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.

Validity Pulumi.GoogleNative.Monitoring.V3.Inputs.StatusArgs

Read-only description of how the alert policy is invalid. OK if the alert policy is valid. If not OK, the 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.

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.

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
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.

Validity StatusArgs

Read-only description of how the alert policy is invalid. OK if the alert policy is valid. If not OK, the 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 List<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.

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.

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 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 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 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
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.

validity StatusArgs

Read-only description of how the alert policy is invalid. OK if the alert policy is valid. If not OK, the 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.

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.

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 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 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
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.

validity StatusArgs

Read-only description of how the alert policy is invalid. OK if the alert policy is valid. If not OK, the 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.

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.

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
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.

validity StatusArgs

Read-only description of how the alert policy is invalid. OK if the alert policy is valid. If not OK, the 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.

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.

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
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.

validity Property Map

Read-only description of how the alert policy is invalid. OK if the alert policy is valid. If not OK, the 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

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

ReduceNone
REDUCE_NONE

No cross-time series reduction. The output of the Aligner is returned.

ReduceMean
REDUCE_MEAN

Reduce 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_MIN

Reduce 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_MAX

Reduce 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_SUM

Reduce 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_STDDEV

Reduce 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_COUNT

Reduce 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_TRUE

Reduce 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_FALSE

Reduce 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_TRUE

Reduce 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_99

Reduce 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_95

Reduce 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_50

Reduce 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_05

Reduce 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_NONE

No cross-time series reduction. The output of the Aligner is returned.

AggregationCrossSeriesReducerReduceMean
REDUCE_MEAN

Reduce 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_MIN

Reduce 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_MAX

Reduce 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_SUM

Reduce 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_STDDEV

Reduce 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_COUNT

Reduce 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_TRUE

Reduce 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_FALSE

Reduce 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_TRUE

Reduce 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_99

Reduce 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_95

Reduce 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_50

Reduce 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_05

Reduce 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_NONE

No cross-time series reduction. The output of the Aligner is returned.

ReduceMean
REDUCE_MEAN

Reduce 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_MIN

Reduce 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_MAX

Reduce 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_SUM

Reduce 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_STDDEV

Reduce 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_COUNT

Reduce 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_TRUE

Reduce 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_FALSE

Reduce 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_TRUE

Reduce 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_99

Reduce 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_95

Reduce 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_50

Reduce 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_05

Reduce 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_NONE

No cross-time series reduction. The output of the Aligner is returned.

ReduceMean
REDUCE_MEAN

Reduce 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_MIN

Reduce 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_MAX

Reduce 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_SUM

Reduce 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_STDDEV

Reduce 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_COUNT

Reduce 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_TRUE

Reduce 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_FALSE

Reduce 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_TRUE

Reduce 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_99

Reduce 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_95

Reduce 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_50

Reduce 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_05

Reduce 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_NONE

No cross-time series reduction. The output of the Aligner is returned.

REDUCE_MEAN
REDUCE_MEAN

Reduce 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_MIN

Reduce 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_MAX

Reduce 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_SUM

Reduce 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_STDDEV

Reduce 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_COUNT

Reduce 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_TRUE

Reduce 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_FALSE

Reduce 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_TRUE

Reduce 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_99

Reduce 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_95

Reduce 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_50

Reduce 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_05

Reduce 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_NONE

No cross-time series reduction. The output of the Aligner is returned.

"REDUCE_MEAN"
REDUCE_MEAN

Reduce 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_MIN

Reduce 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_MAX

Reduce 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_SUM

Reduce 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_STDDEV

Reduce 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_COUNT

Reduce 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_TRUE

Reduce 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_FALSE

Reduce 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_TRUE

Reduce 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_99

Reduce 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_95

Reduce 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_50

Reduce 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_05

Reduce 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

AlignNone
ALIGN_NONE

No 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_DELTA

Align 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_RATE

Align 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_INTERPOLATE

Align 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_OLDER

Align 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_MIN

Align 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_MAX

Align 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_MEAN

Align 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_COUNT

Align 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_SUM

Align 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_STDDEV

Align 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_TRUE

Align 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_FALSE

Align 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_TRUE

Align 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_99

Align 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_95

Align 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_50

Align 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_05

Align 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_CHANGE

Align 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_NONE

No 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_DELTA

Align 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_RATE

Align 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_INTERPOLATE

Align 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_OLDER

Align 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_MIN

Align 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_MAX

Align 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_MEAN

Align 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_COUNT

Align 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_SUM

Align 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_STDDEV

Align 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_TRUE

Align 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_FALSE

Align 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_TRUE

Align 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_99

Align 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_95

Align 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_50

Align 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_05

Align 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_CHANGE

Align 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_NONE

No 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_DELTA

Align 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_RATE

Align 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_INTERPOLATE

Align 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_OLDER

Align 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_MIN

Align 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_MAX

Align 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_MEAN

Align 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_COUNT

Align 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_SUM

Align 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_STDDEV

Align 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_TRUE

Align 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_FALSE

Align 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_TRUE

Align 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_99

Align 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_95

Align 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_50

Align 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_05

Align 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_CHANGE

Align 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_NONE

No 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_DELTA

Align 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_RATE

Align 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_INTERPOLATE

Align 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_OLDER

Align 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_MIN

Align 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_MAX

Align 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_MEAN

Align 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_COUNT

Align 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_SUM

Align 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_STDDEV

Align 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_TRUE

Align 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_FALSE

Align 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_TRUE

Align 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_99

Align 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_95

Align 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_50

Align 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_05

Align 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_CHANGE

Align 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_NONE

No 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_DELTA

Align 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_RATE

Align 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_INTERPOLATE

Align 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_OLDER

Align 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_MIN

Align 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_MAX

Align 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_MEAN

Align 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_COUNT

Align 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_SUM

Align 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_STDDEV

Align 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_TRUE

Align 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_FALSE

Align 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_TRUE

Align 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_99

Align 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_95

Align 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_50

Align 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_05

Align 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_CHANGE

Align 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_NONE

No 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_DELTA

Align 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_RATE

Align 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_INTERPOLATE

Align 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_OLDER

Align 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_MIN

Align 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_MAX

Align 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_MEAN

Align 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_COUNT

Align 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_SUM

Align 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_STDDEV

Align 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_TRUE

Align 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_FALSE

Align 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_TRUE

Align 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_99

Align 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_95

Align 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_50

Align 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_05

Align 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_CHANGE

Align 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

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

CombineUnspecified
COMBINE_UNSPECIFIED

An unspecified combiner.

And
AND

Combine 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
OR

Combine conditions using the logical OR operator. An incident is created if any of the listed conditions is met.

AndWithMatchingResource
AND_WITH_MATCHING_RESOURCE

Combine 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_UNSPECIFIED

An unspecified combiner.

AlertPolicyCombinerAnd
AND

Combine 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
OR

Combine conditions using the logical OR operator. An incident is created if any of the listed conditions is met.

AlertPolicyCombinerAndWithMatchingResource
AND_WITH_MATCHING_RESOURCE

Combine 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_UNSPECIFIED

An unspecified combiner.

And
AND

Combine 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
OR

Combine conditions using the logical OR operator. An incident is created if any of the listed conditions is met.

AndWithMatchingResource
AND_WITH_MATCHING_RESOURCE

Combine 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_UNSPECIFIED

An unspecified combiner.

And
AND

Combine 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
OR

Combine conditions using the logical OR operator. An incident is created if any of the listed conditions is met.

AndWithMatchingResource
AND_WITH_MATCHING_RESOURCE

Combine 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_UNSPECIFIED

An unspecified combiner.

AND_
AND

Combine 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_
OR

Combine 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_RESOURCE

Combine 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_UNSPECIFIED

An unspecified combiner.

"AND"
AND

Combine 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"
OR

Combine 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_RESOURCE

Combine 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.

AlertStrategy

AutoClose string

If an alert policy that was active has no data for this long, any open incidents will close

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

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

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

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_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

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

AutoClose string

If an alert policy that was active has no data for this long, any open incidents will close

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

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

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

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_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

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

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.

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.

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.

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.

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_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.

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

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.

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.

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.

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.

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_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.

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

Content string

The text 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.

Content string

The text 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.

content String

The text 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.

content string

The text 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.

content str

The text 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.

content String

The text 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.

DocumentationResponse

Content string

The text 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.

Content string

The text 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.

content String

The text 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.

content string

The text 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.

content str

The text 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.

content String

The text 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.

LogMatch

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

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

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

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

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.

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.

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.

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.

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.

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.

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

ComparisonUnspecified
COMPARISON_UNSPECIFIED

No ordering relationship is specified.

ComparisonGt
COMPARISON_GT

True if the left argument is greater than the right argument.

ComparisonGe
COMPARISON_GE

True if the left argument is greater than or equal to the right argument.

ComparisonLt
COMPARISON_LT

True if the left argument is less than the right argument.

ComparisonLe
COMPARISON_LE

True if the left argument is less than or equal to the right argument.

ComparisonEq
COMPARISON_EQ

True if the left argument is equal to the right argument.

ComparisonNe
COMPARISON_NE

True if the left argument is not equal to the right argument.

MetricThresholdComparisonComparisonUnspecified
COMPARISON_UNSPECIFIED

No ordering relationship is specified.

MetricThresholdComparisonComparisonGt
COMPARISON_GT

True if the left argument is greater than the right argument.

MetricThresholdComparisonComparisonGe
COMPARISON_GE

True if the left argument is greater than or equal to the right argument.

MetricThresholdComparisonComparisonLt
COMPARISON_LT

True if the left argument is less than the right argument.

MetricThresholdComparisonComparisonLe
COMPARISON_LE

True if the left argument is less than or equal to the right argument.

MetricThresholdComparisonComparisonEq
COMPARISON_EQ

True if the left argument is equal to the right argument.

MetricThresholdComparisonComparisonNe
COMPARISON_NE

True if the left argument is not equal to the right argument.

ComparisonUnspecified
COMPARISON_UNSPECIFIED

No ordering relationship is specified.

ComparisonGt
COMPARISON_GT

True if the left argument is greater than the right argument.

ComparisonGe
COMPARISON_GE

True if the left argument is greater than or equal to the right argument.

ComparisonLt
COMPARISON_LT

True if the left argument is less than the right argument.

ComparisonLe
COMPARISON_LE

True if the left argument is less than or equal to the right argument.

ComparisonEq
COMPARISON_EQ

True if the left argument is equal to the right argument.

ComparisonNe
COMPARISON_NE

True if the left argument is not equal to the right argument.

ComparisonUnspecified
COMPARISON_UNSPECIFIED

No ordering relationship is specified.

ComparisonGt
COMPARISON_GT

True if the left argument is greater than the right argument.

ComparisonGe
COMPARISON_GE

True if the left argument is greater than or equal to the right argument.

ComparisonLt
COMPARISON_LT

True if the left argument is less than the right argument.

ComparisonLe
COMPARISON_LE

True if the left argument is less than or equal to the right argument.

ComparisonEq
COMPARISON_EQ

True if the left argument is equal to the right argument.

ComparisonNe
COMPARISON_NE

True if the left argument is not equal to the right argument.

COMPARISON_UNSPECIFIED
COMPARISON_UNSPECIFIED

No ordering relationship is specified.

COMPARISON_GT
COMPARISON_GT

True if the left argument is greater than the right argument.

COMPARISON_GE
COMPARISON_GE

True if the left argument is greater than or equal to the right argument.

COMPARISON_LT
COMPARISON_LT

True if the left argument is less than the right argument.

COMPARISON_LE
COMPARISON_LE

True if the left argument is less than or equal to the right argument.

COMPARISON_EQ
COMPARISON_EQ

True if the left argument is equal to the right argument.

COMPARISON_NE
COMPARISON_NE

True if the left argument is not equal to the right argument.

"COMPARISON_UNSPECIFIED"
COMPARISON_UNSPECIFIED

No ordering relationship is specified.

"COMPARISON_GT"
COMPARISON_GT

True if the left argument is greater than the right argument.

"COMPARISON_GE"
COMPARISON_GE

True if the left argument is greater than or equal to the right argument.

"COMPARISON_LT"
COMPARISON_LT

True if the left argument is less than the right argument.

"COMPARISON_LE"
COMPARISON_LE

True if the left argument is less than or equal to the right argument.

"COMPARISON_EQ"
COMPARISON_EQ

True if the left argument is equal to the right argument.

"COMPARISON_NE"
COMPARISON_NE

True if the left argument is not equal to the right argument.

MetricThresholdEvaluationMissingData

EvaluationMissingDataUnspecified
EVALUATION_MISSING_DATA_UNSPECIFIED

An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.

EvaluationMissingDataInactive
EVALUATION_MISSING_DATA_INACTIVE

If there is no data to evaluate the condition, then evaluate the condition as false.

EvaluationMissingDataActive
EVALUATION_MISSING_DATA_ACTIVE

If there is no data to evaluate the condition, then evaluate the condition as true.

EvaluationMissingDataNoOp
EVALUATION_MISSING_DATA_NO_OP

Do not evaluate the condition to any value if there is no data.

MetricThresholdEvaluationMissingDataEvaluationMissingDataUnspecified
EVALUATION_MISSING_DATA_UNSPECIFIED

An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.

MetricThresholdEvaluationMissingDataEvaluationMissingDataInactive
EVALUATION_MISSING_DATA_INACTIVE

If there is no data to evaluate the condition, then evaluate the condition as false.

MetricThresholdEvaluationMissingDataEvaluationMissingDataActive
EVALUATION_MISSING_DATA_ACTIVE

If there is no data to evaluate the condition, then evaluate the condition as true.

MetricThresholdEvaluationMissingDataEvaluationMissingDataNoOp
EVALUATION_MISSING_DATA_NO_OP

Do not evaluate the condition to any value if there is no data.

EvaluationMissingDataUnspecified
EVALUATION_MISSING_DATA_UNSPECIFIED

An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.

EvaluationMissingDataInactive
EVALUATION_MISSING_DATA_INACTIVE

If there is no data to evaluate the condition, then evaluate the condition as false.

EvaluationMissingDataActive
EVALUATION_MISSING_DATA_ACTIVE

If there is no data to evaluate the condition, then evaluate the condition as true.

EvaluationMissingDataNoOp
EVALUATION_MISSING_DATA_NO_OP

Do not evaluate the condition to any value if there is no data.

EvaluationMissingDataUnspecified
EVALUATION_MISSING_DATA_UNSPECIFIED

An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.

EvaluationMissingDataInactive
EVALUATION_MISSING_DATA_INACTIVE

If there is no data to evaluate the condition, then evaluate the condition as false.

EvaluationMissingDataActive
EVALUATION_MISSING_DATA_ACTIVE

If there is no data to evaluate the condition, then evaluate the condition as true.

EvaluationMissingDataNoOp
EVALUATION_MISSING_DATA_NO_OP

Do not evaluate the condition to any value if there is no data.

EVALUATION_MISSING_DATA_UNSPECIFIED
EVALUATION_MISSING_DATA_UNSPECIFIED

An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.

EVALUATION_MISSING_DATA_INACTIVE
EVALUATION_MISSING_DATA_INACTIVE

If there is no data to evaluate the condition, then evaluate the condition as false.

EVALUATION_MISSING_DATA_ACTIVE
EVALUATION_MISSING_DATA_ACTIVE

If there is no data to evaluate the condition, then evaluate the condition as true.

EVALUATION_MISSING_DATA_NO_OP
EVALUATION_MISSING_DATA_NO_OP

Do not evaluate the condition to any value if there is no data.

"EVALUATION_MISSING_DATA_UNSPECIFIED"
EVALUATION_MISSING_DATA_UNSPECIFIED

An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.

"EVALUATION_MISSING_DATA_INACTIVE"
EVALUATION_MISSING_DATA_INACTIVE

If there is no data to evaluate the condition, then evaluate the condition as false.

"EVALUATION_MISSING_DATA_ACTIVE"
EVALUATION_MISSING_DATA_ACTIVE

If there is no data to evaluate the condition, then evaluate the condition as true.

"EVALUATION_MISSING_DATA_NO_OP"
EVALUATION_MISSING_DATA_NO_OP

Do not evaluate the condition to any value if there is no data.

MetricThresholdResponse

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.

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.

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.

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.

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.

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.

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

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

EvaluationMissingDataUnspecified
EVALUATION_MISSING_DATA_UNSPECIFIED

An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.

EvaluationMissingDataInactive
EVALUATION_MISSING_DATA_INACTIVE

If there is no data to evaluate the condition, then evaluate the condition as false.

EvaluationMissingDataActive
EVALUATION_MISSING_DATA_ACTIVE

If there is no data to evaluate the condition, then evaluate the condition as true.

EvaluationMissingDataNoOp
EVALUATION_MISSING_DATA_NO_OP

Do not evaluate the condition to any value if there is no data.

MonitoringQueryLanguageConditionEvaluationMissingDataEvaluationMissingDataUnspecified
EVALUATION_MISSING_DATA_UNSPECIFIED

An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.

MonitoringQueryLanguageConditionEvaluationMissingDataEvaluationMissingDataInactive
EVALUATION_MISSING_DATA_INACTIVE

If there is no data to evaluate the condition, then evaluate the condition as false.

MonitoringQueryLanguageConditionEvaluationMissingDataEvaluationMissingDataActive
EVALUATION_MISSING_DATA_ACTIVE

If there is no data to evaluate the condition, then evaluate the condition as true.

MonitoringQueryLanguageConditionEvaluationMissingDataEvaluationMissingDataNoOp
EVALUATION_MISSING_DATA_NO_OP

Do not evaluate the condition to any value if there is no data.

EvaluationMissingDataUnspecified
EVALUATION_MISSING_DATA_UNSPECIFIED

An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.

EvaluationMissingDataInactive
EVALUATION_MISSING_DATA_INACTIVE

If there is no data to evaluate the condition, then evaluate the condition as false.

EvaluationMissingDataActive
EVALUATION_MISSING_DATA_ACTIVE

If there is no data to evaluate the condition, then evaluate the condition as true.

EvaluationMissingDataNoOp
EVALUATION_MISSING_DATA_NO_OP

Do not evaluate the condition to any value if there is no data.

EvaluationMissingDataUnspecified
EVALUATION_MISSING_DATA_UNSPECIFIED

An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.

EvaluationMissingDataInactive
EVALUATION_MISSING_DATA_INACTIVE

If there is no data to evaluate the condition, then evaluate the condition as false.

EvaluationMissingDataActive
EVALUATION_MISSING_DATA_ACTIVE

If there is no data to evaluate the condition, then evaluate the condition as true.

EvaluationMissingDataNoOp
EVALUATION_MISSING_DATA_NO_OP

Do not evaluate the condition to any value if there is no data.

EVALUATION_MISSING_DATA_UNSPECIFIED
EVALUATION_MISSING_DATA_UNSPECIFIED

An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.

EVALUATION_MISSING_DATA_INACTIVE
EVALUATION_MISSING_DATA_INACTIVE

If there is no data to evaluate the condition, then evaluate the condition as false.

EVALUATION_MISSING_DATA_ACTIVE
EVALUATION_MISSING_DATA_ACTIVE

If there is no data to evaluate the condition, then evaluate the condition as true.

EVALUATION_MISSING_DATA_NO_OP
EVALUATION_MISSING_DATA_NO_OP

Do not evaluate the condition to any value if there is no data.

"EVALUATION_MISSING_DATA_UNSPECIFIED"
EVALUATION_MISSING_DATA_UNSPECIFIED

An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.

"EVALUATION_MISSING_DATA_INACTIVE"
EVALUATION_MISSING_DATA_INACTIVE

If there is no data to evaluate the condition, then evaluate the condition as false.

"EVALUATION_MISSING_DATA_ACTIVE"
EVALUATION_MISSING_DATA_ACTIVE

If there is no data to evaluate the condition, then evaluate the condition as true.

"EVALUATION_MISSING_DATA_NO_OP"
EVALUATION_MISSING_DATA_NO_OP

Do not evaluate the condition to any value if there is no data.

MonitoringQueryLanguageConditionResponse

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

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

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.

NotificationRateLimit

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

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.

Status

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

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

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

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
https://github.com/pulumi/pulumi-google-native
License
Apache-2.0