Google Cloud Native v0.28.0, Feb 2 23

google-native.monitoring/v1.Dashboard

Creates a new custom dashboard. For examples on how you can use this API to create dashboards, see Managing dashboards by API (https://cloud.google.com/monitoring/dashboards/api-dashboard). This method requires the monitoring.dashboards.create permission on the specified project. For more information about permissions, see Cloud Identity and Access Management (https://cloud.google.com/iam).

Create Dashboard Resource

new Dashboard(name: string, args: DashboardArgs, opts?: CustomResourceOptions);

@overload
def Dashboard(resource_name: str,
              opts: Optional[ResourceOptions] = None,
              column_layout: Optional[ColumnLayoutArgs] = None,
              dashboard_filters: Optional[Sequence[DashboardFilterArgs]] = None,
              display_name: Optional[str] = None,
              etag: Optional[str] = None,
              grid_layout: Optional[GridLayoutArgs] = None,
              labels: Optional[Mapping[str, str]] = None,
              mosaic_layout: Optional[MosaicLayoutArgs] = None,
              name: Optional[str] = None,
              project: Optional[str] = None,
              row_layout: Optional[RowLayoutArgs] = None,
              validate_only: Optional[bool] = None)
@overload
def Dashboard(resource_name: str,
              args: DashboardArgs,
              opts: Optional[ResourceOptions] = None)

func NewDashboard(ctx *Context, name string, args DashboardArgs, opts ...ResourceOption) (*Dashboard, error)

public Dashboard(string name, DashboardArgs args, CustomResourceOptions? opts = null)

public Dashboard(String name, DashboardArgs args)
public Dashboard(String name, DashboardArgs args, CustomResourceOptions options)

type: google-native:monitoring/v1:Dashboard
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

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

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

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

Dashboard 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 Dashboard resource accepts the following input properties:

DisplayName string: The mutable, human-readable name.
ColumnLayout Pulumi.GoogleNative.Monitoring.V1.Inputs.ColumnLayoutArgs: The content is divided into equally spaced columns and the widgets are arranged vertically.
DashboardFilters List<Pulumi.GoogleNative.Monitoring.V1.Inputs.DashboardFilterArgs>: Filters to reduce the amount of data charted based on the filter criteria.
Etag string: etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. An etag is returned in the response to GetDashboard, and users are expected to put that etag in the request to UpdateDashboard to ensure that their change will be applied to the same version of the Dashboard configuration. The field should not be passed during dashboard creation.
GridLayout Pulumi.GoogleNative.Monitoring.V1.Inputs.GridLayoutArgs: Content is arranged with a basic layout that re-flows a simple list of informational elements like widgets or tiles.
Labels Dictionary<string, string>: Labels applied to the dashboard
MosaicLayout Pulumi.GoogleNative.Monitoring.V1.Inputs.MosaicLayoutArgs: The content is arranged as a grid of tiles, with each content widget occupying one or more grid blocks.
Name string: Immutable. The resource name of the dashboard.
Project string
RowLayout Pulumi.GoogleNative.Monitoring.V1.Inputs.RowLayoutArgs: The content is divided into equally spaced rows and the widgets are arranged horizontally.
ValidateOnly bool: If set, validate the request and preview the review, but do not actually save it.

DisplayName string: The mutable, human-readable name.
ColumnLayout ColumnLayoutArgs: The content is divided into equally spaced columns and the widgets are arranged vertically.
DashboardFilters []DashboardFilterArgs: Filters to reduce the amount of data charted based on the filter criteria.
Etag string: etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. An etag is returned in the response to GetDashboard, and users are expected to put that etag in the request to UpdateDashboard to ensure that their change will be applied to the same version of the Dashboard configuration. The field should not be passed during dashboard creation.
GridLayout GridLayoutArgs: Content is arranged with a basic layout that re-flows a simple list of informational elements like widgets or tiles.
Labels map[string]string: Labels applied to the dashboard
MosaicLayout MosaicLayoutArgs: The content is arranged as a grid of tiles, with each content widget occupying one or more grid blocks.
Name string: Immutable. The resource name of the dashboard.
Project string
RowLayout RowLayoutArgs: The content is divided into equally spaced rows and the widgets are arranged horizontally.
ValidateOnly bool: If set, validate the request and preview the review, but do not actually save it.

displayName String: The mutable, human-readable name.
columnLayout ColumnLayoutArgs: The content is divided into equally spaced columns and the widgets are arranged vertically.
dashboardFilters List<DashboardFilterArgs>: Filters to reduce the amount of data charted based on the filter criteria.
etag String: etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. An etag is returned in the response to GetDashboard, and users are expected to put that etag in the request to UpdateDashboard to ensure that their change will be applied to the same version of the Dashboard configuration. The field should not be passed during dashboard creation.
gridLayout GridLayoutArgs: Content is arranged with a basic layout that re-flows a simple list of informational elements like widgets or tiles.
labels Map<String,String>: Labels applied to the dashboard
mosaicLayout MosaicLayoutArgs: The content is arranged as a grid of tiles, with each content widget occupying one or more grid blocks.
name String: Immutable. The resource name of the dashboard.
project String
rowLayout RowLayoutArgs: The content is divided into equally spaced rows and the widgets are arranged horizontally.
validateOnly Boolean: If set, validate the request and preview the review, but do not actually save it.

displayName string: The mutable, human-readable name.
columnLayout ColumnLayoutArgs: The content is divided into equally spaced columns and the widgets are arranged vertically.
dashboardFilters DashboardFilterArgs[]: Filters to reduce the amount of data charted based on the filter criteria.
etag string: etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. An etag is returned in the response to GetDashboard, and users are expected to put that etag in the request to UpdateDashboard to ensure that their change will be applied to the same version of the Dashboard configuration. The field should not be passed during dashboard creation.
gridLayout GridLayoutArgs: Content is arranged with a basic layout that re-flows a simple list of informational elements like widgets or tiles.
labels {[key: string]: string}: Labels applied to the dashboard
mosaicLayout MosaicLayoutArgs: The content is arranged as a grid of tiles, with each content widget occupying one or more grid blocks.
name string: Immutable. The resource name of the dashboard.
project string
rowLayout RowLayoutArgs: The content is divided into equally spaced rows and the widgets are arranged horizontally.
validateOnly boolean: If set, validate the request and preview the review, but do not actually save it.

display_name str: The mutable, human-readable name.
column_layout ColumnLayoutArgs: The content is divided into equally spaced columns and the widgets are arranged vertically.
dashboard_filters Sequence[DashboardFilterArgs]: Filters to reduce the amount of data charted based on the filter criteria.
etag str: etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. An etag is returned in the response to GetDashboard, and users are expected to put that etag in the request to UpdateDashboard to ensure that their change will be applied to the same version of the Dashboard configuration. The field should not be passed during dashboard creation.
grid_layout GridLayoutArgs: Content is arranged with a basic layout that re-flows a simple list of informational elements like widgets or tiles.
labels Mapping[str, str]: Labels applied to the dashboard
mosaic_layout MosaicLayoutArgs: The content is arranged as a grid of tiles, with each content widget occupying one or more grid blocks.
name str: Immutable. The resource name of the dashboard.
project str
row_layout RowLayoutArgs: The content is divided into equally spaced rows and the widgets are arranged horizontally.
validate_only bool: If set, validate the request and preview the review, but do not actually save it.

displayName String: The mutable, human-readable name.
columnLayout Property Map: The content is divided into equally spaced columns and the widgets are arranged vertically.
dashboardFilters List<Property Map>: Filters to reduce the amount of data charted based on the filter criteria.
etag String: etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. An etag is returned in the response to GetDashboard, and users are expected to put that etag in the request to UpdateDashboard to ensure that their change will be applied to the same version of the Dashboard configuration. The field should not be passed during dashboard creation.
gridLayout Property Map: Content is arranged with a basic layout that re-flows a simple list of informational elements like widgets or tiles.
labels Map<String>: Labels applied to the dashboard
mosaicLayout Property Map: The content is arranged as a grid of tiles, with each content widget occupying one or more grid blocks.
name String: Immutable. The resource name of the dashboard.
project String
rowLayout Property Map: The content is divided into equally spaced rows and the widgets are arranged horizontally.
validateOnly Boolean: If set, validate the request and preview the review, but do not actually save it.

Outputs

All input properties are implicitly available as output properties. Additionally, the Dashboard 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 2 years, or 104 weeks.
CrossSeriesReducer Pulumi.GoogleNative.Monitoring.V1.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.V1.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 2 years, or 104 weeks.
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 2 years, or 104 weeks.
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 2 years, or 104 weeks.
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 2 years, or 104 weeks.
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 2 years, or 104 weeks.
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 2 years, or 104 weeks.
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 2 years, or 104 weeks.
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 2 years, or 104 weeks.
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 2 years, or 104 weeks.
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 2 years, or 104 weeks.
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 2 years, or 104 weeks.
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.

AlertChart

Name string: The resource name of the alert policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID]

Name string: The resource name of the alert policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID]

name String: The resource name of the alert policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID]

name string: The resource name of the alert policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID]

name str: The resource name of the alert policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID]

name String: The resource name of the alert policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID]

AlertChartResponse

Name string: The resource name of the alert policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID]

Name string: The resource name of the alert policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID]

name String: The resource name of the alert policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID]

name string: The resource name of the alert policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID]

name str: The resource name of the alert policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID]

name String: The resource name of the alert policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID]

Axis

Label string: The label of the axis.
Scale Pulumi.GoogleNative.Monitoring.V1.AxisScale: The axis scale. By default, a linear scale is used.

Label string: The label of the axis.
Scale AxisScale: The axis scale. By default, a linear scale is used.

label String: The label of the axis.
scale AxisScale: The axis scale. By default, a linear scale is used.

label string: The label of the axis.
scale AxisScale: The axis scale. By default, a linear scale is used.

label str: The label of the axis.
scale AxisScale: The axis scale. By default, a linear scale is used.

label String: The label of the axis.
scale "SCALE_UNSPECIFIED" | "LINEAR" | "LOG10": The axis scale. By default, a linear scale is used.

AxisResponse

Label string: The label of the axis.
Scale string: The axis scale. By default, a linear scale is used.

Label string: The label of the axis.
Scale string: The axis scale. By default, a linear scale is used.

label String: The label of the axis.
scale String: The axis scale. By default, a linear scale is used.

label string: The label of the axis.
scale string: The axis scale. By default, a linear scale is used.

label str: The label of the axis.
scale str: The axis scale. By default, a linear scale is used.

label String: The label of the axis.
scale String: The axis scale. By default, a linear scale is used.

AxisScale

ScaleUnspecified: SCALE_UNSPECIFIED
Scale is unspecified. The view will default to LINEAR.
Linear: LINEAR
Linear scale.
Log10: LOG10
Logarithmic scale (base 10).

AxisScaleScaleUnspecified: SCALE_UNSPECIFIED
Scale is unspecified. The view will default to LINEAR.
AxisScaleLinear: LINEAR
Linear scale.
AxisScaleLog10: LOG10
Logarithmic scale (base 10).

ScaleUnspecified: SCALE_UNSPECIFIED
Scale is unspecified. The view will default to LINEAR.
Linear: LINEAR
Linear scale.
Log10: LOG10
Logarithmic scale (base 10).

ScaleUnspecified: SCALE_UNSPECIFIED
Scale is unspecified. The view will default to LINEAR.
Linear: LINEAR
Linear scale.
Log10: LOG10
Logarithmic scale (base 10).

SCALE_UNSPECIFIED: SCALE_UNSPECIFIED
Scale is unspecified. The view will default to LINEAR.
LINEAR: LINEAR
Linear scale.
LOG10: LOG10
Logarithmic scale (base 10).

"SCALE_UNSPECIFIED": SCALE_UNSPECIFIED
Scale is unspecified. The view will default to LINEAR.
"LINEAR": LINEAR
Linear scale.
"LOG10": LOG10
Logarithmic scale (base 10).

ChartOptions

Mode Pulumi.GoogleNative.Monitoring.V1.ChartOptionsMode: The chart mode.

Mode ChartOptionsMode: The chart mode.

mode ChartOptionsMode: The chart mode.

mode ChartOptionsMode: The chart mode.

mode ChartOptionsMode: The chart mode.

mode "MODE_UNSPECIFIED" | "COLOR" | "X_RAY" | "STATS": The chart mode.

ChartOptionsMode

ModeUnspecified: MODE_UNSPECIFIED
Mode is unspecified. The view will default to COLOR.
Color: COLOR
The chart distinguishes data series using different color. Line colors may get reused when there are many lines in the chart.
XRay: X_RAY
The chart uses the Stackdriver x-ray mode, in which each data set is plotted using the same semi-transparent color.
Stats: STATS
The chart displays statistics such as average, median, 95th percentile, and more.

ChartOptionsModeModeUnspecified: MODE_UNSPECIFIED
Mode is unspecified. The view will default to COLOR.
ChartOptionsModeColor: COLOR
The chart distinguishes data series using different color. Line colors may get reused when there are many lines in the chart.
ChartOptionsModeXRay: X_RAY
The chart uses the Stackdriver x-ray mode, in which each data set is plotted using the same semi-transparent color.
ChartOptionsModeStats: STATS
The chart displays statistics such as average, median, 95th percentile, and more.

ModeUnspecified: MODE_UNSPECIFIED
Mode is unspecified. The view will default to COLOR.
Color: COLOR
The chart distinguishes data series using different color. Line colors may get reused when there are many lines in the chart.
XRay: X_RAY
The chart uses the Stackdriver x-ray mode, in which each data set is plotted using the same semi-transparent color.
Stats: STATS
The chart displays statistics such as average, median, 95th percentile, and more.

ModeUnspecified: MODE_UNSPECIFIED
Mode is unspecified. The view will default to COLOR.
Color: COLOR
The chart distinguishes data series using different color. Line colors may get reused when there are many lines in the chart.
XRay: X_RAY
The chart uses the Stackdriver x-ray mode, in which each data set is plotted using the same semi-transparent color.
Stats: STATS
The chart displays statistics such as average, median, 95th percentile, and more.

MODE_UNSPECIFIED: MODE_UNSPECIFIED
Mode is unspecified. The view will default to COLOR.
COLOR: COLOR
The chart distinguishes data series using different color. Line colors may get reused when there are many lines in the chart.
X_RAY: X_RAY
The chart uses the Stackdriver x-ray mode, in which each data set is plotted using the same semi-transparent color.
STATS: STATS
The chart displays statistics such as average, median, 95th percentile, and more.

"MODE_UNSPECIFIED": MODE_UNSPECIFIED
Mode is unspecified. The view will default to COLOR.
"COLOR": COLOR
The chart distinguishes data series using different color. Line colors may get reused when there are many lines in the chart.
"X_RAY": X_RAY
The chart uses the Stackdriver x-ray mode, in which each data set is plotted using the same semi-transparent color.
"STATS": STATS
The chart displays statistics such as average, median, 95th percentile, and more.

ChartOptionsResponse

Mode string: The chart mode.

Mode string: The chart mode.

mode String: The chart mode.

mode string: The chart mode.

mode str: The chart mode.

mode String: The chart mode.

CollapsibleGroup

Collapsed bool: The collapsed state of the widget on first page load.

Collapsed bool: The collapsed state of the widget on first page load.

collapsed Boolean: The collapsed state of the widget on first page load.

collapsed boolean: The collapsed state of the widget on first page load.

collapsed bool: The collapsed state of the widget on first page load.

collapsed Boolean: The collapsed state of the widget on first page load.

CollapsibleGroupResponse

Collapsed bool: The collapsed state of the widget on first page load.

Collapsed bool: The collapsed state of the widget on first page load.

collapsed Boolean: The collapsed state of the widget on first page load.

collapsed boolean: The collapsed state of the widget on first page load.

collapsed bool: The collapsed state of the widget on first page load.

collapsed Boolean: The collapsed state of the widget on first page load.

Column

Weight string: The relative weight of this column. The column weight is used to adjust the width of columns on the screen (relative to peers). Greater the weight, greater the width of the column on the screen. If omitted, a value of 1 is used while rendering.
Widgets List<Pulumi.GoogleNative.Monitoring.V1.Inputs.Widget>: The display widgets arranged vertically in this column.

Weight string: The relative weight of this column. The column weight is used to adjust the width of columns on the screen (relative to peers). Greater the weight, greater the width of the column on the screen. If omitted, a value of 1 is used while rendering.
Widgets []Widget: The display widgets arranged vertically in this column.

weight String: The relative weight of this column. The column weight is used to adjust the width of columns on the screen (relative to peers). Greater the weight, greater the width of the column on the screen. If omitted, a value of 1 is used while rendering.
widgets List<Widget>: The display widgets arranged vertically in this column.

weight string: The relative weight of this column. The column weight is used to adjust the width of columns on the screen (relative to peers). Greater the weight, greater the width of the column on the screen. If omitted, a value of 1 is used while rendering.
widgets Widget[]: The display widgets arranged vertically in this column.

weight str: The relative weight of this column. The column weight is used to adjust the width of columns on the screen (relative to peers). Greater the weight, greater the width of the column on the screen. If omitted, a value of 1 is used while rendering.
widgets Sequence[Widget]: The display widgets arranged vertically in this column.

weight String: The relative weight of this column. The column weight is used to adjust the width of columns on the screen (relative to peers). Greater the weight, greater the width of the column on the screen. If omitted, a value of 1 is used while rendering.
widgets List<Property Map>: The display widgets arranged vertically in this column.

ColumnLayout

Columns List<Pulumi.GoogleNative.Monitoring.V1.Inputs.Column>: The columns of content to display.

Columns []Column: The columns of content to display.

columns List<Column>: The columns of content to display.

columns Column[]: The columns of content to display.

columns Sequence[Column]: The columns of content to display.

columns List<Property Map>: The columns of content to display.

ColumnLayoutResponse

Columns List<Pulumi.GoogleNative.Monitoring.V1.Inputs.ColumnResponse>: The columns of content to display.

Columns []ColumnResponse: The columns of content to display.

columns List<ColumnResponse>: The columns of content to display.

columns ColumnResponse[]: The columns of content to display.

columns Sequence[ColumnResponse]: The columns of content to display.

columns List<Property Map>: The columns of content to display.

ColumnResponse

Weight string: The relative weight of this column. The column weight is used to adjust the width of columns on the screen (relative to peers). Greater the weight, greater the width of the column on the screen. If omitted, a value of 1 is used while rendering.
Widgets List<Pulumi.GoogleNative.Monitoring.V1.Inputs.WidgetResponse>: The display widgets arranged vertically in this column.

Weight string: The relative weight of this column. The column weight is used to adjust the width of columns on the screen (relative to peers). Greater the weight, greater the width of the column on the screen. If omitted, a value of 1 is used while rendering.
Widgets []WidgetResponse: The display widgets arranged vertically in this column.

weight String: The relative weight of this column. The column weight is used to adjust the width of columns on the screen (relative to peers). Greater the weight, greater the width of the column on the screen. If omitted, a value of 1 is used while rendering.
widgets List<WidgetResponse>: The display widgets arranged vertically in this column.

weight string: The relative weight of this column. The column weight is used to adjust the width of columns on the screen (relative to peers). Greater the weight, greater the width of the column on the screen. If omitted, a value of 1 is used while rendering.
widgets WidgetResponse[]: The display widgets arranged vertically in this column.

weight str: The relative weight of this column. The column weight is used to adjust the width of columns on the screen (relative to peers). Greater the weight, greater the width of the column on the screen. If omitted, a value of 1 is used while rendering.
widgets Sequence[WidgetResponse]: The display widgets arranged vertically in this column.

weight String: The relative weight of this column. The column weight is used to adjust the width of columns on the screen (relative to peers). Greater the weight, greater the width of the column on the screen. If omitted, a value of 1 is used while rendering.
widgets List<Property Map>: The display widgets arranged vertically in this column.

ColumnSettings

Column string: The id of the column.
Visible bool: Whether the column should be visible on page load.

Column string: The id of the column.
Visible bool: Whether the column should be visible on page load.

column String: The id of the column.
visible Boolean: Whether the column should be visible on page load.

column string: The id of the column.
visible boolean: Whether the column should be visible on page load.

column str: The id of the column.
visible bool: Whether the column should be visible on page load.

column String: The id of the column.
visible Boolean: Whether the column should be visible on page load.

ColumnSettingsResponse

Column string: The id of the column.
Visible bool: Whether the column should be visible on page load.

Column string: The id of the column.
Visible bool: Whether the column should be visible on page load.

column String: The id of the column.
visible Boolean: Whether the column should be visible on page load.

column string: The id of the column.
visible boolean: Whether the column should be visible on page load.

column str: The id of the column.
visible bool: Whether the column should be visible on page load.

column String: The id of the column.
visible Boolean: Whether the column should be visible on page load.

DashboardFilter

LabelKey string: The key for the label
FilterType Pulumi.GoogleNative.Monitoring.V1.DashboardFilterFilterType: The specified filter type
StringValue string: A variable-length string value.
TemplateVariable string: The placeholder text that can be referenced in a filter string or MQL query. If omitted, the dashboard filter will be applied to all relevant widgets in the dashboard.

LabelKey string: The key for the label
FilterType DashboardFilterFilterType: The specified filter type
StringValue string: A variable-length string value.
TemplateVariable string: The placeholder text that can be referenced in a filter string or MQL query. If omitted, the dashboard filter will be applied to all relevant widgets in the dashboard.

labelKey String: The key for the label
filterType DashboardFilterFilterType: The specified filter type
stringValue String: A variable-length string value.
templateVariable String: The placeholder text that can be referenced in a filter string or MQL query. If omitted, the dashboard filter will be applied to all relevant widgets in the dashboard.

labelKey string: The key for the label
filterType DashboardFilterFilterType: The specified filter type
stringValue string: A variable-length string value.
templateVariable string: The placeholder text that can be referenced in a filter string or MQL query. If omitted, the dashboard filter will be applied to all relevant widgets in the dashboard.

label_key str: The key for the label
filter_type DashboardFilterFilterType: The specified filter type
string_value str: A variable-length string value.
template_variable str: The placeholder text that can be referenced in a filter string or MQL query. If omitted, the dashboard filter will be applied to all relevant widgets in the dashboard.

labelKey String: The key for the label
filterType "FILTER_TYPE_UNSPECIFIED" | "RESOURCE_LABEL" | "METRIC_LABEL" | "USER_METADATA_LABEL" | "SYSTEM_METADATA_LABEL" | "GROUP": The specified filter type
stringValue String: A variable-length string value.
templateVariable String: The placeholder text that can be referenced in a filter string or MQL query. If omitted, the dashboard filter will be applied to all relevant widgets in the dashboard.

DashboardFilterFilterType

FilterTypeUnspecified: FILTER_TYPE_UNSPECIFIED
Filter type is unspecified. This is not valid in a well-formed request.
ResourceLabel: RESOURCE_LABEL
Filter on a resource label value
MetricLabel: METRIC_LABEL
Filter on a metrics label value
UserMetadataLabel: USER_METADATA_LABEL
Filter on a user metadata label value
SystemMetadataLabel: SYSTEM_METADATA_LABEL
Filter on a system metadata label value
Group: GROUP
Filter on a group id

DashboardFilterFilterTypeFilterTypeUnspecified: FILTER_TYPE_UNSPECIFIED
Filter type is unspecified. This is not valid in a well-formed request.
DashboardFilterFilterTypeResourceLabel: RESOURCE_LABEL
Filter on a resource label value
DashboardFilterFilterTypeMetricLabel: METRIC_LABEL
Filter on a metrics label value
DashboardFilterFilterTypeUserMetadataLabel: USER_METADATA_LABEL
Filter on a user metadata label value
DashboardFilterFilterTypeSystemMetadataLabel: SYSTEM_METADATA_LABEL
Filter on a system metadata label value
DashboardFilterFilterTypeGroup: GROUP
Filter on a group id

FilterTypeUnspecified: FILTER_TYPE_UNSPECIFIED
Filter type is unspecified. This is not valid in a well-formed request.
ResourceLabel: RESOURCE_LABEL
Filter on a resource label value
MetricLabel: METRIC_LABEL
Filter on a metrics label value
UserMetadataLabel: USER_METADATA_LABEL
Filter on a user metadata label value
SystemMetadataLabel: SYSTEM_METADATA_LABEL
Filter on a system metadata label value
Group: GROUP
Filter on a group id

FilterTypeUnspecified: FILTER_TYPE_UNSPECIFIED
Filter type is unspecified. This is not valid in a well-formed request.
ResourceLabel: RESOURCE_LABEL
Filter on a resource label value
MetricLabel: METRIC_LABEL
Filter on a metrics label value
UserMetadataLabel: USER_METADATA_LABEL
Filter on a user metadata label value
SystemMetadataLabel: SYSTEM_METADATA_LABEL
Filter on a system metadata label value
Group: GROUP
Filter on a group id

FILTER_TYPE_UNSPECIFIED: FILTER_TYPE_UNSPECIFIED
Filter type is unspecified. This is not valid in a well-formed request.
RESOURCE_LABEL: RESOURCE_LABEL
Filter on a resource label value
METRIC_LABEL: METRIC_LABEL
Filter on a metrics label value
USER_METADATA_LABEL: USER_METADATA_LABEL
Filter on a user metadata label value
SYSTEM_METADATA_LABEL: SYSTEM_METADATA_LABEL
Filter on a system metadata label value
GROUP: GROUP
Filter on a group id

"FILTER_TYPE_UNSPECIFIED": FILTER_TYPE_UNSPECIFIED
Filter type is unspecified. This is not valid in a well-formed request.
"RESOURCE_LABEL": RESOURCE_LABEL
Filter on a resource label value
"METRIC_LABEL": METRIC_LABEL
Filter on a metrics label value
"USER_METADATA_LABEL": USER_METADATA_LABEL
Filter on a user metadata label value
"SYSTEM_METADATA_LABEL": SYSTEM_METADATA_LABEL
Filter on a system metadata label value
"GROUP": GROUP
Filter on a group id

DashboardFilterResponse

FilterType string: The specified filter type
LabelKey string: The key for the label
StringValue string: A variable-length string value.
TemplateVariable string: The placeholder text that can be referenced in a filter string or MQL query. If omitted, the dashboard filter will be applied to all relevant widgets in the dashboard.

FilterType string: The specified filter type
LabelKey string: The key for the label
StringValue string: A variable-length string value.
TemplateVariable string: The placeholder text that can be referenced in a filter string or MQL query. If omitted, the dashboard filter will be applied to all relevant widgets in the dashboard.

filterType String: The specified filter type
labelKey String: The key for the label
stringValue String: A variable-length string value.
templateVariable String: The placeholder text that can be referenced in a filter string or MQL query. If omitted, the dashboard filter will be applied to all relevant widgets in the dashboard.

filterType string: The specified filter type
labelKey string: The key for the label
stringValue string: A variable-length string value.
templateVariable string: The placeholder text that can be referenced in a filter string or MQL query. If omitted, the dashboard filter will be applied to all relevant widgets in the dashboard.

filter_type str: The specified filter type
label_key str: The key for the label
string_value str: A variable-length string value.
template_variable str: The placeholder text that can be referenced in a filter string or MQL query. If omitted, the dashboard filter will be applied to all relevant widgets in the dashboard.

filterType String: The specified filter type
labelKey String: The key for the label
stringValue String: A variable-length string value.
templateVariable String: The placeholder text that can be referenced in a filter string or MQL query. If omitted, the dashboard filter will be applied to all relevant widgets in the dashboard.

DataSet

TimeSeriesQuery Pulumi.GoogleNative.Monitoring.V1.Inputs.TimeSeriesQuery: Fields for querying time series data from the Stackdriver metrics API.
LegendTemplate string: A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value.
MinAlignmentPeriod string: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
PlotType Pulumi.GoogleNative.Monitoring.V1.DataSetPlotType: How this data should be plotted on the chart.
TargetAxis Pulumi.GoogleNative.Monitoring.V1.DataSetTargetAxis: Optional. The target axis to use for plotting the metric.

TimeSeriesQuery TimeSeriesQuery: Fields for querying time series data from the Stackdriver metrics API.
LegendTemplate string: A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value.
MinAlignmentPeriod string: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
PlotType DataSetPlotType: How this data should be plotted on the chart.
TargetAxis DataSetTargetAxis: Optional. The target axis to use for plotting the metric.

timeSeriesQuery TimeSeriesQuery: Fields for querying time series data from the Stackdriver metrics API.
legendTemplate String: A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value.
minAlignmentPeriod String: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
plotType DataSetPlotType: How this data should be plotted on the chart.
targetAxis DataSetTargetAxis: Optional. The target axis to use for plotting the metric.

timeSeriesQuery TimeSeriesQuery: Fields for querying time series data from the Stackdriver metrics API.
legendTemplate string: A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value.
minAlignmentPeriod string: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
plotType DataSetPlotType: How this data should be plotted on the chart.
targetAxis DataSetTargetAxis: Optional. The target axis to use for plotting the metric.

time_series_query TimeSeriesQuery: Fields for querying time series data from the Stackdriver metrics API.
legend_template str: A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value.
min_alignment_period str: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
plot_type DataSetPlotType: How this data should be plotted on the chart.
target_axis DataSetTargetAxis: Optional. The target axis to use for plotting the metric.

timeSeriesQuery Property Map: Fields for querying time series data from the Stackdriver metrics API.
legendTemplate String: A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value.
minAlignmentPeriod String: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
plotType "PLOT_TYPE_UNSPECIFIED" | "LINE" | "STACKED_AREA" | "STACKED_BAR" | "HEATMAP": How this data should be plotted on the chart.
targetAxis "TARGET_AXIS_UNSPECIFIED" | "Y1" | "Y2": Optional. The target axis to use for plotting the metric.

DataSetPlotType

PlotTypeUnspecified: PLOT_TYPE_UNSPECIFIED
Plot type is unspecified. The view will default to LINE.
Line: LINE
The data is plotted as a set of lines (one line per series).
StackedArea: STACKED_AREA
The data is plotted as a set of filled areas (one area per series), with the areas stacked vertically (the base of each area is the top of its predecessor, and the base of the first area is the X axis). Since the areas do not overlap, each is filled with a different opaque color.
StackedBar: STACKED_BAR
The data is plotted as a set of rectangular boxes (one box per series), with the boxes stacked vertically (the base of each box is the top of its predecessor, and the base of the first box is the X axis). Since the boxes do not overlap, each is filled with a different opaque color.
Heatmap: HEATMAP
The data is plotted as a heatmap. The series being plotted must have a DISTRIBUTION value type. The value of each bucket in the distribution is displayed as a color. This type is not currently available in the Stackdriver Monitoring application.

DataSetPlotTypePlotTypeUnspecified: PLOT_TYPE_UNSPECIFIED
Plot type is unspecified. The view will default to LINE.
DataSetPlotTypeLine: LINE
The data is plotted as a set of lines (one line per series).
DataSetPlotTypeStackedArea: STACKED_AREA
The data is plotted as a set of filled areas (one area per series), with the areas stacked vertically (the base of each area is the top of its predecessor, and the base of the first area is the X axis). Since the areas do not overlap, each is filled with a different opaque color.
DataSetPlotTypeStackedBar: STACKED_BAR
The data is plotted as a set of rectangular boxes (one box per series), with the boxes stacked vertically (the base of each box is the top of its predecessor, and the base of the first box is the X axis). Since the boxes do not overlap, each is filled with a different opaque color.
DataSetPlotTypeHeatmap: HEATMAP
The data is plotted as a heatmap. The series being plotted must have a DISTRIBUTION value type. The value of each bucket in the distribution is displayed as a color. This type is not currently available in the Stackdriver Monitoring application.

PlotTypeUnspecified: PLOT_TYPE_UNSPECIFIED
Plot type is unspecified. The view will default to LINE.
Line: LINE
The data is plotted as a set of lines (one line per series).
StackedArea: STACKED_AREA
The data is plotted as a set of filled areas (one area per series), with the areas stacked vertically (the base of each area is the top of its predecessor, and the base of the first area is the X axis). Since the areas do not overlap, each is filled with a different opaque color.
StackedBar: STACKED_BAR
The data is plotted as a set of rectangular boxes (one box per series), with the boxes stacked vertically (the base of each box is the top of its predecessor, and the base of the first box is the X axis). Since the boxes do not overlap, each is filled with a different opaque color.
Heatmap: HEATMAP
The data is plotted as a heatmap. The series being plotted must have a DISTRIBUTION value type. The value of each bucket in the distribution is displayed as a color. This type is not currently available in the Stackdriver Monitoring application.

PlotTypeUnspecified: PLOT_TYPE_UNSPECIFIED
Plot type is unspecified. The view will default to LINE.
Line: LINE
The data is plotted as a set of lines (one line per series).
StackedArea: STACKED_AREA
The data is plotted as a set of filled areas (one area per series), with the areas stacked vertically (the base of each area is the top of its predecessor, and the base of the first area is the X axis). Since the areas do not overlap, each is filled with a different opaque color.
StackedBar: STACKED_BAR
The data is plotted as a set of rectangular boxes (one box per series), with the boxes stacked vertically (the base of each box is the top of its predecessor, and the base of the first box is the X axis). Since the boxes do not overlap, each is filled with a different opaque color.
Heatmap: HEATMAP
The data is plotted as a heatmap. The series being plotted must have a DISTRIBUTION value type. The value of each bucket in the distribution is displayed as a color. This type is not currently available in the Stackdriver Monitoring application.

PLOT_TYPE_UNSPECIFIED: PLOT_TYPE_UNSPECIFIED
Plot type is unspecified. The view will default to LINE.
LINE: LINE
The data is plotted as a set of lines (one line per series).
STACKED_AREA: STACKED_AREA
The data is plotted as a set of filled areas (one area per series), with the areas stacked vertically (the base of each area is the top of its predecessor, and the base of the first area is the X axis). Since the areas do not overlap, each is filled with a different opaque color.
STACKED_BAR: STACKED_BAR
The data is plotted as a set of rectangular boxes (one box per series), with the boxes stacked vertically (the base of each box is the top of its predecessor, and the base of the first box is the X axis). Since the boxes do not overlap, each is filled with a different opaque color.
HEATMAP: HEATMAP
The data is plotted as a heatmap. The series being plotted must have a DISTRIBUTION value type. The value of each bucket in the distribution is displayed as a color. This type is not currently available in the Stackdriver Monitoring application.

"PLOT_TYPE_UNSPECIFIED": PLOT_TYPE_UNSPECIFIED
Plot type is unspecified. The view will default to LINE.
"LINE": LINE
The data is plotted as a set of lines (one line per series).
"STACKED_AREA": STACKED_AREA
The data is plotted as a set of filled areas (one area per series), with the areas stacked vertically (the base of each area is the top of its predecessor, and the base of the first area is the X axis). Since the areas do not overlap, each is filled with a different opaque color.
"STACKED_BAR": STACKED_BAR
The data is plotted as a set of rectangular boxes (one box per series), with the boxes stacked vertically (the base of each box is the top of its predecessor, and the base of the first box is the X axis). Since the boxes do not overlap, each is filled with a different opaque color.
"HEATMAP": HEATMAP
The data is plotted as a heatmap. The series being plotted must have a DISTRIBUTION value type. The value of each bucket in the distribution is displayed as a color. This type is not currently available in the Stackdriver Monitoring application.

DataSetResponse

LegendTemplate string: A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value.
MinAlignmentPeriod string: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
PlotType string: How this data should be plotted on the chart.
TargetAxis string: Optional. The target axis to use for plotting the metric.
TimeSeriesQuery Pulumi.GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryResponse: Fields for querying time series data from the Stackdriver metrics API.

LegendTemplate string: A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value.
MinAlignmentPeriod string: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
PlotType string: How this data should be plotted on the chart.
TargetAxis string: Optional. The target axis to use for plotting the metric.
TimeSeriesQuery TimeSeriesQueryResponse: Fields for querying time series data from the Stackdriver metrics API.

legendTemplate String: A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value.
minAlignmentPeriod String: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
plotType String: How this data should be plotted on the chart.
targetAxis String: Optional. The target axis to use for plotting the metric.
timeSeriesQuery TimeSeriesQueryResponse: Fields for querying time series data from the Stackdriver metrics API.

legendTemplate string: A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value.
minAlignmentPeriod string: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
plotType string: How this data should be plotted on the chart.
targetAxis string: Optional. The target axis to use for plotting the metric.
timeSeriesQuery TimeSeriesQueryResponse: Fields for querying time series data from the Stackdriver metrics API.

legend_template str: A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value.
min_alignment_period str: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
plot_type str: How this data should be plotted on the chart.
target_axis str: Optional. The target axis to use for plotting the metric.
time_series_query TimeSeriesQueryResponse: Fields for querying time series data from the Stackdriver metrics API.

legendTemplate String: A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value.
minAlignmentPeriod String: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
plotType String: How this data should be plotted on the chart.
targetAxis String: Optional. The target axis to use for plotting the metric.
timeSeriesQuery Property Map: Fields for querying time series data from the Stackdriver metrics API.

DataSetTargetAxis

TargetAxisUnspecified: TARGET_AXIS_UNSPECIFIED
The target axis was not specified. Defaults to Y1.
Y1: Y1
The y_axis (the right axis of chart).
Y2: Y2
The y2_axis (the left axis of chart).

DataSetTargetAxisTargetAxisUnspecified: TARGET_AXIS_UNSPECIFIED
The target axis was not specified. Defaults to Y1.
DataSetTargetAxisY1: Y1
The y_axis (the right axis of chart).
DataSetTargetAxisY2: Y2
The y2_axis (the left axis of chart).

TargetAxisUnspecified: TARGET_AXIS_UNSPECIFIED
The target axis was not specified. Defaults to Y1.
Y1: Y1
The y_axis (the right axis of chart).
Y2: Y2
The y2_axis (the left axis of chart).

TargetAxisUnspecified: TARGET_AXIS_UNSPECIFIED
The target axis was not specified. Defaults to Y1.
Y1: Y1
The y_axis (the right axis of chart).
Y2: Y2
The y2_axis (the left axis of chart).

TARGET_AXIS_UNSPECIFIED: TARGET_AXIS_UNSPECIFIED
The target axis was not specified. Defaults to Y1.
Y1: Y1
The y_axis (the right axis of chart).
Y2: Y2
The y2_axis (the left axis of chart).

"TARGET_AXIS_UNSPECIFIED": TARGET_AXIS_UNSPECIFIED
The target axis was not specified. Defaults to Y1.
"Y1": Y1
The y_axis (the right axis of chart).
"Y2": Y2
The y2_axis (the left axis of chart).

GaugeView

LowerBound double: The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
UpperBound double: The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.

LowerBound float64: The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
UpperBound float64: The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.

lowerBound Double: The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
upperBound Double: The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.

lowerBound number: The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
upperBound number: The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.

lower_bound float: The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
upper_bound float: The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.

lowerBound Number: The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
upperBound Number: The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.

GaugeViewResponse

LowerBound double: The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
UpperBound double: The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.

LowerBound float64: The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
UpperBound float64: The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.

lowerBound Double: The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
upperBound Double: The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.

lowerBound number: The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
upperBound number: The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.

lower_bound float: The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
upper_bound float: The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.

lowerBound Number: The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
upperBound Number: The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.

GridLayout

Columns string: The number of columns into which the view's width is divided. If omitted or set to zero, a system default will be used while rendering.
Widgets List<Pulumi.GoogleNative.Monitoring.V1.Inputs.Widget>: The informational elements that are arranged into the columns row-first.

Columns string: The number of columns into which the view's width is divided. If omitted or set to zero, a system default will be used while rendering.
Widgets []Widget: The informational elements that are arranged into the columns row-first.

columns String: The number of columns into which the view's width is divided. If omitted or set to zero, a system default will be used while rendering.
widgets List<Widget>: The informational elements that are arranged into the columns row-first.

columns string: The number of columns into which the view's width is divided. If omitted or set to zero, a system default will be used while rendering.
widgets Widget[]: The informational elements that are arranged into the columns row-first.

columns str: The number of columns into which the view's width is divided. If omitted or set to zero, a system default will be used while rendering.
widgets Sequence[Widget]: The informational elements that are arranged into the columns row-first.

columns String: The number of columns into which the view's width is divided. If omitted or set to zero, a system default will be used while rendering.
widgets List<Property Map>: The informational elements that are arranged into the columns row-first.

GridLayoutResponse

Columns string: The number of columns into which the view's width is divided. If omitted or set to zero, a system default will be used while rendering.
Widgets List<Pulumi.GoogleNative.Monitoring.V1.Inputs.WidgetResponse>: The informational elements that are arranged into the columns row-first.

Columns string: The number of columns into which the view's width is divided. If omitted or set to zero, a system default will be used while rendering.
Widgets []WidgetResponse: The informational elements that are arranged into the columns row-first.

columns String: The number of columns into which the view's width is divided. If omitted or set to zero, a system default will be used while rendering.
widgets List<WidgetResponse>: The informational elements that are arranged into the columns row-first.

columns string: The number of columns into which the view's width is divided. If omitted or set to zero, a system default will be used while rendering.
widgets WidgetResponse[]: The informational elements that are arranged into the columns row-first.

columns str: The number of columns into which the view's width is divided. If omitted or set to zero, a system default will be used while rendering.
widgets Sequence[WidgetResponse]: The informational elements that are arranged into the columns row-first.

columns String: The number of columns into which the view's width is divided. If omitted or set to zero, a system default will be used while rendering.
widgets List<Property Map>: The informational elements that are arranged into the columns row-first.

LogsPanel

Filter string: A filter that chooses which log entries to return. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries). Only log entries that match the filter are returned. An empty filter matches all log entries.
ResourceNames List<string>: The names of logging resources to collect logs for. Currently only projects are supported. If empty, the widget will default to the host project.

Filter string: A filter that chooses which log entries to return. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries). Only log entries that match the filter are returned. An empty filter matches all log entries.
ResourceNames []string: The names of logging resources to collect logs for. Currently only projects are supported. If empty, the widget will default to the host project.

filter String: A filter that chooses which log entries to return. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries). Only log entries that match the filter are returned. An empty filter matches all log entries.
resourceNames List<String>: The names of logging resources to collect logs for. Currently only projects are supported. If empty, the widget will default to the host project.

filter string: A filter that chooses which log entries to return. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries). Only log entries that match the filter are returned. An empty filter matches all log entries.
resourceNames string[]: The names of logging resources to collect logs for. Currently only projects are supported. If empty, the widget will default to the host project.

filter str: A filter that chooses which log entries to return. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries). Only log entries that match the filter are returned. An empty filter matches all log entries.
resource_names Sequence[str]: The names of logging resources to collect logs for. Currently only projects are supported. If empty, the widget will default to the host project.

filter String: A filter that chooses which log entries to return. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries). Only log entries that match the filter are returned. An empty filter matches all log entries.
resourceNames List<String>: The names of logging resources to collect logs for. Currently only projects are supported. If empty, the widget will default to the host project.

LogsPanelResponse

Filter string: A filter that chooses which log entries to return. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries). Only log entries that match the filter are returned. An empty filter matches all log entries.
ResourceNames List<string>: The names of logging resources to collect logs for. Currently only projects are supported. If empty, the widget will default to the host project.

Filter string: A filter that chooses which log entries to return. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries). Only log entries that match the filter are returned. An empty filter matches all log entries.
ResourceNames []string: The names of logging resources to collect logs for. Currently only projects are supported. If empty, the widget will default to the host project.

filter String: A filter that chooses which log entries to return. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries). Only log entries that match the filter are returned. An empty filter matches all log entries.
resourceNames List<String>: The names of logging resources to collect logs for. Currently only projects are supported. If empty, the widget will default to the host project.

filter string: A filter that chooses which log entries to return. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries). Only log entries that match the filter are returned. An empty filter matches all log entries.
resourceNames string[]: The names of logging resources to collect logs for. Currently only projects are supported. If empty, the widget will default to the host project.

filter str: A filter that chooses which log entries to return. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries). Only log entries that match the filter are returned. An empty filter matches all log entries.
resource_names Sequence[str]: The names of logging resources to collect logs for. Currently only projects are supported. If empty, the widget will default to the host project.

filter String: A filter that chooses which log entries to return. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries). Only log entries that match the filter are returned. An empty filter matches all log entries.
resourceNames List<String>: The names of logging resources to collect logs for. Currently only projects are supported. If empty, the widget will default to the host project.

MosaicLayout

Columns int: The number of columns in the mosaic grid. The number of columns must be between 1 and 12, inclusive.
Tiles List<Pulumi.GoogleNative.Monitoring.V1.Inputs.Tile>: The tiles to display.

Columns int: The number of columns in the mosaic grid. The number of columns must be between 1 and 12, inclusive.
Tiles []Tile: The tiles to display.

columns Integer: The number of columns in the mosaic grid. The number of columns must be between 1 and 12, inclusive.
tiles List<Tile>: The tiles to display.

columns number: The number of columns in the mosaic grid. The number of columns must be between 1 and 12, inclusive.
tiles Tile[]: The tiles to display.

columns int: The number of columns in the mosaic grid. The number of columns must be between 1 and 12, inclusive.
tiles Sequence[Tile]: The tiles to display.

columns Number: The number of columns in the mosaic grid. The number of columns must be between 1 and 12, inclusive.
tiles List<Property Map>: The tiles to display.

MosaicLayoutResponse

Columns int: The number of columns in the mosaic grid. The number of columns must be between 1 and 12, inclusive.
Tiles List<Pulumi.GoogleNative.Monitoring.V1.Inputs.TileResponse>: The tiles to display.

Columns int: The number of columns in the mosaic grid. The number of columns must be between 1 and 12, inclusive.
Tiles []TileResponse: The tiles to display.

columns Integer: The number of columns in the mosaic grid. The number of columns must be between 1 and 12, inclusive.
tiles List<TileResponse>: The tiles to display.

columns number: The number of columns in the mosaic grid. The number of columns must be between 1 and 12, inclusive.
tiles TileResponse[]: The tiles to display.

columns int: The number of columns in the mosaic grid. The number of columns must be between 1 and 12, inclusive.
tiles Sequence[TileResponse]: The tiles to display.

columns Number: The number of columns in the mosaic grid. The number of columns must be between 1 and 12, inclusive.
tiles List<Property Map>: The tiles to display.

PickTimeSeriesFilter

Direction Pulumi.GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection: How to use the ranking to select time series that pass through the filter.
NumTimeSeries int: How many time series to allow to pass through the filter.
RankingMethod Pulumi.GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod: ranking_method is applied to each time series independently to produce the value which will be used to compare the time series to other time series.

Direction PickTimeSeriesFilterDirection: How to use the ranking to select time series that pass through the filter.
NumTimeSeries int: How many time series to allow to pass through the filter.
RankingMethod PickTimeSeriesFilterRankingMethod: ranking_method is applied to each time series independently to produce the value which will be used to compare the time series to other time series.

direction PickTimeSeriesFilterDirection: How to use the ranking to select time series that pass through the filter.
numTimeSeries Integer: How many time series to allow to pass through the filter.
rankingMethod PickTimeSeriesFilterRankingMethod: ranking_method is applied to each time series independently to produce the value which will be used to compare the time series to other time series.

direction PickTimeSeriesFilterDirection: How to use the ranking to select time series that pass through the filter.
numTimeSeries number: How many time series to allow to pass through the filter.
rankingMethod PickTimeSeriesFilterRankingMethod: ranking_method is applied to each time series independently to produce the value which will be used to compare the time series to other time series.

direction PickTimeSeriesFilterDirection: How to use the ranking to select time series that pass through the filter.
num_time_series int: How many time series to allow to pass through the filter.
ranking_method PickTimeSeriesFilterRankingMethod: ranking_method is applied to each time series independently to produce the value which will be used to compare the time series to other time series.

direction "DIRECTION_UNSPECIFIED" | "TOP" | "BOTTOM": How to use the ranking to select time series that pass through the filter.
numTimeSeries Number: How many time series to allow to pass through the filter.
rankingMethod "METHOD_UNSPECIFIED" | "METHOD_MEAN" | "METHOD_MAX" | "METHOD_MIN" | "METHOD_SUM" | "METHOD_LATEST": ranking_method is applied to each time series independently to produce the value which will be used to compare the time series to other time series.

PickTimeSeriesFilterDirection

DirectionUnspecified: DIRECTION_UNSPECIFIED
Not allowed. You must specify a different Direction if you specify a PickTimeSeriesFilter.
Top: TOP
Pass the highest num_time_series ranking inputs.
Bottom: BOTTOM
Pass the lowest num_time_series ranking inputs.

PickTimeSeriesFilterDirectionDirectionUnspecified: DIRECTION_UNSPECIFIED
Not allowed. You must specify a different Direction if you specify a PickTimeSeriesFilter.
PickTimeSeriesFilterDirectionTop: TOP
Pass the highest num_time_series ranking inputs.
PickTimeSeriesFilterDirectionBottom: BOTTOM
Pass the lowest num_time_series ranking inputs.

DirectionUnspecified: DIRECTION_UNSPECIFIED
Not allowed. You must specify a different Direction if you specify a PickTimeSeriesFilter.
Top: TOP
Pass the highest num_time_series ranking inputs.
Bottom: BOTTOM
Pass the lowest num_time_series ranking inputs.

DirectionUnspecified: DIRECTION_UNSPECIFIED
Not allowed. You must specify a different Direction if you specify a PickTimeSeriesFilter.
Top: TOP
Pass the highest num_time_series ranking inputs.
Bottom: BOTTOM
Pass the lowest num_time_series ranking inputs.

DIRECTION_UNSPECIFIED: DIRECTION_UNSPECIFIED
Not allowed. You must specify a different Direction if you specify a PickTimeSeriesFilter.
TOP: TOP
Pass the highest num_time_series ranking inputs.
BOTTOM: BOTTOM
Pass the lowest num_time_series ranking inputs.

"DIRECTION_UNSPECIFIED": DIRECTION_UNSPECIFIED
Not allowed. You must specify a different Direction if you specify a PickTimeSeriesFilter.
"TOP": TOP
Pass the highest num_time_series ranking inputs.
"BOTTOM": BOTTOM
Pass the lowest num_time_series ranking inputs.

PickTimeSeriesFilterRankingMethod

MethodUnspecified: METHOD_UNSPECIFIED
Not allowed. You must specify a different Method if you specify a PickTimeSeriesFilter.
MethodMean: METHOD_MEAN
Select the mean of all values.
MethodMax: METHOD_MAX
Select the maximum value.
MethodMin: METHOD_MIN
Select the minimum value.
MethodSum: METHOD_SUM
Compute the sum of all values.
MethodLatest: METHOD_LATEST
Select the most recent value.

PickTimeSeriesFilterRankingMethodMethodUnspecified: METHOD_UNSPECIFIED
Not allowed. You must specify a different Method if you specify a PickTimeSeriesFilter.
PickTimeSeriesFilterRankingMethodMethodMean: METHOD_MEAN
Select the mean of all values.
PickTimeSeriesFilterRankingMethodMethodMax: METHOD_MAX
Select the maximum value.
PickTimeSeriesFilterRankingMethodMethodMin: METHOD_MIN
Select the minimum value.
PickTimeSeriesFilterRankingMethodMethodSum: METHOD_SUM
Compute the sum of all values.
PickTimeSeriesFilterRankingMethodMethodLatest: METHOD_LATEST
Select the most recent value.

MethodUnspecified: METHOD_UNSPECIFIED
Not allowed. You must specify a different Method if you specify a PickTimeSeriesFilter.
MethodMean: METHOD_MEAN
Select the mean of all values.
MethodMax: METHOD_MAX
Select the maximum value.
MethodMin: METHOD_MIN
Select the minimum value.
MethodSum: METHOD_SUM
Compute the sum of all values.
MethodLatest: METHOD_LATEST
Select the most recent value.

MethodUnspecified: METHOD_UNSPECIFIED
Not allowed. You must specify a different Method if you specify a PickTimeSeriesFilter.
MethodMean: METHOD_MEAN
Select the mean of all values.
MethodMax: METHOD_MAX
Select the maximum value.
MethodMin: METHOD_MIN
Select the minimum value.
MethodSum: METHOD_SUM
Compute the sum of all values.
MethodLatest: METHOD_LATEST
Select the most recent value.

METHOD_UNSPECIFIED: METHOD_UNSPECIFIED
Not allowed. You must specify a different Method if you specify a PickTimeSeriesFilter.
METHOD_MEAN: METHOD_MEAN
Select the mean of all values.
METHOD_MAX: METHOD_MAX
Select the maximum value.
METHOD_MIN: METHOD_MIN
Select the minimum value.
METHOD_SUM: METHOD_SUM
Compute the sum of all values.
METHOD_LATEST: METHOD_LATEST
Select the most recent value.

"METHOD_UNSPECIFIED": METHOD_UNSPECIFIED
Not allowed. You must specify a different Method if you specify a PickTimeSeriesFilter.
"METHOD_MEAN": METHOD_MEAN
Select the mean of all values.
"METHOD_MAX": METHOD_MAX
Select the maximum value.
"METHOD_MIN": METHOD_MIN
Select the minimum value.
"METHOD_SUM": METHOD_SUM
Compute the sum of all values.
"METHOD_LATEST": METHOD_LATEST
Select the most recent value.

PickTimeSeriesFilterResponse

Direction string: How to use the ranking to select time series that pass through the filter.
NumTimeSeries int: How many time series to allow to pass through the filter.
RankingMethod string: ranking_method is applied to each time series independently to produce the value which will be used to compare the time series to other time series.

Direction string: How to use the ranking to select time series that pass through the filter.
NumTimeSeries int: How many time series to allow to pass through the filter.
RankingMethod string: ranking_method is applied to each time series independently to produce the value which will be used to compare the time series to other time series.

direction String: How to use the ranking to select time series that pass through the filter.
numTimeSeries Integer: How many time series to allow to pass through the filter.
rankingMethod String: ranking_method is applied to each time series independently to produce the value which will be used to compare the time series to other time series.

direction string: How to use the ranking to select time series that pass through the filter.
numTimeSeries number: How many time series to allow to pass through the filter.
rankingMethod string: ranking_method is applied to each time series independently to produce the value which will be used to compare the time series to other time series.

direction str: How to use the ranking to select time series that pass through the filter.
num_time_series int: How many time series to allow to pass through the filter.
ranking_method str: ranking_method is applied to each time series independently to produce the value which will be used to compare the time series to other time series.

direction String: How to use the ranking to select time series that pass through the filter.
numTimeSeries Number: How many time series to allow to pass through the filter.
rankingMethod String: ranking_method is applied to each time series independently to produce the value which will be used to compare the time series to other time series.

RatioPart

Filter string: The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.
Aggregation Pulumi.GoogleNative.Monitoring.V1.Inputs.Aggregation: By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

Filter string: The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.
Aggregation Aggregation: By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

filter String: The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.
aggregation Aggregation: By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

filter string: The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.
aggregation Aggregation: By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

filter str: The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.
aggregation Aggregation: By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

filter String: The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.
aggregation Property Map: By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

RatioPartResponse

Aggregation Pulumi.GoogleNative.Monitoring.V1.Inputs.AggregationResponse: By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.
Filter string: The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

Aggregation AggregationResponse: By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.
Filter string: The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

aggregation AggregationResponse: By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.
filter String: The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

aggregation AggregationResponse: By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.
filter string: The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

aggregation AggregationResponse: By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.
filter str: The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

aggregation Property Map: By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.
filter String: The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

Row

Weight string: The relative weight of this row. The row weight is used to adjust the height of rows on the screen (relative to peers). Greater the weight, greater the height of the row on the screen. If omitted, a value of 1 is used while rendering.
Widgets List<Pulumi.GoogleNative.Monitoring.V1.Inputs.Widget>: The display widgets arranged horizontally in this row.

Weight string: The relative weight of this row. The row weight is used to adjust the height of rows on the screen (relative to peers). Greater the weight, greater the height of the row on the screen. If omitted, a value of 1 is used while rendering.
Widgets []Widget: The display widgets arranged horizontally in this row.

weight String: The relative weight of this row. The row weight is used to adjust the height of rows on the screen (relative to peers). Greater the weight, greater the height of the row on the screen. If omitted, a value of 1 is used while rendering.
widgets List<Widget>: The display widgets arranged horizontally in this row.

weight string: The relative weight of this row. The row weight is used to adjust the height of rows on the screen (relative to peers). Greater the weight, greater the height of the row on the screen. If omitted, a value of 1 is used while rendering.
widgets Widget[]: The display widgets arranged horizontally in this row.

weight str: The relative weight of this row. The row weight is used to adjust the height of rows on the screen (relative to peers). Greater the weight, greater the height of the row on the screen. If omitted, a value of 1 is used while rendering.
widgets Sequence[Widget]: The display widgets arranged horizontally in this row.

weight String: The relative weight of this row. The row weight is used to adjust the height of rows on the screen (relative to peers). Greater the weight, greater the height of the row on the screen. If omitted, a value of 1 is used while rendering.
widgets List<Property Map>: The display widgets arranged horizontally in this row.

RowLayout

Rows List<Pulumi.GoogleNative.Monitoring.V1.Inputs.Row>: The rows of content to display.

Rows []Row: The rows of content to display.

rows List<Row>: The rows of content to display.

rows Row[]: The rows of content to display.

rows Sequence[Row]: The rows of content to display.

rows List<Property Map>: The rows of content to display.

RowLayoutResponse

Rows List<Pulumi.GoogleNative.Monitoring.V1.Inputs.RowResponse>: The rows of content to display.

Rows []RowResponse: The rows of content to display.

rows List<RowResponse>: The rows of content to display.

rows RowResponse[]: The rows of content to display.

rows Sequence[RowResponse]: The rows of content to display.

rows List<Property Map>: The rows of content to display.

RowResponse

Weight string: The relative weight of this row. The row weight is used to adjust the height of rows on the screen (relative to peers). Greater the weight, greater the height of the row on the screen. If omitted, a value of 1 is used while rendering.
Widgets List<Pulumi.GoogleNative.Monitoring.V1.Inputs.WidgetResponse>: The display widgets arranged horizontally in this row.

Weight string: The relative weight of this row. The row weight is used to adjust the height of rows on the screen (relative to peers). Greater the weight, greater the height of the row on the screen. If omitted, a value of 1 is used while rendering.
Widgets []WidgetResponse: The display widgets arranged horizontally in this row.

weight String: The relative weight of this row. The row weight is used to adjust the height of rows on the screen (relative to peers). Greater the weight, greater the height of the row on the screen. If omitted, a value of 1 is used while rendering.
widgets List<WidgetResponse>: The display widgets arranged horizontally in this row.

weight string: The relative weight of this row. The row weight is used to adjust the height of rows on the screen (relative to peers). Greater the weight, greater the height of the row on the screen. If omitted, a value of 1 is used while rendering.
widgets WidgetResponse[]: The display widgets arranged horizontally in this row.

weight str: The relative weight of this row. The row weight is used to adjust the height of rows on the screen (relative to peers). Greater the weight, greater the height of the row on the screen. If omitted, a value of 1 is used while rendering.
widgets Sequence[WidgetResponse]: The display widgets arranged horizontally in this row.

weight String: The relative weight of this row. The row weight is used to adjust the height of rows on the screen (relative to peers). Greater the weight, greater the height of the row on the screen. If omitted, a value of 1 is used while rendering.
widgets List<Property Map>: The display widgets arranged horizontally in this row.

Scorecard

TimeSeriesQuery Pulumi.GoogleNative.Monitoring.V1.Inputs.TimeSeriesQuery: Fields for querying time series data from the Stackdriver metrics API.
GaugeView Pulumi.GoogleNative.Monitoring.V1.Inputs.GaugeView: Will cause the scorecard to show a gauge chart.
SparkChartView Pulumi.GoogleNative.Monitoring.V1.Inputs.SparkChartView: Will cause the scorecard to show a spark chart.
Thresholds List<Pulumi.GoogleNative.Monitoring.V1.Inputs.Threshold>: The thresholds used to determine the state of the scorecard given the time series' current value. For an actual value x, the scorecard is in a danger state if x is less than or equal to a danger threshold that triggers below, or greater than or equal to a danger threshold that triggers above. Similarly, if x is above/below a warning threshold that triggers above/below, then the scorecard is in a warning state - unless x also puts it in a danger state. (Danger trumps warning.)As an example, consider a scorecard with the following four thresholds: { value: 90, category: 'DANGER', trigger: 'ABOVE', }, { value: 70, category: 'WARNING', trigger: 'ABOVE', }, { value: 10, category: 'DANGER', trigger: 'BELOW', }, { value: 20, category: 'WARNING', trigger: 'BELOW', } Then: values less than or equal to 10 would put the scorecard in a DANGER state, values greater than 10 but less than or equal to 20 a WARNING state, values strictly between 20 and 70 an OK state, values greater than or equal to 70 but less than 90 a WARNING state, and values greater than or equal to 90 a DANGER state.

TimeSeriesQuery TimeSeriesQuery: Fields for querying time series data from the Stackdriver metrics API.
GaugeView GaugeView: Will cause the scorecard to show a gauge chart.
SparkChartView SparkChartView: Will cause the scorecard to show a spark chart.
Thresholds []Threshold: The thresholds used to determine the state of the scorecard given the time series' current value. For an actual value x, the scorecard is in a danger state if x is less than or equal to a danger threshold that triggers below, or greater than or equal to a danger threshold that triggers above. Similarly, if x is above/below a warning threshold that triggers above/below, then the scorecard is in a warning state - unless x also puts it in a danger state. (Danger trumps warning.)As an example, consider a scorecard with the following four thresholds: { value: 90, category: 'DANGER', trigger: 'ABOVE', }, { value: 70, category: 'WARNING', trigger: 'ABOVE', }, { value: 10, category: 'DANGER', trigger: 'BELOW', }, { value: 20, category: 'WARNING', trigger: 'BELOW', } Then: values less than or equal to 10 would put the scorecard in a DANGER state, values greater than 10 but less than or equal to 20 a WARNING state, values strictly between 20 and 70 an OK state, values greater than or equal to 70 but less than 90 a WARNING state, and values greater than or equal to 90 a DANGER state.

timeSeriesQuery TimeSeriesQuery: Fields for querying time series data from the Stackdriver metrics API.
gaugeView GaugeView: Will cause the scorecard to show a gauge chart.
sparkChartView SparkChartView: Will cause the scorecard to show a spark chart.
thresholds List<Threshold>: The thresholds used to determine the state of the scorecard given the time series' current value. For an actual value x, the scorecard is in a danger state if x is less than or equal to a danger threshold that triggers below, or greater than or equal to a danger threshold that triggers above. Similarly, if x is above/below a warning threshold that triggers above/below, then the scorecard is in a warning state - unless x also puts it in a danger state. (Danger trumps warning.)As an example, consider a scorecard with the following four thresholds: { value: 90, category: 'DANGER', trigger: 'ABOVE', }, { value: 70, category: 'WARNING', trigger: 'ABOVE', }, { value: 10, category: 'DANGER', trigger: 'BELOW', }, { value: 20, category: 'WARNING', trigger: 'BELOW', } Then: values less than or equal to 10 would put the scorecard in a DANGER state, values greater than 10 but less than or equal to 20 a WARNING state, values strictly between 20 and 70 an OK state, values greater than or equal to 70 but less than 90 a WARNING state, and values greater than or equal to 90 a DANGER state.

timeSeriesQuery TimeSeriesQuery: Fields for querying time series data from the Stackdriver metrics API.
gaugeView GaugeView: Will cause the scorecard to show a gauge chart.
sparkChartView SparkChartView: Will cause the scorecard to show a spark chart.
thresholds Threshold[]: The thresholds used to determine the state of the scorecard given the time series' current value. For an actual value x, the scorecard is in a danger state if x is less than or equal to a danger threshold that triggers below, or greater than or equal to a danger threshold that triggers above. Similarly, if x is above/below a warning threshold that triggers above/below, then the scorecard is in a warning state - unless x also puts it in a danger state. (Danger trumps warning.)As an example, consider a scorecard with the following four thresholds: { value: 90, category: 'DANGER', trigger: 'ABOVE', }, { value: 70, category: 'WARNING', trigger: 'ABOVE', }, { value: 10, category: 'DANGER', trigger: 'BELOW', }, { value: 20, category: 'WARNING', trigger: 'BELOW', } Then: values less than or equal to 10 would put the scorecard in a DANGER state, values greater than 10 but less than or equal to 20 a WARNING state, values strictly between 20 and 70 an OK state, values greater than or equal to 70 but less than 90 a WARNING state, and values greater than or equal to 90 a DANGER state.

time_series_query TimeSeriesQuery: Fields for querying time series data from the Stackdriver metrics API.
gauge_view GaugeView: Will cause the scorecard to show a gauge chart.
spark_chart_view SparkChartView: Will cause the scorecard to show a spark chart.
thresholds Sequence[Threshold]: The thresholds used to determine the state of the scorecard given the time series' current value. For an actual value x, the scorecard is in a danger state if x is less than or equal to a danger threshold that triggers below, or greater than or equal to a danger threshold that triggers above. Similarly, if x is above/below a warning threshold that triggers above/below, then the scorecard is in a warning state - unless x also puts it in a danger state. (Danger trumps warning.)As an example, consider a scorecard with the following four thresholds: { value: 90, category: 'DANGER', trigger: 'ABOVE', }, { value: 70, category: 'WARNING', trigger: 'ABOVE', }, { value: 10, category: 'DANGER', trigger: 'BELOW', }, { value: 20, category: 'WARNING', trigger: 'BELOW', } Then: values less than or equal to 10 would put the scorecard in a DANGER state, values greater than 10 but less than or equal to 20 a WARNING state, values strictly between 20 and 70 an OK state, values greater than or equal to 70 but less than 90 a WARNING state, and values greater than or equal to 90 a DANGER state.

timeSeriesQuery Property Map: Fields for querying time series data from the Stackdriver metrics API.
gaugeView Property Map: Will cause the scorecard to show a gauge chart.
sparkChartView Property Map: Will cause the scorecard to show a spark chart.
thresholds List<Property Map>: The thresholds used to determine the state of the scorecard given the time series' current value. For an actual value x, the scorecard is in a danger state if x is less than or equal to a danger threshold that triggers below, or greater than or equal to a danger threshold that triggers above. Similarly, if x is above/below a warning threshold that triggers above/below, then the scorecard is in a warning state - unless x also puts it in a danger state. (Danger trumps warning.)As an example, consider a scorecard with the following four thresholds: { value: 90, category: 'DANGER', trigger: 'ABOVE', }, { value: 70, category: 'WARNING', trigger: 'ABOVE', }, { value: 10, category: 'DANGER', trigger: 'BELOW', }, { value: 20, category: 'WARNING', trigger: 'BELOW', } Then: values less than or equal to 10 would put the scorecard in a DANGER state, values greater than 10 but less than or equal to 20 a WARNING state, values strictly between 20 and 70 an OK state, values greater than or equal to 70 but less than 90 a WARNING state, and values greater than or equal to 90 a DANGER state.

ScorecardResponse

GaugeView Pulumi.GoogleNative.Monitoring.V1.Inputs.GaugeViewResponse: Will cause the scorecard to show a gauge chart.
SparkChartView Pulumi.GoogleNative.Monitoring.V1.Inputs.SparkChartViewResponse: Will cause the scorecard to show a spark chart.
Thresholds List<Pulumi.GoogleNative.Monitoring.V1.Inputs.ThresholdResponse>: The thresholds used to determine the state of the scorecard given the time series' current value. For an actual value x, the scorecard is in a danger state if x is less than or equal to a danger threshold that triggers below, or greater than or equal to a danger threshold that triggers above. Similarly, if x is above/below a warning threshold that triggers above/below, then the scorecard is in a warning state - unless x also puts it in a danger state. (Danger trumps warning.)As an example, consider a scorecard with the following four thresholds: { value: 90, category: 'DANGER', trigger: 'ABOVE', }, { value: 70, category: 'WARNING', trigger: 'ABOVE', }, { value: 10, category: 'DANGER', trigger: 'BELOW', }, { value: 20, category: 'WARNING', trigger: 'BELOW', } Then: values less than or equal to 10 would put the scorecard in a DANGER state, values greater than 10 but less than or equal to 20 a WARNING state, values strictly between 20 and 70 an OK state, values greater than or equal to 70 but less than 90 a WARNING state, and values greater than or equal to 90 a DANGER state.
TimeSeriesQuery Pulumi.GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryResponse: Fields for querying time series data from the Stackdriver metrics API.

GaugeView GaugeViewResponse: Will cause the scorecard to show a gauge chart.
SparkChartView SparkChartViewResponse: Will cause the scorecard to show a spark chart.
Thresholds []ThresholdResponse: The thresholds used to determine the state of the scorecard given the time series' current value. For an actual value x, the scorecard is in a danger state if x is less than or equal to a danger threshold that triggers below, or greater than or equal to a danger threshold that triggers above. Similarly, if x is above/below a warning threshold that triggers above/below, then the scorecard is in a warning state - unless x also puts it in a danger state. (Danger trumps warning.)As an example, consider a scorecard with the following four thresholds: { value: 90, category: 'DANGER', trigger: 'ABOVE', }, { value: 70, category: 'WARNING', trigger: 'ABOVE', }, { value: 10, category: 'DANGER', trigger: 'BELOW', }, { value: 20, category: 'WARNING', trigger: 'BELOW', } Then: values less than or equal to 10 would put the scorecard in a DANGER state, values greater than 10 but less than or equal to 20 a WARNING state, values strictly between 20 and 70 an OK state, values greater than or equal to 70 but less than 90 a WARNING state, and values greater than or equal to 90 a DANGER state.
TimeSeriesQuery TimeSeriesQueryResponse: Fields for querying time series data from the Stackdriver metrics API.

gaugeView GaugeViewResponse: Will cause the scorecard to show a gauge chart.
sparkChartView SparkChartViewResponse: Will cause the scorecard to show a spark chart.
thresholds List<ThresholdResponse>: The thresholds used to determine the state of the scorecard given the time series' current value. For an actual value x, the scorecard is in a danger state if x is less than or equal to a danger threshold that triggers below, or greater than or equal to a danger threshold that triggers above. Similarly, if x is above/below a warning threshold that triggers above/below, then the scorecard is in a warning state - unless x also puts it in a danger state. (Danger trumps warning.)As an example, consider a scorecard with the following four thresholds: { value: 90, category: 'DANGER', trigger: 'ABOVE', }, { value: 70, category: 'WARNING', trigger: 'ABOVE', }, { value: 10, category: 'DANGER', trigger: 'BELOW', }, { value: 20, category: 'WARNING', trigger: 'BELOW', } Then: values less than or equal to 10 would put the scorecard in a DANGER state, values greater than 10 but less than or equal to 20 a WARNING state, values strictly between 20 and 70 an OK state, values greater than or equal to 70 but less than 90 a WARNING state, and values greater than or equal to 90 a DANGER state.
timeSeriesQuery TimeSeriesQueryResponse: Fields for querying time series data from the Stackdriver metrics API.

gaugeView GaugeViewResponse: Will cause the scorecard to show a gauge chart.
sparkChartView SparkChartViewResponse: Will cause the scorecard to show a spark chart.
thresholds ThresholdResponse[]: The thresholds used to determine the state of the scorecard given the time series' current value. For an actual value x, the scorecard is in a danger state if x is less than or equal to a danger threshold that triggers below, or greater than or equal to a danger threshold that triggers above. Similarly, if x is above/below a warning threshold that triggers above/below, then the scorecard is in a warning state - unless x also puts it in a danger state. (Danger trumps warning.)As an example, consider a scorecard with the following four thresholds: { value: 90, category: 'DANGER', trigger: 'ABOVE', }, { value: 70, category: 'WARNING', trigger: 'ABOVE', }, { value: 10, category: 'DANGER', trigger: 'BELOW', }, { value: 20, category: 'WARNING', trigger: 'BELOW', } Then: values less than or equal to 10 would put the scorecard in a DANGER state, values greater than 10 but less than or equal to 20 a WARNING state, values strictly between 20 and 70 an OK state, values greater than or equal to 70 but less than 90 a WARNING state, and values greater than or equal to 90 a DANGER state.
timeSeriesQuery TimeSeriesQueryResponse: Fields for querying time series data from the Stackdriver metrics API.

gauge_view GaugeViewResponse: Will cause the scorecard to show a gauge chart.
spark_chart_view SparkChartViewResponse: Will cause the scorecard to show a spark chart.
thresholds Sequence[ThresholdResponse]: The thresholds used to determine the state of the scorecard given the time series' current value. For an actual value x, the scorecard is in a danger state if x is less than or equal to a danger threshold that triggers below, or greater than or equal to a danger threshold that triggers above. Similarly, if x is above/below a warning threshold that triggers above/below, then the scorecard is in a warning state - unless x also puts it in a danger state. (Danger trumps warning.)As an example, consider a scorecard with the following four thresholds: { value: 90, category: 'DANGER', trigger: 'ABOVE', }, { value: 70, category: 'WARNING', trigger: 'ABOVE', }, { value: 10, category: 'DANGER', trigger: 'BELOW', }, { value: 20, category: 'WARNING', trigger: 'BELOW', } Then: values less than or equal to 10 would put the scorecard in a DANGER state, values greater than 10 but less than or equal to 20 a WARNING state, values strictly between 20 and 70 an OK state, values greater than or equal to 70 but less than 90 a WARNING state, and values greater than or equal to 90 a DANGER state.
time_series_query TimeSeriesQueryResponse: Fields for querying time series data from the Stackdriver metrics API.

gaugeView Property Map: Will cause the scorecard to show a gauge chart.
sparkChartView Property Map: Will cause the scorecard to show a spark chart.
thresholds List<Property Map>: The thresholds used to determine the state of the scorecard given the time series' current value. For an actual value x, the scorecard is in a danger state if x is less than or equal to a danger threshold that triggers below, or greater than or equal to a danger threshold that triggers above. Similarly, if x is above/below a warning threshold that triggers above/below, then the scorecard is in a warning state - unless x also puts it in a danger state. (Danger trumps warning.)As an example, consider a scorecard with the following four thresholds: { value: 90, category: 'DANGER', trigger: 'ABOVE', }, { value: 70, category: 'WARNING', trigger: 'ABOVE', }, { value: 10, category: 'DANGER', trigger: 'BELOW', }, { value: 20, category: 'WARNING', trigger: 'BELOW', } Then: values less than or equal to 10 would put the scorecard in a DANGER state, values greater than 10 but less than or equal to 20 a WARNING state, values strictly between 20 and 70 an OK state, values greater than or equal to 70 but less than 90 a WARNING state, and values greater than or equal to 90 a DANGER state.
timeSeriesQuery Property Map: Fields for querying time series data from the Stackdriver metrics API.

SparkChartView

SparkChartType Pulumi.GoogleNative.Monitoring.V1.SparkChartViewSparkChartType: The type of sparkchart to show in this chartView.
MinAlignmentPeriod string: The lower bound on data point frequency in the chart implemented by specifying the minimum alignment period to use in a time series query. For example, if the data is published once every 10 minutes it would not make sense to fetch and align data at one minute intervals. This field is optional and exists only as a hint.

SparkChartType SparkChartViewSparkChartType: The type of sparkchart to show in this chartView.
MinAlignmentPeriod string: The lower bound on data point frequency in the chart implemented by specifying the minimum alignment period to use in a time series query. For example, if the data is published once every 10 minutes it would not make sense to fetch and align data at one minute intervals. This field is optional and exists only as a hint.

sparkChartType SparkChartViewSparkChartType: The type of sparkchart to show in this chartView.
minAlignmentPeriod String: The lower bound on data point frequency in the chart implemented by specifying the minimum alignment period to use in a time series query. For example, if the data is published once every 10 minutes it would not make sense to fetch and align data at one minute intervals. This field is optional and exists only as a hint.

sparkChartType SparkChartViewSparkChartType: The type of sparkchart to show in this chartView.
minAlignmentPeriod string: The lower bound on data point frequency in the chart implemented by specifying the minimum alignment period to use in a time series query. For example, if the data is published once every 10 minutes it would not make sense to fetch and align data at one minute intervals. This field is optional and exists only as a hint.

spark_chart_type SparkChartViewSparkChartType: The type of sparkchart to show in this chartView.
min_alignment_period str: The lower bound on data point frequency in the chart implemented by specifying the minimum alignment period to use in a time series query. For example, if the data is published once every 10 minutes it would not make sense to fetch and align data at one minute intervals. This field is optional and exists only as a hint.

sparkChartType "SPARK_CHART_TYPE_UNSPECIFIED" | "SPARK_LINE" | "SPARK_BAR": The type of sparkchart to show in this chartView.
minAlignmentPeriod String: The lower bound on data point frequency in the chart implemented by specifying the minimum alignment period to use in a time series query. For example, if the data is published once every 10 minutes it would not make sense to fetch and align data at one minute intervals. This field is optional and exists only as a hint.

SparkChartViewResponse

MinAlignmentPeriod string: The lower bound on data point frequency in the chart implemented by specifying the minimum alignment period to use in a time series query. For example, if the data is published once every 10 minutes it would not make sense to fetch and align data at one minute intervals. This field is optional and exists only as a hint.
SparkChartType string: The type of sparkchart to show in this chartView.

MinAlignmentPeriod string: The lower bound on data point frequency in the chart implemented by specifying the minimum alignment period to use in a time series query. For example, if the data is published once every 10 minutes it would not make sense to fetch and align data at one minute intervals. This field is optional and exists only as a hint.
SparkChartType string: The type of sparkchart to show in this chartView.

minAlignmentPeriod String: The lower bound on data point frequency in the chart implemented by specifying the minimum alignment period to use in a time series query. For example, if the data is published once every 10 minutes it would not make sense to fetch and align data at one minute intervals. This field is optional and exists only as a hint.
sparkChartType String: The type of sparkchart to show in this chartView.

minAlignmentPeriod string: The lower bound on data point frequency in the chart implemented by specifying the minimum alignment period to use in a time series query. For example, if the data is published once every 10 minutes it would not make sense to fetch and align data at one minute intervals. This field is optional and exists only as a hint.
sparkChartType string: The type of sparkchart to show in this chartView.

min_alignment_period str: The lower bound on data point frequency in the chart implemented by specifying the minimum alignment period to use in a time series query. For example, if the data is published once every 10 minutes it would not make sense to fetch and align data at one minute intervals. This field is optional and exists only as a hint.
spark_chart_type str: The type of sparkchart to show in this chartView.

minAlignmentPeriod String: The lower bound on data point frequency in the chart implemented by specifying the minimum alignment period to use in a time series query. For example, if the data is published once every 10 minutes it would not make sense to fetch and align data at one minute intervals. This field is optional and exists only as a hint.
sparkChartType String: The type of sparkchart to show in this chartView.

SparkChartViewSparkChartType

SparkChartTypeUnspecified: SPARK_CHART_TYPE_UNSPECIFIED
Not allowed in well-formed requests.
SparkLine: SPARK_LINE
The sparkline will be rendered as a small line chart.
SparkBar: SPARK_BAR
The sparkbar will be rendered as a small bar chart.

SparkChartViewSparkChartTypeSparkChartTypeUnspecified: SPARK_CHART_TYPE_UNSPECIFIED
Not allowed in well-formed requests.
SparkChartViewSparkChartTypeSparkLine: SPARK_LINE
The sparkline will be rendered as a small line chart.
SparkChartViewSparkChartTypeSparkBar: SPARK_BAR
The sparkbar will be rendered as a small bar chart.

SparkChartTypeUnspecified: SPARK_CHART_TYPE_UNSPECIFIED
Not allowed in well-formed requests.
SparkLine: SPARK_LINE
The sparkline will be rendered as a small line chart.
SparkBar: SPARK_BAR
The sparkbar will be rendered as a small bar chart.

SparkChartTypeUnspecified: SPARK_CHART_TYPE_UNSPECIFIED
Not allowed in well-formed requests.
SparkLine: SPARK_LINE
The sparkline will be rendered as a small line chart.
SparkBar: SPARK_BAR
The sparkbar will be rendered as a small bar chart.

SPARK_CHART_TYPE_UNSPECIFIED: SPARK_CHART_TYPE_UNSPECIFIED
Not allowed in well-formed requests.
SPARK_LINE: SPARK_LINE
The sparkline will be rendered as a small line chart.
SPARK_BAR: SPARK_BAR
The sparkbar will be rendered as a small bar chart.

"SPARK_CHART_TYPE_UNSPECIFIED": SPARK_CHART_TYPE_UNSPECIFIED
Not allowed in well-formed requests.
"SPARK_LINE": SPARK_LINE
The sparkline will be rendered as a small line chart.
"SPARK_BAR": SPARK_BAR
The sparkbar will be rendered as a small bar chart.

StatisticalTimeSeriesFilter

NumTimeSeries int: How many time series to output.
RankingMethod Pulumi.GoogleNative.Monitoring.V1.StatisticalTimeSeriesFilterRankingMethod: rankingMethod is applied to a set of time series, and then the produced value for each individual time series is used to compare a given time series to others. These are methods that cannot be applied stream-by-stream, but rather require the full context of a request to evaluate time series.

NumTimeSeries int: How many time series to output.
RankingMethod StatisticalTimeSeriesFilterRankingMethod: rankingMethod is applied to a set of time series, and then the produced value for each individual time series is used to compare a given time series to others. These are methods that cannot be applied stream-by-stream, but rather require the full context of a request to evaluate time series.

numTimeSeries Integer: How many time series to output.
rankingMethod StatisticalTimeSeriesFilterRankingMethod: rankingMethod is applied to a set of time series, and then the produced value for each individual time series is used to compare a given time series to others. These are methods that cannot be applied stream-by-stream, but rather require the full context of a request to evaluate time series.

numTimeSeries number: How many time series to output.
rankingMethod StatisticalTimeSeriesFilterRankingMethod: rankingMethod is applied to a set of time series, and then the produced value for each individual time series is used to compare a given time series to others. These are methods that cannot be applied stream-by-stream, but rather require the full context of a request to evaluate time series.

num_time_series int: How many time series to output.
ranking_method StatisticalTimeSeriesFilterRankingMethod: rankingMethod is applied to a set of time series, and then the produced value for each individual time series is used to compare a given time series to others. These are methods that cannot be applied stream-by-stream, but rather require the full context of a request to evaluate time series.

numTimeSeries Number: How many time series to output.
rankingMethod "METHOD_UNSPECIFIED" | "METHOD_CLUSTER_OUTLIER": rankingMethod is applied to a set of time series, and then the produced value for each individual time series is used to compare a given time series to others. These are methods that cannot be applied stream-by-stream, but rather require the full context of a request to evaluate time series.

StatisticalTimeSeriesFilterRankingMethod

MethodUnspecified: METHOD_UNSPECIFIED
Not allowed in well-formed requests.
MethodClusterOutlier: METHOD_CLUSTER_OUTLIER
Compute the outlier score of each stream.

StatisticalTimeSeriesFilterRankingMethodMethodUnspecified: METHOD_UNSPECIFIED
Not allowed in well-formed requests.
StatisticalTimeSeriesFilterRankingMethodMethodClusterOutlier: METHOD_CLUSTER_OUTLIER
Compute the outlier score of each stream.

MethodUnspecified: METHOD_UNSPECIFIED
Not allowed in well-formed requests.
MethodClusterOutlier: METHOD_CLUSTER_OUTLIER
Compute the outlier score of each stream.

MethodUnspecified: METHOD_UNSPECIFIED
Not allowed in well-formed requests.
MethodClusterOutlier: METHOD_CLUSTER_OUTLIER
Compute the outlier score of each stream.

METHOD_UNSPECIFIED: METHOD_UNSPECIFIED
Not allowed in well-formed requests.
METHOD_CLUSTER_OUTLIER: METHOD_CLUSTER_OUTLIER
Compute the outlier score of each stream.

"METHOD_UNSPECIFIED": METHOD_UNSPECIFIED
Not allowed in well-formed requests.
"METHOD_CLUSTER_OUTLIER": METHOD_CLUSTER_OUTLIER
Compute the outlier score of each stream.

StatisticalTimeSeriesFilterResponse

NumTimeSeries int: How many time series to output.
RankingMethod string: rankingMethod is applied to a set of time series, and then the produced value for each individual time series is used to compare a given time series to others. These are methods that cannot be applied stream-by-stream, but rather require the full context of a request to evaluate time series.

NumTimeSeries int: How many time series to output.
RankingMethod string: rankingMethod is applied to a set of time series, and then the produced value for each individual time series is used to compare a given time series to others. These are methods that cannot be applied stream-by-stream, but rather require the full context of a request to evaluate time series.

numTimeSeries Integer: How many time series to output.
rankingMethod String: rankingMethod is applied to a set of time series, and then the produced value for each individual time series is used to compare a given time series to others. These are methods that cannot be applied stream-by-stream, but rather require the full context of a request to evaluate time series.

numTimeSeries number: How many time series to output.
rankingMethod string: rankingMethod is applied to a set of time series, and then the produced value for each individual time series is used to compare a given time series to others. These are methods that cannot be applied stream-by-stream, but rather require the full context of a request to evaluate time series.

num_time_series int: How many time series to output.
ranking_method str: rankingMethod is applied to a set of time series, and then the produced value for each individual time series is used to compare a given time series to others. These are methods that cannot be applied stream-by-stream, but rather require the full context of a request to evaluate time series.

numTimeSeries Number: How many time series to output.
rankingMethod String: rankingMethod is applied to a set of time series, and then the produced value for each individual time series is used to compare a given time series to others. These are methods that cannot be applied stream-by-stream, but rather require the full context of a request to evaluate time series.

TableDataSet

TimeSeriesQuery Pulumi.GoogleNative.Monitoring.V1.Inputs.TimeSeriesQuery: Fields for querying time series data from the Stackdriver metrics API.
MinAlignmentPeriod string: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
TableDisplayOptions Pulumi.GoogleNative.Monitoring.V1.Inputs.TableDisplayOptions: Optional. Table display options for configuring how the table is rendered.
TableTemplate string: Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}."

TimeSeriesQuery TimeSeriesQuery: Fields for querying time series data from the Stackdriver metrics API.
MinAlignmentPeriod string: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
TableDisplayOptions TableDisplayOptions: Optional. Table display options for configuring how the table is rendered.
TableTemplate string: Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}."

timeSeriesQuery TimeSeriesQuery: Fields for querying time series data from the Stackdriver metrics API.
minAlignmentPeriod String: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
tableDisplayOptions TableDisplayOptions: Optional. Table display options for configuring how the table is rendered.
tableTemplate String: Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}."

timeSeriesQuery TimeSeriesQuery: Fields for querying time series data from the Stackdriver metrics API.
minAlignmentPeriod string: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
tableDisplayOptions TableDisplayOptions: Optional. Table display options for configuring how the table is rendered.
tableTemplate string: Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}."

time_series_query TimeSeriesQuery: Fields for querying time series data from the Stackdriver metrics API.
min_alignment_period str: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
table_display_options TableDisplayOptions: Optional. Table display options for configuring how the table is rendered.
table_template str: Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}."

timeSeriesQuery Property Map: Fields for querying time series data from the Stackdriver metrics API.
minAlignmentPeriod String: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
tableDisplayOptions Property Map: Optional. Table display options for configuring how the table is rendered.
tableTemplate String: Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}."

TableDataSetResponse

MinAlignmentPeriod string: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
TableDisplayOptions Pulumi.GoogleNative.Monitoring.V1.Inputs.TableDisplayOptionsResponse: Optional. Table display options for configuring how the table is rendered.
TableTemplate string: Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}."
TimeSeriesQuery Pulumi.GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryResponse: Fields for querying time series data from the Stackdriver metrics API.

MinAlignmentPeriod string: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
TableDisplayOptions TableDisplayOptionsResponse: Optional. Table display options for configuring how the table is rendered.
TableTemplate string: Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}."
TimeSeriesQuery TimeSeriesQueryResponse: Fields for querying time series data from the Stackdriver metrics API.

minAlignmentPeriod String: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
tableDisplayOptions TableDisplayOptionsResponse: Optional. Table display options for configuring how the table is rendered.
tableTemplate String: Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}."
timeSeriesQuery TimeSeriesQueryResponse: Fields for querying time series data from the Stackdriver metrics API.

minAlignmentPeriod string: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
tableDisplayOptions TableDisplayOptionsResponse: Optional. Table display options for configuring how the table is rendered.
tableTemplate string: Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}."
timeSeriesQuery TimeSeriesQueryResponse: Fields for querying time series data from the Stackdriver metrics API.

min_alignment_period str: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
table_display_options TableDisplayOptionsResponse: Optional. Table display options for configuring how the table is rendered.
table_template str: Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}."
time_series_query TimeSeriesQueryResponse: Fields for querying time series data from the Stackdriver metrics API.

minAlignmentPeriod String: Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.
tableDisplayOptions Property Map: Optional. Table display options for configuring how the table is rendered.
tableTemplate String: Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}."
timeSeriesQuery Property Map: Fields for querying time series data from the Stackdriver metrics API.

TableDisplayOptions

ShownColumns List<string>: Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings

ShownColumns []string: Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings

shownColumns List<String>: Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings

shownColumns string[]: Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings

shown_columns Sequence[str]: Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings

shownColumns List<String>: Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings

TableDisplayOptionsResponse

ShownColumns List<string>: Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings

ShownColumns []string: Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings

shownColumns List<String>: Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings

shownColumns string[]: Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings

shown_columns Sequence[str]: Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings

shownColumns List<String>: Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings

Text

Content string: The text content to be displayed.
Format Pulumi.GoogleNative.Monitoring.V1.TextFormat: How the text content is formatted.

Content string: The text content to be displayed.
Format TextFormat: How the text content is formatted.

content String: The text content to be displayed.
format TextFormat: How the text content is formatted.

content string: The text content to be displayed.
format TextFormat: How the text content is formatted.

content str: The text content to be displayed.
format TextFormat: How the text content is formatted.

content String: The text content to be displayed.
format "FORMAT_UNSPECIFIED" | "MARKDOWN" | "RAW": How the text content is formatted.

TextFormat

FormatUnspecified: FORMAT_UNSPECIFIED
Format is unspecified. Defaults to MARKDOWN.
Markdown: MARKDOWN
The text contains Markdown formatting.
Raw: RAW
The text contains no special formatting.

TextFormatFormatUnspecified: FORMAT_UNSPECIFIED
Format is unspecified. Defaults to MARKDOWN.
TextFormatMarkdown: MARKDOWN
The text contains Markdown formatting.
TextFormatRaw: RAW
The text contains no special formatting.

FormatUnspecified: FORMAT_UNSPECIFIED
Format is unspecified. Defaults to MARKDOWN.
Markdown: MARKDOWN
The text contains Markdown formatting.
Raw: RAW
The text contains no special formatting.

FormatUnspecified: FORMAT_UNSPECIFIED
Format is unspecified. Defaults to MARKDOWN.
Markdown: MARKDOWN
The text contains Markdown formatting.
Raw: RAW
The text contains no special formatting.

FORMAT_UNSPECIFIED: FORMAT_UNSPECIFIED
Format is unspecified. Defaults to MARKDOWN.
MARKDOWN: MARKDOWN
The text contains Markdown formatting.
RAW: RAW
The text contains no special formatting.

"FORMAT_UNSPECIFIED": FORMAT_UNSPECIFIED
Format is unspecified. Defaults to MARKDOWN.
"MARKDOWN": MARKDOWN
The text contains Markdown formatting.
"RAW": RAW
The text contains no special formatting.

TextResponse

Content string: The text content to be displayed.
Format string: How the text content is formatted.

Content string: The text content to be displayed.
Format string: How the text content is formatted.

content String: The text content to be displayed.
format String: How the text content is formatted.

content string: The text content to be displayed.
format string: How the text content is formatted.

content str: The text content to be displayed.
format str: How the text content is formatted.

content String: The text content to be displayed.
format String: How the text content is formatted.

Threshold

Color Pulumi.GoogleNative.Monitoring.V1.ThresholdColor: The state color for this threshold. Color is not allowed in a XyChart.
Direction Pulumi.GoogleNative.Monitoring.V1.ThresholdDirection: The direction for the current threshold. Direction is not allowed in a XyChart.
Label string: A label for the threshold.
TargetAxis Pulumi.GoogleNative.Monitoring.V1.ThresholdTargetAxis: The target axis to use for plotting the threshold. Target axis is not allowed in a Scorecard.
Value double: The value of the threshold. The value should be defined in the native scale of the metric.

Color ThresholdColor: The state color for this threshold. Color is not allowed in a XyChart.
Direction ThresholdDirection: The direction for the current threshold. Direction is not allowed in a XyChart.
Label string: A label for the threshold.
TargetAxis ThresholdTargetAxis: The target axis to use for plotting the threshold. Target axis is not allowed in a Scorecard.
Value float64: The value of the threshold. The value should be defined in the native scale of the metric.

color ThresholdColor: The state color for this threshold. Color is not allowed in a XyChart.
direction ThresholdDirection: The direction for the current threshold. Direction is not allowed in a XyChart.
label String: A label for the threshold.
targetAxis ThresholdTargetAxis: The target axis to use for plotting the threshold. Target axis is not allowed in a Scorecard.
value Double: The value of the threshold. The value should be defined in the native scale of the metric.

color ThresholdColor: The state color for this threshold. Color is not allowed in a XyChart.
direction ThresholdDirection: The direction for the current threshold. Direction is not allowed in a XyChart.
label string: A label for the threshold.
targetAxis ThresholdTargetAxis: The target axis to use for plotting the threshold. Target axis is not allowed in a Scorecard.
value number: The value of the threshold. The value should be defined in the native scale of the metric.

color ThresholdColor: The state color for this threshold. Color is not allowed in a XyChart.
direction ThresholdDirection: The direction for the current threshold. Direction is not allowed in a XyChart.
label str: A label for the threshold.
target_axis ThresholdTargetAxis: The target axis to use for plotting the threshold. Target axis is not allowed in a Scorecard.
value float: The value of the threshold. The value should be defined in the native scale of the metric.

color "COLOR_UNSPECIFIED" | "YELLOW" | "RED": The state color for this threshold. Color is not allowed in a XyChart.
direction "DIRECTION_UNSPECIFIED" | "ABOVE" | "BELOW": The direction for the current threshold. Direction is not allowed in a XyChart.
label String: A label for the threshold.
targetAxis "TARGET_AXIS_UNSPECIFIED" | "Y1" | "Y2": The target axis to use for plotting the threshold. Target axis is not allowed in a Scorecard.
value Number: The value of the threshold. The value should be defined in the native scale of the metric.

ThresholdColor

ColorUnspecified: COLOR_UNSPECIFIED
Color is unspecified. Not allowed in well-formed requests.
Yellow: YELLOW
Crossing the threshold is "concerning" behavior.
Red: RED
Crossing the threshold is "emergency" behavior.

ThresholdColorColorUnspecified: COLOR_UNSPECIFIED
Color is unspecified. Not allowed in well-formed requests.
ThresholdColorYellow: YELLOW
Crossing the threshold is "concerning" behavior.
ThresholdColorRed: RED
Crossing the threshold is "emergency" behavior.

ColorUnspecified: COLOR_UNSPECIFIED
Color is unspecified. Not allowed in well-formed requests.
Yellow: YELLOW
Crossing the threshold is "concerning" behavior.
Red: RED
Crossing the threshold is "emergency" behavior.

ColorUnspecified: COLOR_UNSPECIFIED
Color is unspecified. Not allowed in well-formed requests.
Yellow: YELLOW
Crossing the threshold is "concerning" behavior.
Red: RED
Crossing the threshold is "emergency" behavior.

COLOR_UNSPECIFIED: COLOR_UNSPECIFIED
Color is unspecified. Not allowed in well-formed requests.
YELLOW: YELLOW
Crossing the threshold is "concerning" behavior.
RED: RED
Crossing the threshold is "emergency" behavior.

"COLOR_UNSPECIFIED": COLOR_UNSPECIFIED
Color is unspecified. Not allowed in well-formed requests.
"YELLOW": YELLOW
Crossing the threshold is "concerning" behavior.
"RED": RED
Crossing the threshold is "emergency" behavior.

ThresholdDirection

DirectionUnspecified: DIRECTION_UNSPECIFIED
Not allowed in well-formed requests.
Above: ABOVE
The threshold will be considered crossed if the actual value is above the threshold value.
Below: BELOW
The threshold will be considered crossed if the actual value is below the threshold value.

ThresholdDirectionDirectionUnspecified: DIRECTION_UNSPECIFIED
Not allowed in well-formed requests.
ThresholdDirectionAbove: ABOVE
The threshold will be considered crossed if the actual value is above the threshold value.
ThresholdDirectionBelow: BELOW
The threshold will be considered crossed if the actual value is below the threshold value.

DirectionUnspecified: DIRECTION_UNSPECIFIED
Not allowed in well-formed requests.
Above: ABOVE
The threshold will be considered crossed if the actual value is above the threshold value.
Below: BELOW
The threshold will be considered crossed if the actual value is below the threshold value.

DirectionUnspecified: DIRECTION_UNSPECIFIED
Not allowed in well-formed requests.
Above: ABOVE
The threshold will be considered crossed if the actual value is above the threshold value.
Below: BELOW
The threshold will be considered crossed if the actual value is below the threshold value.

DIRECTION_UNSPECIFIED: DIRECTION_UNSPECIFIED
Not allowed in well-formed requests.
ABOVE: ABOVE
The threshold will be considered crossed if the actual value is above the threshold value.
BELOW: BELOW
The threshold will be considered crossed if the actual value is below the threshold value.

"DIRECTION_UNSPECIFIED": DIRECTION_UNSPECIFIED
Not allowed in well-formed requests.
"ABOVE": ABOVE
The threshold will be considered crossed if the actual value is above the threshold value.
"BELOW": BELOW
The threshold will be considered crossed if the actual value is below the threshold value.

ThresholdResponse

Color string: The state color for this threshold. Color is not allowed in a XyChart.
Direction string: The direction for the current threshold. Direction is not allowed in a XyChart.
Label string: A label for the threshold.
TargetAxis string: The target axis to use for plotting the threshold. Target axis is not allowed in a Scorecard.
Value double: The value of the threshold. The value should be defined in the native scale of the metric.

Color string: The state color for this threshold. Color is not allowed in a XyChart.
Direction string: The direction for the current threshold. Direction is not allowed in a XyChart.
Label string: A label for the threshold.
TargetAxis string: The target axis to use for plotting the threshold. Target axis is not allowed in a Scorecard.
Value float64: The value of the threshold. The value should be defined in the native scale of the metric.

color String: The state color for this threshold. Color is not allowed in a XyChart.
direction String: The direction for the current threshold. Direction is not allowed in a XyChart.
label String: A label for the threshold.
targetAxis String: The target axis to use for plotting the threshold. Target axis is not allowed in a Scorecard.
value Double: The value of the threshold. The value should be defined in the native scale of the metric.

color string: The state color for this threshold. Color is not allowed in a XyChart.
direction string: The direction for the current threshold. Direction is not allowed in a XyChart.
label string: A label for the threshold.
targetAxis string: The target axis to use for plotting the threshold. Target axis is not allowed in a Scorecard.
value number: The value of the threshold. The value should be defined in the native scale of the metric.

color str: The state color for this threshold. Color is not allowed in a XyChart.
direction str: The direction for the current threshold. Direction is not allowed in a XyChart.
label str: A label for the threshold.
target_axis str: The target axis to use for plotting the threshold. Target axis is not allowed in a Scorecard.
value float: The value of the threshold. The value should be defined in the native scale of the metric.

color String: The state color for this threshold. Color is not allowed in a XyChart.
direction String: The direction for the current threshold. Direction is not allowed in a XyChart.
label String: A label for the threshold.
targetAxis String: The target axis to use for plotting the threshold. Target axis is not allowed in a Scorecard.
value Number: The value of the threshold. The value should be defined in the native scale of the metric.

ThresholdTargetAxis

TargetAxisUnspecified: TARGET_AXIS_UNSPECIFIED
The target axis was not specified. Defaults to Y1.
Y1: Y1
The y_axis (the right axis of chart).
Y2: Y2
The y2_axis (the left axis of chart).

ThresholdTargetAxisTargetAxisUnspecified: TARGET_AXIS_UNSPECIFIED
The target axis was not specified. Defaults to Y1.
ThresholdTargetAxisY1: Y1
The y_axis (the right axis of chart).
ThresholdTargetAxisY2: Y2
The y2_axis (the left axis of chart).

TargetAxisUnspecified: TARGET_AXIS_UNSPECIFIED
The target axis was not specified. Defaults to Y1.
Y1: Y1
The y_axis (the right axis of chart).
Y2: Y2
The y2_axis (the left axis of chart).

TargetAxisUnspecified: TARGET_AXIS_UNSPECIFIED
The target axis was not specified. Defaults to Y1.
Y1: Y1
The y_axis (the right axis of chart).
Y2: Y2
The y2_axis (the left axis of chart).

TARGET_AXIS_UNSPECIFIED: TARGET_AXIS_UNSPECIFIED
The target axis was not specified. Defaults to Y1.
Y1: Y1
The y_axis (the right axis of chart).
Y2: Y2
The y2_axis (the left axis of chart).

"TARGET_AXIS_UNSPECIFIED": TARGET_AXIS_UNSPECIFIED
The target axis was not specified. Defaults to Y1.
"Y1": Y1
The y_axis (the right axis of chart).
"Y2": Y2
The y2_axis (the left axis of chart).

Tile

Height int: The height of the tile, measured in grid blocks. Tiles must have a minimum height of 1.
Widget Pulumi.GoogleNative.Monitoring.V1.Inputs.Widget: The informational widget contained in the tile. For example an XyChart.
Width int: The width of the tile, measured in grid blocks. Tiles must have a minimum width of 1.
XPos int: The zero-indexed position of the tile in grid blocks relative to the left edge of the grid. Tiles must be contained within the specified number of columns. x_pos cannot be negative.
YPos int: The zero-indexed position of the tile in grid blocks relative to the top edge of the grid. y_pos cannot be negative.

Height int: The height of the tile, measured in grid blocks. Tiles must have a minimum height of 1.
Widget Widget: The informational widget contained in the tile. For example an XyChart.
Width int: The width of the tile, measured in grid blocks. Tiles must have a minimum width of 1.
XPos int: The zero-indexed position of the tile in grid blocks relative to the left edge of the grid. Tiles must be contained within the specified number of columns. x_pos cannot be negative.
YPos int: The zero-indexed position of the tile in grid blocks relative to the top edge of the grid. y_pos cannot be negative.

height Integer: The height of the tile, measured in grid blocks. Tiles must have a minimum height of 1.
widget Widget: The informational widget contained in the tile. For example an XyChart.
width Integer: The width of the tile, measured in grid blocks. Tiles must have a minimum width of 1.
xPos Integer: The zero-indexed position of the tile in grid blocks relative to the left edge of the grid. Tiles must be contained within the specified number of columns. x_pos cannot be negative.
yPos Integer: The zero-indexed position of the tile in grid blocks relative to the top edge of the grid. y_pos cannot be negative.

height number: The height of the tile, measured in grid blocks. Tiles must have a minimum height of 1.
widget Widget: The informational widget contained in the tile. For example an XyChart.
width number: The width of the tile, measured in grid blocks. Tiles must have a minimum width of 1.
xPos number: The zero-indexed position of the tile in grid blocks relative to the left edge of the grid. Tiles must be contained within the specified number of columns. x_pos cannot be negative.
yPos number: The zero-indexed position of the tile in grid blocks relative to the top edge of the grid. y_pos cannot be negative.

height int: The height of the tile, measured in grid blocks. Tiles must have a minimum height of 1.
widget Widget: The informational widget contained in the tile. For example an XyChart.
width int: The width of the tile, measured in grid blocks. Tiles must have a minimum width of 1.
x_pos int: The zero-indexed position of the tile in grid blocks relative to the left edge of the grid. Tiles must be contained within the specified number of columns. x_pos cannot be negative.
y_pos int: The zero-indexed position of the tile in grid blocks relative to the top edge of the grid. y_pos cannot be negative.

height Number: The height of the tile, measured in grid blocks. Tiles must have a minimum height of 1.
widget Property Map: The informational widget contained in the tile. For example an XyChart.
width Number: The width of the tile, measured in grid blocks. Tiles must have a minimum width of 1.
xPos Number: The zero-indexed position of the tile in grid blocks relative to the left edge of the grid. Tiles must be contained within the specified number of columns. x_pos cannot be negative.
yPos Number: The zero-indexed position of the tile in grid blocks relative to the top edge of the grid. y_pos cannot be negative.

TileResponse

Height int: The height of the tile, measured in grid blocks. Tiles must have a minimum height of 1.
Widget Pulumi.GoogleNative.Monitoring.V1.Inputs.WidgetResponse: The informational widget contained in the tile. For example an XyChart.
Width int: The width of the tile, measured in grid blocks. Tiles must have a minimum width of 1.
XPos int: The zero-indexed position of the tile in grid blocks relative to the left edge of the grid. Tiles must be contained within the specified number of columns. x_pos cannot be negative.
YPos int: The zero-indexed position of the tile in grid blocks relative to the top edge of the grid. y_pos cannot be negative.

Height int: The height of the tile, measured in grid blocks. Tiles must have a minimum height of 1.
Widget WidgetResponse: The informational widget contained in the tile. For example an XyChart.
Width int: The width of the tile, measured in grid blocks. Tiles must have a minimum width of 1.
XPos int: The zero-indexed position of the tile in grid blocks relative to the left edge of the grid. Tiles must be contained within the specified number of columns. x_pos cannot be negative.
YPos int: The zero-indexed position of the tile in grid blocks relative to the top edge of the grid. y_pos cannot be negative.

height Integer: The height of the tile, measured in grid blocks. Tiles must have a minimum height of 1.
widget WidgetResponse: The informational widget contained in the tile. For example an XyChart.
width Integer: The width of the tile, measured in grid blocks. Tiles must have a minimum width of 1.
xPos Integer: The zero-indexed position of the tile in grid blocks relative to the left edge of the grid. Tiles must be contained within the specified number of columns. x_pos cannot be negative.
yPos Integer: The zero-indexed position of the tile in grid blocks relative to the top edge of the grid. y_pos cannot be negative.

height number: The height of the tile, measured in grid blocks. Tiles must have a minimum height of 1.
widget WidgetResponse: The informational widget contained in the tile. For example an XyChart.
width number: The width of the tile, measured in grid blocks. Tiles must have a minimum width of 1.
xPos number: The zero-indexed position of the tile in grid blocks relative to the left edge of the grid. Tiles must be contained within the specified number of columns. x_pos cannot be negative.
yPos number: The zero-indexed position of the tile in grid blocks relative to the top edge of the grid. y_pos cannot be negative.

height int: The height of the tile, measured in grid blocks. Tiles must have a minimum height of 1.
widget WidgetResponse: The informational widget contained in the tile. For example an XyChart.
width int: The width of the tile, measured in grid blocks. Tiles must have a minimum width of 1.
x_pos int: The zero-indexed position of the tile in grid blocks relative to the left edge of the grid. Tiles must be contained within the specified number of columns. x_pos cannot be negative.
y_pos int: The zero-indexed position of the tile in grid blocks relative to the top edge of the grid. y_pos cannot be negative.

height Number: The height of the tile, measured in grid blocks. Tiles must have a minimum height of 1.
widget Property Map: The informational widget contained in the tile. For example an XyChart.
width Number: The width of the tile, measured in grid blocks. Tiles must have a minimum width of 1.
xPos Number: The zero-indexed position of the tile in grid blocks relative to the left edge of the grid. Tiles must be contained within the specified number of columns. x_pos cannot be negative.
yPos Number: The zero-indexed position of the tile in grid blocks relative to the top edge of the grid. y_pos cannot be negative.

TimeSeriesFilter

Filter string

The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

Aggregation Pulumi.GoogleNative.Monitoring.V1.Inputs.Aggregation

By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

PickTimeSeriesFilter Pulumi.GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilter

Ranking based time series filter.

SecondaryAggregation Pulumi.GoogleNative.Monitoring.V1.Inputs.Aggregation

Apply a second aggregation after aggregation is applied.

StatisticalTimeSeriesFilter Pulumi.GoogleNative.Monitoring.V1.Inputs.StatisticalTimeSeriesFilter

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Filter string

The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

Aggregation Aggregation

By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

PickTimeSeriesFilter PickTimeSeriesFilter

Ranking based time series filter.

SecondaryAggregation Aggregation

Apply a second aggregation after aggregation is applied.

StatisticalTimeSeriesFilter StatisticalTimeSeriesFilter

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

filter String

The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

aggregation Aggregation

By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

pickTimeSeriesFilter PickTimeSeriesFilter

Ranking based time series filter.

secondaryAggregation Aggregation

Apply a second aggregation after aggregation is applied.

statisticalTimeSeriesFilter StatisticalTimeSeriesFilter

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

filter string

The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

aggregation Aggregation

By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

pickTimeSeriesFilter PickTimeSeriesFilter

Ranking based time series filter.

secondaryAggregation Aggregation

Apply a second aggregation after aggregation is applied.

statisticalTimeSeriesFilter StatisticalTimeSeriesFilter

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

filter str

The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

aggregation Aggregation

By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

pick_time_series_filter PickTimeSeriesFilter

Ranking based time series filter.

secondary_aggregation Aggregation

Apply a second aggregation after aggregation is applied.

statistical_time_series_filter StatisticalTimeSeriesFilter

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

filter String

The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

aggregation Property Map

By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

pickTimeSeriesFilter Property Map

Ranking based time series filter.

secondaryAggregation Property Map

Apply a second aggregation after aggregation is applied.

statisticalTimeSeriesFilter Property Map

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

TimeSeriesFilterRatio

Denominator Pulumi.GoogleNative.Monitoring.V1.Inputs.RatioPart

The denominator of the ratio.

Numerator Pulumi.GoogleNative.Monitoring.V1.Inputs.RatioPart

The numerator of the ratio.

PickTimeSeriesFilter Pulumi.GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilter

Ranking based time series filter.

SecondaryAggregation Pulumi.GoogleNative.Monitoring.V1.Inputs.Aggregation

Apply a second aggregation after the ratio is computed.

StatisticalTimeSeriesFilter Pulumi.GoogleNative.Monitoring.V1.Inputs.StatisticalTimeSeriesFilter

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Denominator RatioPart

The denominator of the ratio.

Numerator RatioPart

The numerator of the ratio.

PickTimeSeriesFilter PickTimeSeriesFilter

Ranking based time series filter.

SecondaryAggregation Aggregation

Apply a second aggregation after the ratio is computed.

StatisticalTimeSeriesFilter StatisticalTimeSeriesFilter

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

denominator RatioPart

The denominator of the ratio.

numerator RatioPart

The numerator of the ratio.

pickTimeSeriesFilter PickTimeSeriesFilter

Ranking based time series filter.

secondaryAggregation Aggregation

Apply a second aggregation after the ratio is computed.

statisticalTimeSeriesFilter StatisticalTimeSeriesFilter

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

denominator RatioPart

The denominator of the ratio.

numerator RatioPart

The numerator of the ratio.

pickTimeSeriesFilter PickTimeSeriesFilter

Ranking based time series filter.

secondaryAggregation Aggregation

Apply a second aggregation after the ratio is computed.

statisticalTimeSeriesFilter StatisticalTimeSeriesFilter

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

denominator RatioPart

The denominator of the ratio.

numerator RatioPart

The numerator of the ratio.

pick_time_series_filter PickTimeSeriesFilter

Ranking based time series filter.

secondary_aggregation Aggregation

Apply a second aggregation after the ratio is computed.

statistical_time_series_filter StatisticalTimeSeriesFilter

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

denominator Property Map

The denominator of the ratio.

numerator Property Map

The numerator of the ratio.

pickTimeSeriesFilter Property Map

Ranking based time series filter.

secondaryAggregation Property Map

Apply a second aggregation after the ratio is computed.

statisticalTimeSeriesFilter Property Map

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

TimeSeriesFilterRatioResponse

Denominator Pulumi.GoogleNative.Monitoring.V1.Inputs.RatioPartResponse

The denominator of the ratio.

Numerator Pulumi.GoogleNative.Monitoring.V1.Inputs.RatioPartResponse

The numerator of the ratio.

PickTimeSeriesFilter Pulumi.GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterResponse

Ranking based time series filter.

SecondaryAggregation Pulumi.GoogleNative.Monitoring.V1.Inputs.AggregationResponse

Apply a second aggregation after the ratio is computed.

StatisticalTimeSeriesFilter Pulumi.GoogleNative.Monitoring.V1.Inputs.StatisticalTimeSeriesFilterResponse

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Denominator RatioPartResponse

The denominator of the ratio.

Numerator RatioPartResponse

The numerator of the ratio.

PickTimeSeriesFilter PickTimeSeriesFilterResponse

Ranking based time series filter.

SecondaryAggregation AggregationResponse

Apply a second aggregation after the ratio is computed.

StatisticalTimeSeriesFilter StatisticalTimeSeriesFilterResponse

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

denominator RatioPartResponse

The denominator of the ratio.

numerator RatioPartResponse

The numerator of the ratio.

pickTimeSeriesFilter PickTimeSeriesFilterResponse

Ranking based time series filter.

secondaryAggregation AggregationResponse

Apply a second aggregation after the ratio is computed.

statisticalTimeSeriesFilter StatisticalTimeSeriesFilterResponse

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

denominator RatioPartResponse

The denominator of the ratio.

numerator RatioPartResponse

The numerator of the ratio.

pickTimeSeriesFilter PickTimeSeriesFilterResponse

Ranking based time series filter.

secondaryAggregation AggregationResponse

Apply a second aggregation after the ratio is computed.

statisticalTimeSeriesFilter StatisticalTimeSeriesFilterResponse

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

denominator RatioPartResponse

The denominator of the ratio.

numerator RatioPartResponse

The numerator of the ratio.

pick_time_series_filter PickTimeSeriesFilterResponse

Ranking based time series filter.

secondary_aggregation AggregationResponse

Apply a second aggregation after the ratio is computed.

statistical_time_series_filter StatisticalTimeSeriesFilterResponse

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

denominator Property Map

The denominator of the ratio.

numerator Property Map

The numerator of the ratio.

pickTimeSeriesFilter Property Map

Ranking based time series filter.

secondaryAggregation Property Map

Apply a second aggregation after the ratio is computed.

statisticalTimeSeriesFilter Property Map

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

TimeSeriesFilterResponse

Aggregation Pulumi.GoogleNative.Monitoring.V1.Inputs.AggregationResponse

By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

Filter string

The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

PickTimeSeriesFilter Pulumi.GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterResponse

Ranking based time series filter.

SecondaryAggregation Pulumi.GoogleNative.Monitoring.V1.Inputs.AggregationResponse

Apply a second aggregation after aggregation is applied.

StatisticalTimeSeriesFilter Pulumi.GoogleNative.Monitoring.V1.Inputs.StatisticalTimeSeriesFilterResponse

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Aggregation AggregationResponse

By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

Filter string

The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

PickTimeSeriesFilter PickTimeSeriesFilterResponse

Ranking based time series filter.

SecondaryAggregation AggregationResponse

Apply a second aggregation after aggregation is applied.

StatisticalTimeSeriesFilter StatisticalTimeSeriesFilterResponse

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

aggregation AggregationResponse

By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

filter String

The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

pickTimeSeriesFilter PickTimeSeriesFilterResponse

Ranking based time series filter.

secondaryAggregation AggregationResponse

Apply a second aggregation after aggregation is applied.

statisticalTimeSeriesFilter StatisticalTimeSeriesFilterResponse

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

aggregation AggregationResponse

By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

filter string

The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

pickTimeSeriesFilter PickTimeSeriesFilterResponse

Ranking based time series filter.

secondaryAggregation AggregationResponse

Apply a second aggregation after aggregation is applied.

statisticalTimeSeriesFilter StatisticalTimeSeriesFilterResponse

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

aggregation AggregationResponse

By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

filter str

The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

pick_time_series_filter PickTimeSeriesFilterResponse

Ranking based time series filter.

secondary_aggregation AggregationResponse

Apply a second aggregation after aggregation is applied.

statistical_time_series_filter StatisticalTimeSeriesFilterResponse

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

aggregation Property Map

By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.

filter String

The monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.

pickTimeSeriesFilter Property Map

Ranking based time series filter.

secondaryAggregation Property Map

Apply a second aggregation after aggregation is applied.

statisticalTimeSeriesFilter Property Map

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

Deprecated:

Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.

TimeSeriesQuery

PrometheusQuery string: A query used to fetch time series with PromQL.
TimeSeriesFilter Pulumi.GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilter: Filter parameters to fetch time series.
TimeSeriesFilterRatio Pulumi.GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatio: Parameters to fetch a ratio between two time series filters.
TimeSeriesQueryLanguage string: A query used to fetch time series with MQL.
UnitOverride string: The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the unit (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in MetricDescriptor.

PrometheusQuery string: A query used to fetch time series with PromQL.
TimeSeriesFilter TimeSeriesFilter: Filter parameters to fetch time series.
TimeSeriesFilterRatio TimeSeriesFilterRatio: Parameters to fetch a ratio between two time series filters.
TimeSeriesQueryLanguage string: A query used to fetch time series with MQL.
UnitOverride string: The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the unit (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in MetricDescriptor.

prometheusQuery String: A query used to fetch time series with PromQL.
timeSeriesFilter TimeSeriesFilter: Filter parameters to fetch time series.
timeSeriesFilterRatio TimeSeriesFilterRatio: Parameters to fetch a ratio between two time series filters.
timeSeriesQueryLanguage String: A query used to fetch time series with MQL.
unitOverride String: The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the unit (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in MetricDescriptor.

prometheusQuery string: A query used to fetch time series with PromQL.
timeSeriesFilter TimeSeriesFilter: Filter parameters to fetch time series.
timeSeriesFilterRatio TimeSeriesFilterRatio: Parameters to fetch a ratio between two time series filters.
timeSeriesQueryLanguage string: A query used to fetch time series with MQL.
unitOverride string: The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the unit (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in MetricDescriptor.

prometheus_query str: A query used to fetch time series with PromQL.
time_series_filter TimeSeriesFilter: Filter parameters to fetch time series.
time_series_filter_ratio TimeSeriesFilterRatio: Parameters to fetch a ratio between two time series filters.
time_series_query_language str: A query used to fetch time series with MQL.
unit_override str: The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the unit (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in MetricDescriptor.

prometheusQuery String: A query used to fetch time series with PromQL.
timeSeriesFilter Property Map: Filter parameters to fetch time series.
timeSeriesFilterRatio Property Map: Parameters to fetch a ratio between two time series filters.
timeSeriesQueryLanguage String: A query used to fetch time series with MQL.
unitOverride String: The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the unit (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in MetricDescriptor.

TimeSeriesQueryResponse

PrometheusQuery string: A query used to fetch time series with PromQL.
TimeSeriesFilter Pulumi.GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterResponse: Filter parameters to fetch time series.
TimeSeriesFilterRatio Pulumi.GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioResponse: Parameters to fetch a ratio between two time series filters.
TimeSeriesQueryLanguage string: A query used to fetch time series with MQL.
UnitOverride string: The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the unit (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in MetricDescriptor.

PrometheusQuery string: A query used to fetch time series with PromQL.
TimeSeriesFilter TimeSeriesFilterResponse: Filter parameters to fetch time series.
TimeSeriesFilterRatio TimeSeriesFilterRatioResponse: Parameters to fetch a ratio between two time series filters.
TimeSeriesQueryLanguage string: A query used to fetch time series with MQL.
UnitOverride string: The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the unit (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in MetricDescriptor.

prometheusQuery String: A query used to fetch time series with PromQL.
timeSeriesFilter TimeSeriesFilterResponse: Filter parameters to fetch time series.
timeSeriesFilterRatio TimeSeriesFilterRatioResponse: Parameters to fetch a ratio between two time series filters.
timeSeriesQueryLanguage String: A query used to fetch time series with MQL.
unitOverride String: The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the unit (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in MetricDescriptor.

prometheusQuery string: A query used to fetch time series with PromQL.
timeSeriesFilter TimeSeriesFilterResponse: Filter parameters to fetch time series.
timeSeriesFilterRatio TimeSeriesFilterRatioResponse: Parameters to fetch a ratio between two time series filters.
timeSeriesQueryLanguage string: A query used to fetch time series with MQL.
unitOverride string: The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the unit (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in MetricDescriptor.

prometheus_query str: A query used to fetch time series with PromQL.
time_series_filter TimeSeriesFilterResponse: Filter parameters to fetch time series.
time_series_filter_ratio TimeSeriesFilterRatioResponse: Parameters to fetch a ratio between two time series filters.
time_series_query_language str: A query used to fetch time series with MQL.
unit_override str: The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the unit (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in MetricDescriptor.

prometheusQuery String: A query used to fetch time series with PromQL.
timeSeriesFilter Property Map: Filter parameters to fetch time series.
timeSeriesFilterRatio Property Map: Parameters to fetch a ratio between two time series filters.
timeSeriesQueryLanguage String: A query used to fetch time series with MQL.
unitOverride String: The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the unit (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in MetricDescriptor.

TimeSeriesTable

DataSets List<Pulumi.GoogleNative.Monitoring.V1.Inputs.TableDataSet>: The data displayed in this table.
ColumnSettings List<Pulumi.GoogleNative.Monitoring.V1.Inputs.ColumnSettings>: Optional. The list of the persistent column settings for the table.
MetricVisualization Pulumi.GoogleNative.Monitoring.V1.TimeSeriesTableMetricVisualization: Optional. Store rendering strategy

DataSets []TableDataSet: The data displayed in this table.
ColumnSettings []ColumnSettings: Optional. The list of the persistent column settings for the table.
MetricVisualization TimeSeriesTableMetricVisualization: Optional. Store rendering strategy

dataSets List<TableDataSet>: The data displayed in this table.
columnSettings List<ColumnSettings>: Optional. The list of the persistent column settings for the table.
metricVisualization TimeSeriesTableMetricVisualization: Optional. Store rendering strategy

dataSets TableDataSet[]: The data displayed in this table.
columnSettings ColumnSettings[]: Optional. The list of the persistent column settings for the table.
metricVisualization TimeSeriesTableMetricVisualization: Optional. Store rendering strategy

data_sets Sequence[TableDataSet]: The data displayed in this table.
column_settings Sequence[ColumnSettings]: Optional. The list of the persistent column settings for the table.
metric_visualization TimeSeriesTableMetricVisualization: Optional. Store rendering strategy

dataSets List<Property Map>: The data displayed in this table.
columnSettings List<Property Map>: Optional. The list of the persistent column settings for the table.
metricVisualization "METRIC_VISUALIZATION_UNSPECIFIED" | "NUMBER" | "BAR": Optional. Store rendering strategy

TimeSeriesTableMetricVisualization

MetricVisualizationUnspecified: METRIC_VISUALIZATION_UNSPECIFIED
Unspecified state
Number: NUMBER
Default text rendering
Bar: BAR
Horizontal bar rendering

TimeSeriesTableMetricVisualizationMetricVisualizationUnspecified: METRIC_VISUALIZATION_UNSPECIFIED
Unspecified state
TimeSeriesTableMetricVisualizationNumber: NUMBER
Default text rendering
TimeSeriesTableMetricVisualizationBar: BAR
Horizontal bar rendering

MetricVisualizationUnspecified: METRIC_VISUALIZATION_UNSPECIFIED
Unspecified state
Number: NUMBER
Default text rendering
Bar: BAR
Horizontal bar rendering

MetricVisualizationUnspecified: METRIC_VISUALIZATION_UNSPECIFIED
Unspecified state
Number: NUMBER
Default text rendering
Bar: BAR
Horizontal bar rendering

METRIC_VISUALIZATION_UNSPECIFIED: METRIC_VISUALIZATION_UNSPECIFIED
Unspecified state
NUMBER: NUMBER
Default text rendering
BAR: BAR
Horizontal bar rendering

"METRIC_VISUALIZATION_UNSPECIFIED": METRIC_VISUALIZATION_UNSPECIFIED
Unspecified state
"NUMBER": NUMBER
Default text rendering
"BAR": BAR
Horizontal bar rendering

TimeSeriesTableResponse

ColumnSettings List<Pulumi.GoogleNative.Monitoring.V1.Inputs.ColumnSettingsResponse>: Optional. The list of the persistent column settings for the table.
DataSets List<Pulumi.GoogleNative.Monitoring.V1.Inputs.TableDataSetResponse>: The data displayed in this table.
MetricVisualization string: Optional. Store rendering strategy

ColumnSettings []ColumnSettingsResponse: Optional. The list of the persistent column settings for the table.
DataSets []TableDataSetResponse: The data displayed in this table.
MetricVisualization string: Optional. Store rendering strategy

columnSettings List<ColumnSettingsResponse>: Optional. The list of the persistent column settings for the table.
dataSets List<TableDataSetResponse>: The data displayed in this table.
metricVisualization String: Optional. Store rendering strategy

columnSettings ColumnSettingsResponse[]: Optional. The list of the persistent column settings for the table.
dataSets TableDataSetResponse[]: The data displayed in this table.
metricVisualization string: Optional. Store rendering strategy

column_settings Sequence[ColumnSettingsResponse]: Optional. The list of the persistent column settings for the table.
data_sets Sequence[TableDataSetResponse]: The data displayed in this table.
metric_visualization str: Optional. Store rendering strategy

columnSettings List<Property Map>: Optional. The list of the persistent column settings for the table.
dataSets List<Property Map>: The data displayed in this table.
metricVisualization String: Optional. Store rendering strategy

AlertChart Pulumi.GoogleNative.Monitoring.V1.Inputs.AlertChart: A chart of alert policy data.
Blank Pulumi.GoogleNative.Monitoring.V1.Inputs.Empty: A blank space.
CollapsibleGroup Pulumi.GoogleNative.Monitoring.V1.Inputs.CollapsibleGroup: A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
LogsPanel Pulumi.GoogleNative.Monitoring.V1.Inputs.LogsPanel: A widget that shows a stream of logs.
Scorecard Pulumi.GoogleNative.Monitoring.V1.Inputs.Scorecard: A scorecard summarizing time series data.
Text Pulumi.GoogleNative.Monitoring.V1.Inputs.Text: A raw string or markdown displaying textual content.
TimeSeriesTable Pulumi.GoogleNative.Monitoring.V1.Inputs.TimeSeriesTable: A widget that displays time series data in a tabular format.
Title string: Optional. The title of the widget.
XyChart Pulumi.GoogleNative.Monitoring.V1.Inputs.XyChart: A chart of time series data.

AlertChart AlertChart: A chart of alert policy data.
Blank Empty: A blank space.
CollapsibleGroup CollapsibleGroup: A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
LogsPanel LogsPanel: A widget that shows a stream of logs.
Scorecard Scorecard: A scorecard summarizing time series data.
Text Text: A raw string or markdown displaying textual content.
TimeSeriesTable TimeSeriesTable: A widget that displays time series data in a tabular format.
Title string: Optional. The title of the widget.
XyChart XyChart: A chart of time series data.

alertChart AlertChart: A chart of alert policy data.
blank Empty: A blank space.
collapsibleGroup CollapsibleGroup: A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
logsPanel LogsPanel: A widget that shows a stream of logs.
scorecard Scorecard: A scorecard summarizing time series data.
text Text: A raw string or markdown displaying textual content.
timeSeriesTable TimeSeriesTable: A widget that displays time series data in a tabular format.
title String: Optional. The title of the widget.
xyChart XyChart: A chart of time series data.

alertChart AlertChart: A chart of alert policy data.
blank Empty: A blank space.
collapsibleGroup CollapsibleGroup: A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
logsPanel LogsPanel: A widget that shows a stream of logs.
scorecard Scorecard: A scorecard summarizing time series data.
text Text: A raw string or markdown displaying textual content.
timeSeriesTable TimeSeriesTable: A widget that displays time series data in a tabular format.
title string: Optional. The title of the widget.
xyChart XyChart: A chart of time series data.

alert_chart AlertChart: A chart of alert policy data.
blank Empty: A blank space.
collapsible_group CollapsibleGroup: A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
logs_panel LogsPanel: A widget that shows a stream of logs.
scorecard Scorecard: A scorecard summarizing time series data.
text Text: A raw string or markdown displaying textual content.
time_series_table TimeSeriesTable: A widget that displays time series data in a tabular format.
title str: Optional. The title of the widget.
xy_chart XyChart: A chart of time series data.

alertChart Property Map: A chart of alert policy data.
blank Property Map: A blank space.
collapsibleGroup Property Map: A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
logsPanel Property Map: A widget that shows a stream of logs.
scorecard Property Map: A scorecard summarizing time series data.
text Property Map: A raw string or markdown displaying textual content.
timeSeriesTable Property Map: A widget that displays time series data in a tabular format.
title String: Optional. The title of the widget.
xyChart Property Map: A chart of time series data.

WidgetResponse

AlertChart Pulumi.GoogleNative.Monitoring.V1.Inputs.AlertChartResponse: A chart of alert policy data.
Blank Pulumi.GoogleNative.Monitoring.V1.Inputs.EmptyResponse: A blank space.
CollapsibleGroup Pulumi.GoogleNative.Monitoring.V1.Inputs.CollapsibleGroupResponse: A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
LogsPanel Pulumi.GoogleNative.Monitoring.V1.Inputs.LogsPanelResponse: A widget that shows a stream of logs.
Scorecard Pulumi.GoogleNative.Monitoring.V1.Inputs.ScorecardResponse: A scorecard summarizing time series data.
Text Pulumi.GoogleNative.Monitoring.V1.Inputs.TextResponse: A raw string or markdown displaying textual content.
TimeSeriesTable Pulumi.GoogleNative.Monitoring.V1.Inputs.TimeSeriesTableResponse: A widget that displays time series data in a tabular format.
Title string: Optional. The title of the widget.
XyChart Pulumi.GoogleNative.Monitoring.V1.Inputs.XyChartResponse: A chart of time series data.

AlertChart AlertChartResponse: A chart of alert policy data.
Blank EmptyResponse: A blank space.
CollapsibleGroup CollapsibleGroupResponse: A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
LogsPanel LogsPanelResponse: A widget that shows a stream of logs.
Scorecard ScorecardResponse: A scorecard summarizing time series data.
Text TextResponse: A raw string or markdown displaying textual content.
TimeSeriesTable TimeSeriesTableResponse: A widget that displays time series data in a tabular format.
Title string: Optional. The title of the widget.
XyChart XyChartResponse: A chart of time series data.

alertChart AlertChartResponse: A chart of alert policy data.
blank EmptyResponse: A blank space.
collapsibleGroup CollapsibleGroupResponse: A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
logsPanel LogsPanelResponse: A widget that shows a stream of logs.
scorecard ScorecardResponse: A scorecard summarizing time series data.
text TextResponse: A raw string or markdown displaying textual content.
timeSeriesTable TimeSeriesTableResponse: A widget that displays time series data in a tabular format.
title String: Optional. The title of the widget.
xyChart XyChartResponse: A chart of time series data.

alertChart AlertChartResponse: A chart of alert policy data.
blank EmptyResponse: A blank space.
collapsibleGroup CollapsibleGroupResponse: A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
logsPanel LogsPanelResponse: A widget that shows a stream of logs.
scorecard ScorecardResponse: A scorecard summarizing time series data.
text TextResponse: A raw string or markdown displaying textual content.
timeSeriesTable TimeSeriesTableResponse: A widget that displays time series data in a tabular format.
title string: Optional. The title of the widget.
xyChart XyChartResponse: A chart of time series data.

alert_chart AlertChartResponse: A chart of alert policy data.
blank EmptyResponse: A blank space.
collapsible_group CollapsibleGroupResponse: A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
logs_panel LogsPanelResponse: A widget that shows a stream of logs.
scorecard ScorecardResponse: A scorecard summarizing time series data.
text TextResponse: A raw string or markdown displaying textual content.
time_series_table TimeSeriesTableResponse: A widget that displays time series data in a tabular format.
title str: Optional. The title of the widget.
xy_chart XyChartResponse: A chart of time series data.

alertChart Property Map: A chart of alert policy data.
blank Property Map: A blank space.
collapsibleGroup Property Map: A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
logsPanel Property Map: A widget that shows a stream of logs.
scorecard Property Map: A scorecard summarizing time series data.
text Property Map: A raw string or markdown displaying textual content.
timeSeriesTable Property Map: A widget that displays time series data in a tabular format.
title String: Optional. The title of the widget.
xyChart Property Map: A chart of time series data.

XyChart

DataSets List<Pulumi.GoogleNative.Monitoring.V1.Inputs.DataSet>: The data displayed in this chart.
ChartOptions Pulumi.GoogleNative.Monitoring.V1.Inputs.ChartOptions: Display options for the chart.
Thresholds List<Pulumi.GoogleNative.Monitoring.V1.Inputs.Threshold>: Threshold lines drawn horizontally across the chart.
TimeshiftDuration string: The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similar-length time periods (e.g., week-over-week metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type.
XAxis Pulumi.GoogleNative.Monitoring.V1.Inputs.Axis: The properties applied to the X axis.
Y2Axis Pulumi.GoogleNative.Monitoring.V1.Inputs.Axis: The properties applied to the Y2 axis.
YAxis Pulumi.GoogleNative.Monitoring.V1.Inputs.Axis: The properties applied to the Y axis.

DataSets []DataSet: The data displayed in this chart.
ChartOptions ChartOptions: Display options for the chart.
Thresholds []Threshold: Threshold lines drawn horizontally across the chart.
TimeshiftDuration string: The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similar-length time periods (e.g., week-over-week metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type.
XAxis Axis: The properties applied to the X axis.
Y2Axis Axis: The properties applied to the Y2 axis.
YAxis Axis: The properties applied to the Y axis.

dataSets List<DataSet>: The data displayed in this chart.
chartOptions ChartOptions: Display options for the chart.
thresholds List<Threshold>: Threshold lines drawn horizontally across the chart.
timeshiftDuration String: The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similar-length time periods (e.g., week-over-week metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type.
xAxis Axis: The properties applied to the X axis.
y2Axis Axis: The properties applied to the Y2 axis.
yAxis Axis: The properties applied to the Y axis.

dataSets DataSet[]: The data displayed in this chart.
chartOptions ChartOptions: Display options for the chart.
thresholds Threshold[]: Threshold lines drawn horizontally across the chart.
timeshiftDuration string: The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similar-length time periods (e.g., week-over-week metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type.
xAxis Axis: The properties applied to the X axis.
y2Axis Axis: The properties applied to the Y2 axis.
yAxis Axis: The properties applied to the Y axis.

data_sets Sequence[DataSet]: The data displayed in this chart.
chart_options ChartOptions: Display options for the chart.
thresholds Sequence[Threshold]: Threshold lines drawn horizontally across the chart.
timeshift_duration str: The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similar-length time periods (e.g., week-over-week metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type.
x_axis Axis: The properties applied to the X axis.
y2_axis Axis: The properties applied to the Y2 axis.
y_axis Axis: The properties applied to the Y axis.

dataSets List<Property Map>: The data displayed in this chart.
chartOptions Property Map: Display options for the chart.
thresholds List<Property Map>: Threshold lines drawn horizontally across the chart.
timeshiftDuration String: The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similar-length time periods (e.g., week-over-week metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type.
xAxis Property Map: The properties applied to the X axis.
y2Axis Property Map: The properties applied to the Y2 axis.
yAxis Property Map: The properties applied to the Y axis.

XyChartResponse

ChartOptions Pulumi.GoogleNative.Monitoring.V1.Inputs.ChartOptionsResponse: Display options for the chart.
DataSets List<Pulumi.GoogleNative.Monitoring.V1.Inputs.DataSetResponse>: The data displayed in this chart.
Thresholds List<Pulumi.GoogleNative.Monitoring.V1.Inputs.ThresholdResponse>: Threshold lines drawn horizontally across the chart.
TimeshiftDuration string: The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similar-length time periods (e.g., week-over-week metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type.
XAxis Pulumi.GoogleNative.Monitoring.V1.Inputs.AxisResponse: The properties applied to the X axis.
Y2Axis Pulumi.GoogleNative.Monitoring.V1.Inputs.AxisResponse: The properties applied to the Y2 axis.
YAxis Pulumi.GoogleNative.Monitoring.V1.Inputs.AxisResponse: The properties applied to the Y axis.

ChartOptions ChartOptionsResponse: Display options for the chart.
DataSets []DataSetResponse: The data displayed in this chart.
Thresholds []ThresholdResponse: Threshold lines drawn horizontally across the chart.
TimeshiftDuration string: The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similar-length time periods (e.g., week-over-week metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type.
XAxis AxisResponse: The properties applied to the X axis.
Y2Axis AxisResponse: The properties applied to the Y2 axis.
YAxis AxisResponse: The properties applied to the Y axis.

chartOptions ChartOptionsResponse: Display options for the chart.
dataSets List<DataSetResponse>: The data displayed in this chart.
thresholds List<ThresholdResponse>: Threshold lines drawn horizontally across the chart.
timeshiftDuration String: The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similar-length time periods (e.g., week-over-week metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type.
xAxis AxisResponse: The properties applied to the X axis.
y2Axis AxisResponse: The properties applied to the Y2 axis.
yAxis AxisResponse: The properties applied to the Y axis.

chartOptions ChartOptionsResponse: Display options for the chart.
dataSets DataSetResponse[]: The data displayed in this chart.
thresholds ThresholdResponse[]: Threshold lines drawn horizontally across the chart.
timeshiftDuration string: The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similar-length time periods (e.g., week-over-week metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type.
xAxis AxisResponse: The properties applied to the X axis.
y2Axis AxisResponse: The properties applied to the Y2 axis.
yAxis AxisResponse: The properties applied to the Y axis.

chart_options ChartOptionsResponse: Display options for the chart.
data_sets Sequence[DataSetResponse]: The data displayed in this chart.
thresholds Sequence[ThresholdResponse]: Threshold lines drawn horizontally across the chart.
timeshift_duration str: The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similar-length time periods (e.g., week-over-week metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type.
x_axis AxisResponse: The properties applied to the X axis.
y2_axis AxisResponse: The properties applied to the Y2 axis.
y_axis AxisResponse: The properties applied to the Y axis.

chartOptions Property Map: Display options for the chart.
dataSets List<Property Map>: The data displayed in this chart.
thresholds List<Property Map>: Threshold lines drawn horizontally across the chart.
timeshiftDuration String: The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similar-length time periods (e.g., week-over-week metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type.
xAxis Property Map: The properties applied to the X axis.
y2Axis Property Map: The properties applied to the Y2 axis.
yAxis Property Map: The properties applied to the Y axis.

Package Details

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