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Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
google-native.monitoring/v1.Dashboard
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Google Cloud Native is in preview. Google Cloud Classic is fully supported.
Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
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
Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.
Constructor syntax
new Dashboard(name: string, args: DashboardArgs, opts?: CustomResourceOptions);@overload
def Dashboard(resource_name: str,
args: DashboardArgs,
opts: Optional[ResourceOptions] = None)
@overload
def Dashboard(resource_name: str,
opts: Optional[ResourceOptions] = None,
display_name: Optional[str] = None,
column_layout: Optional[ColumnLayoutArgs] = None,
dashboard_filters: Optional[Sequence[DashboardFilterArgs]] = 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)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.
Parameters
- 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.
Constructor example
The following reference example uses placeholder values for all input properties.
var dashboardResource = new GoogleNative.Monitoring.V1.Dashboard("dashboardResource", new()
{
DisplayName = "string",
ColumnLayout = new GoogleNative.Monitoring.V1.Inputs.ColumnLayoutArgs
{
Columns = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ColumnArgs
{
Weight = "string",
Widgets = new[]
{
new GoogleNative.Monitoring.V1.Inputs.WidgetArgs
{
AlertChart = new GoogleNative.Monitoring.V1.Inputs.AlertChartArgs
{
Name = "string",
},
Blank = null,
CollapsibleGroup = new GoogleNative.Monitoring.V1.Inputs.CollapsibleGroupArgs
{
Collapsed = false,
},
ErrorReportingPanel = new GoogleNative.Monitoring.V1.Inputs.ErrorReportingPanelArgs
{
ProjectNames = new[]
{
"string",
},
Services = new[]
{
"string",
},
Versions = new[]
{
"string",
},
},
Id = "string",
IncidentList = new GoogleNative.Monitoring.V1.Inputs.IncidentListArgs
{
MonitoredResources = new[]
{
new GoogleNative.Monitoring.V1.Inputs.MonitoredResourceArgs
{
Labels =
{
{ "string", "string" },
},
Type = "string",
},
},
PolicyNames = new[]
{
"string",
},
},
LogsPanel = new GoogleNative.Monitoring.V1.Inputs.LogsPanelArgs
{
Filter = "string",
ResourceNames = new[]
{
"string",
},
},
PieChart = new GoogleNative.Monitoring.V1.Inputs.PieChartArgs
{
ChartType = GoogleNative.Monitoring.V1.PieChartChartType.PieChartTypeUnspecified,
DataSets = new[]
{
new GoogleNative.Monitoring.V1.Inputs.PieChartDataSetArgs
{
TimeSeriesQuery = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryArgs
{
OpsAnalyticsQuery = new GoogleNative.Monitoring.V1.Inputs.OpsAnalyticsQueryArgs
{
Sql = "string",
},
OutputFullDuration = false,
PrometheusQuery = "string",
TimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesFilterRatio = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioArgs
{
Denominator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
Numerator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesQueryLanguage = "string",
UnitOverride = "string",
},
MinAlignmentPeriod = "string",
SliceNameTemplate = "string",
},
},
ShowLabels = false,
},
Scorecard = new GoogleNative.Monitoring.V1.Inputs.ScorecardArgs
{
TimeSeriesQuery = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryArgs
{
OpsAnalyticsQuery = new GoogleNative.Monitoring.V1.Inputs.OpsAnalyticsQueryArgs
{
Sql = "string",
},
OutputFullDuration = false,
PrometheusQuery = "string",
TimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesFilterRatio = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioArgs
{
Denominator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
Numerator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesQueryLanguage = "string",
UnitOverride = "string",
},
BlankView = null,
GaugeView = new GoogleNative.Monitoring.V1.Inputs.GaugeViewArgs
{
LowerBound = 0,
UpperBound = 0,
},
SparkChartView = new GoogleNative.Monitoring.V1.Inputs.SparkChartViewArgs
{
SparkChartType = GoogleNative.Monitoring.V1.SparkChartViewSparkChartType.SparkChartTypeUnspecified,
MinAlignmentPeriod = "string",
},
Thresholds = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ThresholdArgs
{
Color = GoogleNative.Monitoring.V1.ThresholdColor.ColorUnspecified,
Direction = GoogleNative.Monitoring.V1.ThresholdDirection.DirectionUnspecified,
Label = "string",
TargetAxis = GoogleNative.Monitoring.V1.ThresholdTargetAxis.TargetAxisUnspecified,
Value = 0,
},
},
},
Text = new GoogleNative.Monitoring.V1.Inputs.TextArgs
{
Content = "string",
Format = GoogleNative.Monitoring.V1.TextFormat.FormatUnspecified,
Style = new GoogleNative.Monitoring.V1.Inputs.TextStyleArgs
{
BackgroundColor = "string",
FontSize = GoogleNative.Monitoring.V1.TextStyleFontSize.FontSizeUnspecified,
HorizontalAlignment = GoogleNative.Monitoring.V1.TextStyleHorizontalAlignment.HorizontalAlignmentUnspecified,
Padding = GoogleNative.Monitoring.V1.TextStylePadding.PaddingSizeUnspecified,
PointerLocation = GoogleNative.Monitoring.V1.TextStylePointerLocation.PointerLocationUnspecified,
TextColor = "string",
VerticalAlignment = GoogleNative.Monitoring.V1.TextStyleVerticalAlignment.VerticalAlignmentUnspecified,
},
},
TimeSeriesTable = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesTableArgs
{
DataSets = new[]
{
new GoogleNative.Monitoring.V1.Inputs.TableDataSetArgs
{
TimeSeriesQuery = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryArgs
{
OpsAnalyticsQuery = new GoogleNative.Monitoring.V1.Inputs.OpsAnalyticsQueryArgs
{
Sql = "string",
},
OutputFullDuration = false,
PrometheusQuery = "string",
TimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesFilterRatio = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioArgs
{
Denominator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
Numerator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesQueryLanguage = "string",
UnitOverride = "string",
},
MinAlignmentPeriod = "string",
TableDisplayOptions = new GoogleNative.Monitoring.V1.Inputs.TableDisplayOptionsArgs
{
ShownColumns = new[]
{
"string",
},
},
TableTemplate = "string",
},
},
ColumnSettings = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ColumnSettingsArgs
{
Column = "string",
Visible = false,
},
},
MetricVisualization = GoogleNative.Monitoring.V1.TimeSeriesTableMetricVisualization.MetricVisualizationUnspecified,
},
Title = "string",
XyChart = new GoogleNative.Monitoring.V1.Inputs.XyChartArgs
{
DataSets = new[]
{
new GoogleNative.Monitoring.V1.Inputs.DataSetArgs
{
TimeSeriesQuery = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryArgs
{
OpsAnalyticsQuery = new GoogleNative.Monitoring.V1.Inputs.OpsAnalyticsQueryArgs
{
Sql = "string",
},
OutputFullDuration = false,
PrometheusQuery = "string",
TimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesFilterRatio = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioArgs
{
Denominator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
Numerator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesQueryLanguage = "string",
UnitOverride = "string",
},
Breakdowns = new[]
{
new GoogleNative.Monitoring.V1.Inputs.BreakdownArgs
{
AggregationFunction = new GoogleNative.Monitoring.V1.Inputs.AggregationFunctionArgs
{
Type = "string",
Parameters = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ParameterArgs
{
DoubleValue = 0,
IntValue = "string",
},
},
},
Column = "string",
Limit = 0,
SortOrder = GoogleNative.Monitoring.V1.BreakdownSortOrder.SortOrderUnspecified,
},
},
Dimensions = new[]
{
new GoogleNative.Monitoring.V1.Inputs.DimensionArgs
{
Column = "string",
ColumnType = "string",
FloatBinSize = 0,
MaxBinCount = 0,
NumericBinSize = 0,
SortColumn = "string",
SortOrder = GoogleNative.Monitoring.V1.DimensionSortOrder.SortOrderUnspecified,
TimeBinSize = "string",
},
},
LegendTemplate = "string",
Measures = new[]
{
new GoogleNative.Monitoring.V1.Inputs.MeasureArgs
{
AggregationFunction = new GoogleNative.Monitoring.V1.Inputs.AggregationFunctionArgs
{
Type = "string",
Parameters = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ParameterArgs
{
DoubleValue = 0,
IntValue = "string",
},
},
},
Column = "string",
},
},
MinAlignmentPeriod = "string",
PlotType = GoogleNative.Monitoring.V1.DataSetPlotType.PlotTypeUnspecified,
TargetAxis = GoogleNative.Monitoring.V1.DataSetTargetAxis.TargetAxisUnspecified,
},
},
ChartOptions = new GoogleNative.Monitoring.V1.Inputs.ChartOptionsArgs
{
DisplayHorizontal = false,
Mode = GoogleNative.Monitoring.V1.ChartOptionsMode.ModeUnspecified,
},
Thresholds = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ThresholdArgs
{
Color = GoogleNative.Monitoring.V1.ThresholdColor.ColorUnspecified,
Direction = GoogleNative.Monitoring.V1.ThresholdDirection.DirectionUnspecified,
Label = "string",
TargetAxis = GoogleNative.Monitoring.V1.ThresholdTargetAxis.TargetAxisUnspecified,
Value = 0,
},
},
TimeshiftDuration = "string",
XAxis = new GoogleNative.Monitoring.V1.Inputs.AxisArgs
{
Label = "string",
Scale = GoogleNative.Monitoring.V1.AxisScale.ScaleUnspecified,
},
Y2Axis = new GoogleNative.Monitoring.V1.Inputs.AxisArgs
{
Label = "string",
Scale = GoogleNative.Monitoring.V1.AxisScale.ScaleUnspecified,
},
YAxis = new GoogleNative.Monitoring.V1.Inputs.AxisArgs
{
Label = "string",
Scale = GoogleNative.Monitoring.V1.AxisScale.ScaleUnspecified,
},
},
},
},
},
},
},
DashboardFilters = new[]
{
new GoogleNative.Monitoring.V1.Inputs.DashboardFilterArgs
{
LabelKey = "string",
FilterType = GoogleNative.Monitoring.V1.DashboardFilterFilterType.FilterTypeUnspecified,
StringValue = "string",
TemplateVariable = "string",
},
},
Etag = "string",
GridLayout = new GoogleNative.Monitoring.V1.Inputs.GridLayoutArgs
{
Columns = "string",
Widgets = new[]
{
new GoogleNative.Monitoring.V1.Inputs.WidgetArgs
{
AlertChart = new GoogleNative.Monitoring.V1.Inputs.AlertChartArgs
{
Name = "string",
},
Blank = null,
CollapsibleGroup = new GoogleNative.Monitoring.V1.Inputs.CollapsibleGroupArgs
{
Collapsed = false,
},
ErrorReportingPanel = new GoogleNative.Monitoring.V1.Inputs.ErrorReportingPanelArgs
{
ProjectNames = new[]
{
"string",
},
Services = new[]
{
"string",
},
Versions = new[]
{
"string",
},
},
Id = "string",
IncidentList = new GoogleNative.Monitoring.V1.Inputs.IncidentListArgs
{
MonitoredResources = new[]
{
new GoogleNative.Monitoring.V1.Inputs.MonitoredResourceArgs
{
Labels =
{
{ "string", "string" },
},
Type = "string",
},
},
PolicyNames = new[]
{
"string",
},
},
LogsPanel = new GoogleNative.Monitoring.V1.Inputs.LogsPanelArgs
{
Filter = "string",
ResourceNames = new[]
{
"string",
},
},
PieChart = new GoogleNative.Monitoring.V1.Inputs.PieChartArgs
{
ChartType = GoogleNative.Monitoring.V1.PieChartChartType.PieChartTypeUnspecified,
DataSets = new[]
{
new GoogleNative.Monitoring.V1.Inputs.PieChartDataSetArgs
{
TimeSeriesQuery = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryArgs
{
OpsAnalyticsQuery = new GoogleNative.Monitoring.V1.Inputs.OpsAnalyticsQueryArgs
{
Sql = "string",
},
OutputFullDuration = false,
PrometheusQuery = "string",
TimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesFilterRatio = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioArgs
{
Denominator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
Numerator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesQueryLanguage = "string",
UnitOverride = "string",
},
MinAlignmentPeriod = "string",
SliceNameTemplate = "string",
},
},
ShowLabels = false,
},
Scorecard = new GoogleNative.Monitoring.V1.Inputs.ScorecardArgs
{
TimeSeriesQuery = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryArgs
{
OpsAnalyticsQuery = new GoogleNative.Monitoring.V1.Inputs.OpsAnalyticsQueryArgs
{
Sql = "string",
},
OutputFullDuration = false,
PrometheusQuery = "string",
TimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesFilterRatio = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioArgs
{
Denominator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
Numerator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesQueryLanguage = "string",
UnitOverride = "string",
},
BlankView = null,
GaugeView = new GoogleNative.Monitoring.V1.Inputs.GaugeViewArgs
{
LowerBound = 0,
UpperBound = 0,
},
SparkChartView = new GoogleNative.Monitoring.V1.Inputs.SparkChartViewArgs
{
SparkChartType = GoogleNative.Monitoring.V1.SparkChartViewSparkChartType.SparkChartTypeUnspecified,
MinAlignmentPeriod = "string",
},
Thresholds = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ThresholdArgs
{
Color = GoogleNative.Monitoring.V1.ThresholdColor.ColorUnspecified,
Direction = GoogleNative.Monitoring.V1.ThresholdDirection.DirectionUnspecified,
Label = "string",
TargetAxis = GoogleNative.Monitoring.V1.ThresholdTargetAxis.TargetAxisUnspecified,
Value = 0,
},
},
},
Text = new GoogleNative.Monitoring.V1.Inputs.TextArgs
{
Content = "string",
Format = GoogleNative.Monitoring.V1.TextFormat.FormatUnspecified,
Style = new GoogleNative.Monitoring.V1.Inputs.TextStyleArgs
{
BackgroundColor = "string",
FontSize = GoogleNative.Monitoring.V1.TextStyleFontSize.FontSizeUnspecified,
HorizontalAlignment = GoogleNative.Monitoring.V1.TextStyleHorizontalAlignment.HorizontalAlignmentUnspecified,
Padding = GoogleNative.Monitoring.V1.TextStylePadding.PaddingSizeUnspecified,
PointerLocation = GoogleNative.Monitoring.V1.TextStylePointerLocation.PointerLocationUnspecified,
TextColor = "string",
VerticalAlignment = GoogleNative.Monitoring.V1.TextStyleVerticalAlignment.VerticalAlignmentUnspecified,
},
},
TimeSeriesTable = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesTableArgs
{
DataSets = new[]
{
new GoogleNative.Monitoring.V1.Inputs.TableDataSetArgs
{
TimeSeriesQuery = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryArgs
{
OpsAnalyticsQuery = new GoogleNative.Monitoring.V1.Inputs.OpsAnalyticsQueryArgs
{
Sql = "string",
},
OutputFullDuration = false,
PrometheusQuery = "string",
TimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesFilterRatio = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioArgs
{
Denominator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
Numerator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesQueryLanguage = "string",
UnitOverride = "string",
},
MinAlignmentPeriod = "string",
TableDisplayOptions = new GoogleNative.Monitoring.V1.Inputs.TableDisplayOptionsArgs
{
ShownColumns = new[]
{
"string",
},
},
TableTemplate = "string",
},
},
ColumnSettings = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ColumnSettingsArgs
{
Column = "string",
Visible = false,
},
},
MetricVisualization = GoogleNative.Monitoring.V1.TimeSeriesTableMetricVisualization.MetricVisualizationUnspecified,
},
Title = "string",
XyChart = new GoogleNative.Monitoring.V1.Inputs.XyChartArgs
{
DataSets = new[]
{
new GoogleNative.Monitoring.V1.Inputs.DataSetArgs
{
TimeSeriesQuery = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryArgs
{
OpsAnalyticsQuery = new GoogleNative.Monitoring.V1.Inputs.OpsAnalyticsQueryArgs
{
Sql = "string",
},
OutputFullDuration = false,
PrometheusQuery = "string",
TimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesFilterRatio = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioArgs
{
Denominator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
Numerator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesQueryLanguage = "string",
UnitOverride = "string",
},
Breakdowns = new[]
{
new GoogleNative.Monitoring.V1.Inputs.BreakdownArgs
{
AggregationFunction = new GoogleNative.Monitoring.V1.Inputs.AggregationFunctionArgs
{
Type = "string",
Parameters = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ParameterArgs
{
DoubleValue = 0,
IntValue = "string",
},
},
},
Column = "string",
Limit = 0,
SortOrder = GoogleNative.Monitoring.V1.BreakdownSortOrder.SortOrderUnspecified,
},
},
Dimensions = new[]
{
new GoogleNative.Monitoring.V1.Inputs.DimensionArgs
{
Column = "string",
ColumnType = "string",
FloatBinSize = 0,
MaxBinCount = 0,
NumericBinSize = 0,
SortColumn = "string",
SortOrder = GoogleNative.Monitoring.V1.DimensionSortOrder.SortOrderUnspecified,
TimeBinSize = "string",
},
},
LegendTemplate = "string",
Measures = new[]
{
new GoogleNative.Monitoring.V1.Inputs.MeasureArgs
{
AggregationFunction = new GoogleNative.Monitoring.V1.Inputs.AggregationFunctionArgs
{
Type = "string",
Parameters = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ParameterArgs
{
DoubleValue = 0,
IntValue = "string",
},
},
},
Column = "string",
},
},
MinAlignmentPeriod = "string",
PlotType = GoogleNative.Monitoring.V1.DataSetPlotType.PlotTypeUnspecified,
TargetAxis = GoogleNative.Monitoring.V1.DataSetTargetAxis.TargetAxisUnspecified,
},
},
ChartOptions = new GoogleNative.Monitoring.V1.Inputs.ChartOptionsArgs
{
DisplayHorizontal = false,
Mode = GoogleNative.Monitoring.V1.ChartOptionsMode.ModeUnspecified,
},
Thresholds = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ThresholdArgs
{
Color = GoogleNative.Monitoring.V1.ThresholdColor.ColorUnspecified,
Direction = GoogleNative.Monitoring.V1.ThresholdDirection.DirectionUnspecified,
Label = "string",
TargetAxis = GoogleNative.Monitoring.V1.ThresholdTargetAxis.TargetAxisUnspecified,
Value = 0,
},
},
TimeshiftDuration = "string",
XAxis = new GoogleNative.Monitoring.V1.Inputs.AxisArgs
{
Label = "string",
Scale = GoogleNative.Monitoring.V1.AxisScale.ScaleUnspecified,
},
Y2Axis = new GoogleNative.Monitoring.V1.Inputs.AxisArgs
{
Label = "string",
Scale = GoogleNative.Monitoring.V1.AxisScale.ScaleUnspecified,
},
YAxis = new GoogleNative.Monitoring.V1.Inputs.AxisArgs
{
Label = "string",
Scale = GoogleNative.Monitoring.V1.AxisScale.ScaleUnspecified,
},
},
},
},
},
Labels =
{
{ "string", "string" },
},
MosaicLayout = new GoogleNative.Monitoring.V1.Inputs.MosaicLayoutArgs
{
Columns = 0,
Tiles = new[]
{
new GoogleNative.Monitoring.V1.Inputs.TileArgs
{
Height = 0,
Widget = new GoogleNative.Monitoring.V1.Inputs.WidgetArgs
{
AlertChart = new GoogleNative.Monitoring.V1.Inputs.AlertChartArgs
{
Name = "string",
},
Blank = null,
CollapsibleGroup = new GoogleNative.Monitoring.V1.Inputs.CollapsibleGroupArgs
{
Collapsed = false,
},
ErrorReportingPanel = new GoogleNative.Monitoring.V1.Inputs.ErrorReportingPanelArgs
{
ProjectNames = new[]
{
"string",
},
Services = new[]
{
"string",
},
Versions = new[]
{
"string",
},
},
Id = "string",
IncidentList = new GoogleNative.Monitoring.V1.Inputs.IncidentListArgs
{
MonitoredResources = new[]
{
new GoogleNative.Monitoring.V1.Inputs.MonitoredResourceArgs
{
Labels =
{
{ "string", "string" },
},
Type = "string",
},
},
PolicyNames = new[]
{
"string",
},
},
LogsPanel = new GoogleNative.Monitoring.V1.Inputs.LogsPanelArgs
{
Filter = "string",
ResourceNames = new[]
{
"string",
},
},
PieChart = new GoogleNative.Monitoring.V1.Inputs.PieChartArgs
{
ChartType = GoogleNative.Monitoring.V1.PieChartChartType.PieChartTypeUnspecified,
DataSets = new[]
{
new GoogleNative.Monitoring.V1.Inputs.PieChartDataSetArgs
{
TimeSeriesQuery = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryArgs
{
OpsAnalyticsQuery = new GoogleNative.Monitoring.V1.Inputs.OpsAnalyticsQueryArgs
{
Sql = "string",
},
OutputFullDuration = false,
PrometheusQuery = "string",
TimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesFilterRatio = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioArgs
{
Denominator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
Numerator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesQueryLanguage = "string",
UnitOverride = "string",
},
MinAlignmentPeriod = "string",
SliceNameTemplate = "string",
},
},
ShowLabels = false,
},
Scorecard = new GoogleNative.Monitoring.V1.Inputs.ScorecardArgs
{
TimeSeriesQuery = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryArgs
{
OpsAnalyticsQuery = new GoogleNative.Monitoring.V1.Inputs.OpsAnalyticsQueryArgs
{
Sql = "string",
},
OutputFullDuration = false,
PrometheusQuery = "string",
TimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesFilterRatio = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioArgs
{
Denominator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
Numerator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesQueryLanguage = "string",
UnitOverride = "string",
},
BlankView = null,
GaugeView = new GoogleNative.Monitoring.V1.Inputs.GaugeViewArgs
{
LowerBound = 0,
UpperBound = 0,
},
SparkChartView = new GoogleNative.Monitoring.V1.Inputs.SparkChartViewArgs
{
SparkChartType = GoogleNative.Monitoring.V1.SparkChartViewSparkChartType.SparkChartTypeUnspecified,
MinAlignmentPeriod = "string",
},
Thresholds = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ThresholdArgs
{
Color = GoogleNative.Monitoring.V1.ThresholdColor.ColorUnspecified,
Direction = GoogleNative.Monitoring.V1.ThresholdDirection.DirectionUnspecified,
Label = "string",
TargetAxis = GoogleNative.Monitoring.V1.ThresholdTargetAxis.TargetAxisUnspecified,
Value = 0,
},
},
},
Text = new GoogleNative.Monitoring.V1.Inputs.TextArgs
{
Content = "string",
Format = GoogleNative.Monitoring.V1.TextFormat.FormatUnspecified,
Style = new GoogleNative.Monitoring.V1.Inputs.TextStyleArgs
{
BackgroundColor = "string",
FontSize = GoogleNative.Monitoring.V1.TextStyleFontSize.FontSizeUnspecified,
HorizontalAlignment = GoogleNative.Monitoring.V1.TextStyleHorizontalAlignment.HorizontalAlignmentUnspecified,
Padding = GoogleNative.Monitoring.V1.TextStylePadding.PaddingSizeUnspecified,
PointerLocation = GoogleNative.Monitoring.V1.TextStylePointerLocation.PointerLocationUnspecified,
TextColor = "string",
VerticalAlignment = GoogleNative.Monitoring.V1.TextStyleVerticalAlignment.VerticalAlignmentUnspecified,
},
},
TimeSeriesTable = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesTableArgs
{
DataSets = new[]
{
new GoogleNative.Monitoring.V1.Inputs.TableDataSetArgs
{
TimeSeriesQuery = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryArgs
{
OpsAnalyticsQuery = new GoogleNative.Monitoring.V1.Inputs.OpsAnalyticsQueryArgs
{
Sql = "string",
},
OutputFullDuration = false,
PrometheusQuery = "string",
TimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesFilterRatio = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioArgs
{
Denominator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
Numerator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesQueryLanguage = "string",
UnitOverride = "string",
},
MinAlignmentPeriod = "string",
TableDisplayOptions = new GoogleNative.Monitoring.V1.Inputs.TableDisplayOptionsArgs
{
ShownColumns = new[]
{
"string",
},
},
TableTemplate = "string",
},
},
ColumnSettings = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ColumnSettingsArgs
{
Column = "string",
Visible = false,
},
},
MetricVisualization = GoogleNative.Monitoring.V1.TimeSeriesTableMetricVisualization.MetricVisualizationUnspecified,
},
Title = "string",
XyChart = new GoogleNative.Monitoring.V1.Inputs.XyChartArgs
{
DataSets = new[]
{
new GoogleNative.Monitoring.V1.Inputs.DataSetArgs
{
TimeSeriesQuery = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryArgs
{
OpsAnalyticsQuery = new GoogleNative.Monitoring.V1.Inputs.OpsAnalyticsQueryArgs
{
Sql = "string",
},
OutputFullDuration = false,
PrometheusQuery = "string",
TimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesFilterRatio = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioArgs
{
Denominator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
Numerator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesQueryLanguage = "string",
UnitOverride = "string",
},
Breakdowns = new[]
{
new GoogleNative.Monitoring.V1.Inputs.BreakdownArgs
{
AggregationFunction = new GoogleNative.Monitoring.V1.Inputs.AggregationFunctionArgs
{
Type = "string",
Parameters = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ParameterArgs
{
DoubleValue = 0,
IntValue = "string",
},
},
},
Column = "string",
Limit = 0,
SortOrder = GoogleNative.Monitoring.V1.BreakdownSortOrder.SortOrderUnspecified,
},
},
Dimensions = new[]
{
new GoogleNative.Monitoring.V1.Inputs.DimensionArgs
{
Column = "string",
ColumnType = "string",
FloatBinSize = 0,
MaxBinCount = 0,
NumericBinSize = 0,
SortColumn = "string",
SortOrder = GoogleNative.Monitoring.V1.DimensionSortOrder.SortOrderUnspecified,
TimeBinSize = "string",
},
},
LegendTemplate = "string",
Measures = new[]
{
new GoogleNative.Monitoring.V1.Inputs.MeasureArgs
{
AggregationFunction = new GoogleNative.Monitoring.V1.Inputs.AggregationFunctionArgs
{
Type = "string",
Parameters = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ParameterArgs
{
DoubleValue = 0,
IntValue = "string",
},
},
},
Column = "string",
},
},
MinAlignmentPeriod = "string",
PlotType = GoogleNative.Monitoring.V1.DataSetPlotType.PlotTypeUnspecified,
TargetAxis = GoogleNative.Monitoring.V1.DataSetTargetAxis.TargetAxisUnspecified,
},
},
ChartOptions = new GoogleNative.Monitoring.V1.Inputs.ChartOptionsArgs
{
DisplayHorizontal = false,
Mode = GoogleNative.Monitoring.V1.ChartOptionsMode.ModeUnspecified,
},
Thresholds = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ThresholdArgs
{
Color = GoogleNative.Monitoring.V1.ThresholdColor.ColorUnspecified,
Direction = GoogleNative.Monitoring.V1.ThresholdDirection.DirectionUnspecified,
Label = "string",
TargetAxis = GoogleNative.Monitoring.V1.ThresholdTargetAxis.TargetAxisUnspecified,
Value = 0,
},
},
TimeshiftDuration = "string",
XAxis = new GoogleNative.Monitoring.V1.Inputs.AxisArgs
{
Label = "string",
Scale = GoogleNative.Monitoring.V1.AxisScale.ScaleUnspecified,
},
Y2Axis = new GoogleNative.Monitoring.V1.Inputs.AxisArgs
{
Label = "string",
Scale = GoogleNative.Monitoring.V1.AxisScale.ScaleUnspecified,
},
YAxis = new GoogleNative.Monitoring.V1.Inputs.AxisArgs
{
Label = "string",
Scale = GoogleNative.Monitoring.V1.AxisScale.ScaleUnspecified,
},
},
},
Width = 0,
XPos = 0,
YPos = 0,
},
},
},
Name = "string",
Project = "string",
RowLayout = new GoogleNative.Monitoring.V1.Inputs.RowLayoutArgs
{
Rows = new[]
{
new GoogleNative.Monitoring.V1.Inputs.RowArgs
{
Weight = "string",
Widgets = new[]
{
new GoogleNative.Monitoring.V1.Inputs.WidgetArgs
{
AlertChart = new GoogleNative.Monitoring.V1.Inputs.AlertChartArgs
{
Name = "string",
},
Blank = null,
CollapsibleGroup = new GoogleNative.Monitoring.V1.Inputs.CollapsibleGroupArgs
{
Collapsed = false,
},
ErrorReportingPanel = new GoogleNative.Monitoring.V1.Inputs.ErrorReportingPanelArgs
{
ProjectNames = new[]
{
"string",
},
Services = new[]
{
"string",
},
Versions = new[]
{
"string",
},
},
Id = "string",
IncidentList = new GoogleNative.Monitoring.V1.Inputs.IncidentListArgs
{
MonitoredResources = new[]
{
new GoogleNative.Monitoring.V1.Inputs.MonitoredResourceArgs
{
Labels =
{
{ "string", "string" },
},
Type = "string",
},
},
PolicyNames = new[]
{
"string",
},
},
LogsPanel = new GoogleNative.Monitoring.V1.Inputs.LogsPanelArgs
{
Filter = "string",
ResourceNames = new[]
{
"string",
},
},
PieChart = new GoogleNative.Monitoring.V1.Inputs.PieChartArgs
{
ChartType = GoogleNative.Monitoring.V1.PieChartChartType.PieChartTypeUnspecified,
DataSets = new[]
{
new GoogleNative.Monitoring.V1.Inputs.PieChartDataSetArgs
{
TimeSeriesQuery = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryArgs
{
OpsAnalyticsQuery = new GoogleNative.Monitoring.V1.Inputs.OpsAnalyticsQueryArgs
{
Sql = "string",
},
OutputFullDuration = false,
PrometheusQuery = "string",
TimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesFilterRatio = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioArgs
{
Denominator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
Numerator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesQueryLanguage = "string",
UnitOverride = "string",
},
MinAlignmentPeriod = "string",
SliceNameTemplate = "string",
},
},
ShowLabels = false,
},
Scorecard = new GoogleNative.Monitoring.V1.Inputs.ScorecardArgs
{
TimeSeriesQuery = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryArgs
{
OpsAnalyticsQuery = new GoogleNative.Monitoring.V1.Inputs.OpsAnalyticsQueryArgs
{
Sql = "string",
},
OutputFullDuration = false,
PrometheusQuery = "string",
TimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesFilterRatio = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioArgs
{
Denominator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
Numerator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesQueryLanguage = "string",
UnitOverride = "string",
},
BlankView = null,
GaugeView = new GoogleNative.Monitoring.V1.Inputs.GaugeViewArgs
{
LowerBound = 0,
UpperBound = 0,
},
SparkChartView = new GoogleNative.Monitoring.V1.Inputs.SparkChartViewArgs
{
SparkChartType = GoogleNative.Monitoring.V1.SparkChartViewSparkChartType.SparkChartTypeUnspecified,
MinAlignmentPeriod = "string",
},
Thresholds = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ThresholdArgs
{
Color = GoogleNative.Monitoring.V1.ThresholdColor.ColorUnspecified,
Direction = GoogleNative.Monitoring.V1.ThresholdDirection.DirectionUnspecified,
Label = "string",
TargetAxis = GoogleNative.Monitoring.V1.ThresholdTargetAxis.TargetAxisUnspecified,
Value = 0,
},
},
},
Text = new GoogleNative.Monitoring.V1.Inputs.TextArgs
{
Content = "string",
Format = GoogleNative.Monitoring.V1.TextFormat.FormatUnspecified,
Style = new GoogleNative.Monitoring.V1.Inputs.TextStyleArgs
{
BackgroundColor = "string",
FontSize = GoogleNative.Monitoring.V1.TextStyleFontSize.FontSizeUnspecified,
HorizontalAlignment = GoogleNative.Monitoring.V1.TextStyleHorizontalAlignment.HorizontalAlignmentUnspecified,
Padding = GoogleNative.Monitoring.V1.TextStylePadding.PaddingSizeUnspecified,
PointerLocation = GoogleNative.Monitoring.V1.TextStylePointerLocation.PointerLocationUnspecified,
TextColor = "string",
VerticalAlignment = GoogleNative.Monitoring.V1.TextStyleVerticalAlignment.VerticalAlignmentUnspecified,
},
},
TimeSeriesTable = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesTableArgs
{
DataSets = new[]
{
new GoogleNative.Monitoring.V1.Inputs.TableDataSetArgs
{
TimeSeriesQuery = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryArgs
{
OpsAnalyticsQuery = new GoogleNative.Monitoring.V1.Inputs.OpsAnalyticsQueryArgs
{
Sql = "string",
},
OutputFullDuration = false,
PrometheusQuery = "string",
TimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesFilterRatio = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioArgs
{
Denominator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
Numerator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesQueryLanguage = "string",
UnitOverride = "string",
},
MinAlignmentPeriod = "string",
TableDisplayOptions = new GoogleNative.Monitoring.V1.Inputs.TableDisplayOptionsArgs
{
ShownColumns = new[]
{
"string",
},
},
TableTemplate = "string",
},
},
ColumnSettings = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ColumnSettingsArgs
{
Column = "string",
Visible = false,
},
},
MetricVisualization = GoogleNative.Monitoring.V1.TimeSeriesTableMetricVisualization.MetricVisualizationUnspecified,
},
Title = "string",
XyChart = new GoogleNative.Monitoring.V1.Inputs.XyChartArgs
{
DataSets = new[]
{
new GoogleNative.Monitoring.V1.Inputs.DataSetArgs
{
TimeSeriesQuery = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesQueryArgs
{
OpsAnalyticsQuery = new GoogleNative.Monitoring.V1.Inputs.OpsAnalyticsQueryArgs
{
Sql = "string",
},
OutputFullDuration = false,
PrometheusQuery = "string",
TimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesFilterRatio = new GoogleNative.Monitoring.V1.Inputs.TimeSeriesFilterRatioArgs
{
Denominator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
Numerator = new GoogleNative.Monitoring.V1.Inputs.RatioPartArgs
{
Filter = "string",
Aggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
PickTimeSeriesFilter = new GoogleNative.Monitoring.V1.Inputs.PickTimeSeriesFilterArgs
{
Direction = GoogleNative.Monitoring.V1.PickTimeSeriesFilterDirection.DirectionUnspecified,
NumTimeSeries = 0,
RankingMethod = GoogleNative.Monitoring.V1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
SecondaryAggregation = new GoogleNative.Monitoring.V1.Inputs.AggregationArgs
{
AlignmentPeriod = "string",
CrossSeriesReducer = GoogleNative.Monitoring.V1.AggregationCrossSeriesReducer.ReduceNone,
GroupByFields = new[]
{
"string",
},
PerSeriesAligner = GoogleNative.Monitoring.V1.AggregationPerSeriesAligner.AlignNone,
},
},
TimeSeriesQueryLanguage = "string",
UnitOverride = "string",
},
Breakdowns = new[]
{
new GoogleNative.Monitoring.V1.Inputs.BreakdownArgs
{
AggregationFunction = new GoogleNative.Monitoring.V1.Inputs.AggregationFunctionArgs
{
Type = "string",
Parameters = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ParameterArgs
{
DoubleValue = 0,
IntValue = "string",
},
},
},
Column = "string",
Limit = 0,
SortOrder = GoogleNative.Monitoring.V1.BreakdownSortOrder.SortOrderUnspecified,
},
},
Dimensions = new[]
{
new GoogleNative.Monitoring.V1.Inputs.DimensionArgs
{
Column = "string",
ColumnType = "string",
FloatBinSize = 0,
MaxBinCount = 0,
NumericBinSize = 0,
SortColumn = "string",
SortOrder = GoogleNative.Monitoring.V1.DimensionSortOrder.SortOrderUnspecified,
TimeBinSize = "string",
},
},
LegendTemplate = "string",
Measures = new[]
{
new GoogleNative.Monitoring.V1.Inputs.MeasureArgs
{
AggregationFunction = new GoogleNative.Monitoring.V1.Inputs.AggregationFunctionArgs
{
Type = "string",
Parameters = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ParameterArgs
{
DoubleValue = 0,
IntValue = "string",
},
},
},
Column = "string",
},
},
MinAlignmentPeriod = "string",
PlotType = GoogleNative.Monitoring.V1.DataSetPlotType.PlotTypeUnspecified,
TargetAxis = GoogleNative.Monitoring.V1.DataSetTargetAxis.TargetAxisUnspecified,
},
},
ChartOptions = new GoogleNative.Monitoring.V1.Inputs.ChartOptionsArgs
{
DisplayHorizontal = false,
Mode = GoogleNative.Monitoring.V1.ChartOptionsMode.ModeUnspecified,
},
Thresholds = new[]
{
new GoogleNative.Monitoring.V1.Inputs.ThresholdArgs
{
Color = GoogleNative.Monitoring.V1.ThresholdColor.ColorUnspecified,
Direction = GoogleNative.Monitoring.V1.ThresholdDirection.DirectionUnspecified,
Label = "string",
TargetAxis = GoogleNative.Monitoring.V1.ThresholdTargetAxis.TargetAxisUnspecified,
Value = 0,
},
},
TimeshiftDuration = "string",
XAxis = new GoogleNative.Monitoring.V1.Inputs.AxisArgs
{
Label = "string",
Scale = GoogleNative.Monitoring.V1.AxisScale.ScaleUnspecified,
},
Y2Axis = new GoogleNative.Monitoring.V1.Inputs.AxisArgs
{
Label = "string",
Scale = GoogleNative.Monitoring.V1.AxisScale.ScaleUnspecified,
},
YAxis = new GoogleNative.Monitoring.V1.Inputs.AxisArgs
{
Label = "string",
Scale = GoogleNative.Monitoring.V1.AxisScale.ScaleUnspecified,
},
},
},
},
},
},
},
});
example, err := monitoring.NewDashboard(ctx, "dashboardResource", &monitoring.DashboardArgs{
DisplayName: pulumi.String("string"),
ColumnLayout: &monitoring.ColumnLayoutArgs{
Columns: monitoring.ColumnArray{
&monitoring.ColumnArgs{
Weight: pulumi.String("string"),
Widgets: monitoring.WidgetArray{
&monitoring.WidgetArgs{
AlertChart: &monitoring.AlertChartArgs{
Name: pulumi.String("string"),
},
Blank: &monitoring.EmptyArgs{},
CollapsibleGroup: &monitoring.CollapsibleGroupArgs{
Collapsed: pulumi.Bool(false),
},
ErrorReportingPanel: &monitoring.ErrorReportingPanelArgs{
ProjectNames: pulumi.StringArray{
pulumi.String("string"),
},
Services: pulumi.StringArray{
pulumi.String("string"),
},
Versions: pulumi.StringArray{
pulumi.String("string"),
},
},
Id: pulumi.String("string"),
IncidentList: &monitoring.IncidentListArgs{
MonitoredResources: monitoring.MonitoredResourceArray{
&monitoring.MonitoredResourceArgs{
Labels: pulumi.StringMap{
"string": pulumi.String("string"),
},
Type: pulumi.String("string"),
},
},
PolicyNames: pulumi.StringArray{
pulumi.String("string"),
},
},
LogsPanel: &monitoring.LogsPanelArgs{
Filter: pulumi.String("string"),
ResourceNames: pulumi.StringArray{
pulumi.String("string"),
},
},
PieChart: &monitoring.PieChartArgs{
ChartType: monitoring.PieChartChartTypePieChartTypeUnspecified,
DataSets: monitoring.PieChartDataSetArray{
&monitoring.PieChartDataSetArgs{
TimeSeriesQuery: &monitoring.TimeSeriesQueryArgs{
OpsAnalyticsQuery: &monitoring.OpsAnalyticsQueryArgs{
Sql: pulumi.String("string"),
},
OutputFullDuration: pulumi.Bool(false),
PrometheusQuery: pulumi.String("string"),
TimeSeriesFilter: &monitoring.TimeSeriesFilterArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesFilterRatio: &monitoring.TimeSeriesFilterRatioArgs{
Denominator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
Numerator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesQueryLanguage: pulumi.String("string"),
UnitOverride: pulumi.String("string"),
},
MinAlignmentPeriod: pulumi.String("string"),
SliceNameTemplate: pulumi.String("string"),
},
},
ShowLabels: pulumi.Bool(false),
},
Scorecard: &monitoring.ScorecardArgs{
TimeSeriesQuery: &monitoring.TimeSeriesQueryArgs{
OpsAnalyticsQuery: &monitoring.OpsAnalyticsQueryArgs{
Sql: pulumi.String("string"),
},
OutputFullDuration: pulumi.Bool(false),
PrometheusQuery: pulumi.String("string"),
TimeSeriesFilter: &monitoring.TimeSeriesFilterArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesFilterRatio: &monitoring.TimeSeriesFilterRatioArgs{
Denominator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
Numerator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesQueryLanguage: pulumi.String("string"),
UnitOverride: pulumi.String("string"),
},
BlankView: &monitoring.EmptyArgs{},
GaugeView: &monitoring.GaugeViewArgs{
LowerBound: pulumi.Float64(0),
UpperBound: pulumi.Float64(0),
},
SparkChartView: &monitoring.SparkChartViewArgs{
SparkChartType: monitoring.SparkChartViewSparkChartTypeSparkChartTypeUnspecified,
MinAlignmentPeriod: pulumi.String("string"),
},
Thresholds: monitoring.ThresholdArray{
&monitoring.ThresholdArgs{
Color: monitoring.ThresholdColorColorUnspecified,
Direction: monitoring.ThresholdDirectionDirectionUnspecified,
Label: pulumi.String("string"),
TargetAxis: monitoring.ThresholdTargetAxisTargetAxisUnspecified,
Value: pulumi.Float64(0),
},
},
},
Text: &monitoring.TextArgs{
Content: pulumi.String("string"),
Format: monitoring.TextFormatFormatUnspecified,
Style: &monitoring.TextStyleArgs{
BackgroundColor: pulumi.String("string"),
FontSize: monitoring.TextStyleFontSizeFontSizeUnspecified,
HorizontalAlignment: monitoring.TextStyleHorizontalAlignmentHorizontalAlignmentUnspecified,
Padding: monitoring.TextStylePaddingPaddingSizeUnspecified,
PointerLocation: monitoring.TextStylePointerLocationPointerLocationUnspecified,
TextColor: pulumi.String("string"),
VerticalAlignment: monitoring.TextStyleVerticalAlignmentVerticalAlignmentUnspecified,
},
},
TimeSeriesTable: &monitoring.TimeSeriesTableArgs{
DataSets: monitoring.TableDataSetArray{
&monitoring.TableDataSetArgs{
TimeSeriesQuery: &monitoring.TimeSeriesQueryArgs{
OpsAnalyticsQuery: &monitoring.OpsAnalyticsQueryArgs{
Sql: pulumi.String("string"),
},
OutputFullDuration: pulumi.Bool(false),
PrometheusQuery: pulumi.String("string"),
TimeSeriesFilter: &monitoring.TimeSeriesFilterArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesFilterRatio: &monitoring.TimeSeriesFilterRatioArgs{
Denominator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
Numerator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesQueryLanguage: pulumi.String("string"),
UnitOverride: pulumi.String("string"),
},
MinAlignmentPeriod: pulumi.String("string"),
TableDisplayOptions: &monitoring.TableDisplayOptionsArgs{
ShownColumns: pulumi.StringArray{
pulumi.String("string"),
},
},
TableTemplate: pulumi.String("string"),
},
},
ColumnSettings: monitoring.ColumnSettingsArray{
&monitoring.ColumnSettingsArgs{
Column: pulumi.String("string"),
Visible: pulumi.Bool(false),
},
},
MetricVisualization: monitoring.TimeSeriesTableMetricVisualizationMetricVisualizationUnspecified,
},
Title: pulumi.String("string"),
XyChart: &monitoring.XyChartArgs{
DataSets: monitoring.DataSetArray{
&monitoring.DataSetArgs{
TimeSeriesQuery: &monitoring.TimeSeriesQueryArgs{
OpsAnalyticsQuery: &monitoring.OpsAnalyticsQueryArgs{
Sql: pulumi.String("string"),
},
OutputFullDuration: pulumi.Bool(false),
PrometheusQuery: pulumi.String("string"),
TimeSeriesFilter: &monitoring.TimeSeriesFilterArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesFilterRatio: &monitoring.TimeSeriesFilterRatioArgs{
Denominator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
Numerator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesQueryLanguage: pulumi.String("string"),
UnitOverride: pulumi.String("string"),
},
Breakdowns: monitoring.BreakdownArray{
&monitoring.BreakdownArgs{
AggregationFunction: &monitoring.AggregationFunctionArgs{
Type: pulumi.String("string"),
Parameters: monitoring.ParameterArray{
&monitoring.ParameterArgs{
DoubleValue: pulumi.Float64(0),
IntValue: pulumi.String("string"),
},
},
},
Column: pulumi.String("string"),
Limit: pulumi.Int(0),
SortOrder: monitoring.BreakdownSortOrderSortOrderUnspecified,
},
},
Dimensions: monitoring.DimensionArray{
&monitoring.DimensionArgs{
Column: pulumi.String("string"),
ColumnType: pulumi.String("string"),
FloatBinSize: pulumi.Float64(0),
MaxBinCount: pulumi.Int(0),
NumericBinSize: pulumi.Int(0),
SortColumn: pulumi.String("string"),
SortOrder: monitoring.DimensionSortOrderSortOrderUnspecified,
TimeBinSize: pulumi.String("string"),
},
},
LegendTemplate: pulumi.String("string"),
Measures: monitoring.MeasureArray{
&monitoring.MeasureArgs{
AggregationFunction: &monitoring.AggregationFunctionArgs{
Type: pulumi.String("string"),
Parameters: monitoring.ParameterArray{
&monitoring.ParameterArgs{
DoubleValue: pulumi.Float64(0),
IntValue: pulumi.String("string"),
},
},
},
Column: pulumi.String("string"),
},
},
MinAlignmentPeriod: pulumi.String("string"),
PlotType: monitoring.DataSetPlotTypePlotTypeUnspecified,
TargetAxis: monitoring.DataSetTargetAxisTargetAxisUnspecified,
},
},
ChartOptions: &monitoring.ChartOptionsArgs{
DisplayHorizontal: pulumi.Bool(false),
Mode: monitoring.ChartOptionsModeModeUnspecified,
},
Thresholds: monitoring.ThresholdArray{
&monitoring.ThresholdArgs{
Color: monitoring.ThresholdColorColorUnspecified,
Direction: monitoring.ThresholdDirectionDirectionUnspecified,
Label: pulumi.String("string"),
TargetAxis: monitoring.ThresholdTargetAxisTargetAxisUnspecified,
Value: pulumi.Float64(0),
},
},
TimeshiftDuration: pulumi.String("string"),
XAxis: &monitoring.AxisArgs{
Label: pulumi.String("string"),
Scale: monitoring.AxisScaleScaleUnspecified,
},
Y2Axis: &monitoring.AxisArgs{
Label: pulumi.String("string"),
Scale: monitoring.AxisScaleScaleUnspecified,
},
YAxis: &monitoring.AxisArgs{
Label: pulumi.String("string"),
Scale: monitoring.AxisScaleScaleUnspecified,
},
},
},
},
},
},
},
DashboardFilters: monitoring.DashboardFilterArray{
&monitoring.DashboardFilterArgs{
LabelKey: pulumi.String("string"),
FilterType: monitoring.DashboardFilterFilterTypeFilterTypeUnspecified,
StringValue: pulumi.String("string"),
TemplateVariable: pulumi.String("string"),
},
},
Etag: pulumi.String("string"),
GridLayout: &monitoring.GridLayoutArgs{
Columns: pulumi.String("string"),
Widgets: monitoring.WidgetArray{
&monitoring.WidgetArgs{
AlertChart: &monitoring.AlertChartArgs{
Name: pulumi.String("string"),
},
Blank: &monitoring.EmptyArgs{},
CollapsibleGroup: &monitoring.CollapsibleGroupArgs{
Collapsed: pulumi.Bool(false),
},
ErrorReportingPanel: &monitoring.ErrorReportingPanelArgs{
ProjectNames: pulumi.StringArray{
pulumi.String("string"),
},
Services: pulumi.StringArray{
pulumi.String("string"),
},
Versions: pulumi.StringArray{
pulumi.String("string"),
},
},
Id: pulumi.String("string"),
IncidentList: &monitoring.IncidentListArgs{
MonitoredResources: monitoring.MonitoredResourceArray{
&monitoring.MonitoredResourceArgs{
Labels: pulumi.StringMap{
"string": pulumi.String("string"),
},
Type: pulumi.String("string"),
},
},
PolicyNames: pulumi.StringArray{
pulumi.String("string"),
},
},
LogsPanel: &monitoring.LogsPanelArgs{
Filter: pulumi.String("string"),
ResourceNames: pulumi.StringArray{
pulumi.String("string"),
},
},
PieChart: &monitoring.PieChartArgs{
ChartType: monitoring.PieChartChartTypePieChartTypeUnspecified,
DataSets: monitoring.PieChartDataSetArray{
&monitoring.PieChartDataSetArgs{
TimeSeriesQuery: &monitoring.TimeSeriesQueryArgs{
OpsAnalyticsQuery: &monitoring.OpsAnalyticsQueryArgs{
Sql: pulumi.String("string"),
},
OutputFullDuration: pulumi.Bool(false),
PrometheusQuery: pulumi.String("string"),
TimeSeriesFilter: &monitoring.TimeSeriesFilterArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesFilterRatio: &monitoring.TimeSeriesFilterRatioArgs{
Denominator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
Numerator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesQueryLanguage: pulumi.String("string"),
UnitOverride: pulumi.String("string"),
},
MinAlignmentPeriod: pulumi.String("string"),
SliceNameTemplate: pulumi.String("string"),
},
},
ShowLabels: pulumi.Bool(false),
},
Scorecard: &monitoring.ScorecardArgs{
TimeSeriesQuery: &monitoring.TimeSeriesQueryArgs{
OpsAnalyticsQuery: &monitoring.OpsAnalyticsQueryArgs{
Sql: pulumi.String("string"),
},
OutputFullDuration: pulumi.Bool(false),
PrometheusQuery: pulumi.String("string"),
TimeSeriesFilter: &monitoring.TimeSeriesFilterArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesFilterRatio: &monitoring.TimeSeriesFilterRatioArgs{
Denominator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
Numerator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesQueryLanguage: pulumi.String("string"),
UnitOverride: pulumi.String("string"),
},
BlankView: &monitoring.EmptyArgs{},
GaugeView: &monitoring.GaugeViewArgs{
LowerBound: pulumi.Float64(0),
UpperBound: pulumi.Float64(0),
},
SparkChartView: &monitoring.SparkChartViewArgs{
SparkChartType: monitoring.SparkChartViewSparkChartTypeSparkChartTypeUnspecified,
MinAlignmentPeriod: pulumi.String("string"),
},
Thresholds: monitoring.ThresholdArray{
&monitoring.ThresholdArgs{
Color: monitoring.ThresholdColorColorUnspecified,
Direction: monitoring.ThresholdDirectionDirectionUnspecified,
Label: pulumi.String("string"),
TargetAxis: monitoring.ThresholdTargetAxisTargetAxisUnspecified,
Value: pulumi.Float64(0),
},
},
},
Text: &monitoring.TextArgs{
Content: pulumi.String("string"),
Format: monitoring.TextFormatFormatUnspecified,
Style: &monitoring.TextStyleArgs{
BackgroundColor: pulumi.String("string"),
FontSize: monitoring.TextStyleFontSizeFontSizeUnspecified,
HorizontalAlignment: monitoring.TextStyleHorizontalAlignmentHorizontalAlignmentUnspecified,
Padding: monitoring.TextStylePaddingPaddingSizeUnspecified,
PointerLocation: monitoring.TextStylePointerLocationPointerLocationUnspecified,
TextColor: pulumi.String("string"),
VerticalAlignment: monitoring.TextStyleVerticalAlignmentVerticalAlignmentUnspecified,
},
},
TimeSeriesTable: &monitoring.TimeSeriesTableArgs{
DataSets: monitoring.TableDataSetArray{
&monitoring.TableDataSetArgs{
TimeSeriesQuery: &monitoring.TimeSeriesQueryArgs{
OpsAnalyticsQuery: &monitoring.OpsAnalyticsQueryArgs{
Sql: pulumi.String("string"),
},
OutputFullDuration: pulumi.Bool(false),
PrometheusQuery: pulumi.String("string"),
TimeSeriesFilter: &monitoring.TimeSeriesFilterArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesFilterRatio: &monitoring.TimeSeriesFilterRatioArgs{
Denominator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
Numerator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesQueryLanguage: pulumi.String("string"),
UnitOverride: pulumi.String("string"),
},
MinAlignmentPeriod: pulumi.String("string"),
TableDisplayOptions: &monitoring.TableDisplayOptionsArgs{
ShownColumns: pulumi.StringArray{
pulumi.String("string"),
},
},
TableTemplate: pulumi.String("string"),
},
},
ColumnSettings: monitoring.ColumnSettingsArray{
&monitoring.ColumnSettingsArgs{
Column: pulumi.String("string"),
Visible: pulumi.Bool(false),
},
},
MetricVisualization: monitoring.TimeSeriesTableMetricVisualizationMetricVisualizationUnspecified,
},
Title: pulumi.String("string"),
XyChart: &monitoring.XyChartArgs{
DataSets: monitoring.DataSetArray{
&monitoring.DataSetArgs{
TimeSeriesQuery: &monitoring.TimeSeriesQueryArgs{
OpsAnalyticsQuery: &monitoring.OpsAnalyticsQueryArgs{
Sql: pulumi.String("string"),
},
OutputFullDuration: pulumi.Bool(false),
PrometheusQuery: pulumi.String("string"),
TimeSeriesFilter: &monitoring.TimeSeriesFilterArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesFilterRatio: &monitoring.TimeSeriesFilterRatioArgs{
Denominator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
Numerator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesQueryLanguage: pulumi.String("string"),
UnitOverride: pulumi.String("string"),
},
Breakdowns: monitoring.BreakdownArray{
&monitoring.BreakdownArgs{
AggregationFunction: &monitoring.AggregationFunctionArgs{
Type: pulumi.String("string"),
Parameters: monitoring.ParameterArray{
&monitoring.ParameterArgs{
DoubleValue: pulumi.Float64(0),
IntValue: pulumi.String("string"),
},
},
},
Column: pulumi.String("string"),
Limit: pulumi.Int(0),
SortOrder: monitoring.BreakdownSortOrderSortOrderUnspecified,
},
},
Dimensions: monitoring.DimensionArray{
&monitoring.DimensionArgs{
Column: pulumi.String("string"),
ColumnType: pulumi.String("string"),
FloatBinSize: pulumi.Float64(0),
MaxBinCount: pulumi.Int(0),
NumericBinSize: pulumi.Int(0),
SortColumn: pulumi.String("string"),
SortOrder: monitoring.DimensionSortOrderSortOrderUnspecified,
TimeBinSize: pulumi.String("string"),
},
},
LegendTemplate: pulumi.String("string"),
Measures: monitoring.MeasureArray{
&monitoring.MeasureArgs{
AggregationFunction: &monitoring.AggregationFunctionArgs{
Type: pulumi.String("string"),
Parameters: monitoring.ParameterArray{
&monitoring.ParameterArgs{
DoubleValue: pulumi.Float64(0),
IntValue: pulumi.String("string"),
},
},
},
Column: pulumi.String("string"),
},
},
MinAlignmentPeriod: pulumi.String("string"),
PlotType: monitoring.DataSetPlotTypePlotTypeUnspecified,
TargetAxis: monitoring.DataSetTargetAxisTargetAxisUnspecified,
},
},
ChartOptions: &monitoring.ChartOptionsArgs{
DisplayHorizontal: pulumi.Bool(false),
Mode: monitoring.ChartOptionsModeModeUnspecified,
},
Thresholds: monitoring.ThresholdArray{
&monitoring.ThresholdArgs{
Color: monitoring.ThresholdColorColorUnspecified,
Direction: monitoring.ThresholdDirectionDirectionUnspecified,
Label: pulumi.String("string"),
TargetAxis: monitoring.ThresholdTargetAxisTargetAxisUnspecified,
Value: pulumi.Float64(0),
},
},
TimeshiftDuration: pulumi.String("string"),
XAxis: &monitoring.AxisArgs{
Label: pulumi.String("string"),
Scale: monitoring.AxisScaleScaleUnspecified,
},
Y2Axis: &monitoring.AxisArgs{
Label: pulumi.String("string"),
Scale: monitoring.AxisScaleScaleUnspecified,
},
YAxis: &monitoring.AxisArgs{
Label: pulumi.String("string"),
Scale: monitoring.AxisScaleScaleUnspecified,
},
},
},
},
},
Labels: pulumi.StringMap{
"string": pulumi.String("string"),
},
MosaicLayout: &monitoring.MosaicLayoutArgs{
Columns: pulumi.Int(0),
Tiles: monitoring.TileArray{
&monitoring.TileArgs{
Height: pulumi.Int(0),
Widget: &monitoring.WidgetArgs{
AlertChart: &monitoring.AlertChartArgs{
Name: pulumi.String("string"),
},
Blank: &monitoring.EmptyArgs{},
CollapsibleGroup: &monitoring.CollapsibleGroupArgs{
Collapsed: pulumi.Bool(false),
},
ErrorReportingPanel: &monitoring.ErrorReportingPanelArgs{
ProjectNames: pulumi.StringArray{
pulumi.String("string"),
},
Services: pulumi.StringArray{
pulumi.String("string"),
},
Versions: pulumi.StringArray{
pulumi.String("string"),
},
},
Id: pulumi.String("string"),
IncidentList: &monitoring.IncidentListArgs{
MonitoredResources: monitoring.MonitoredResourceArray{
&monitoring.MonitoredResourceArgs{
Labels: pulumi.StringMap{
"string": pulumi.String("string"),
},
Type: pulumi.String("string"),
},
},
PolicyNames: pulumi.StringArray{
pulumi.String("string"),
},
},
LogsPanel: &monitoring.LogsPanelArgs{
Filter: pulumi.String("string"),
ResourceNames: pulumi.StringArray{
pulumi.String("string"),
},
},
PieChart: &monitoring.PieChartArgs{
ChartType: monitoring.PieChartChartTypePieChartTypeUnspecified,
DataSets: monitoring.PieChartDataSetArray{
&monitoring.PieChartDataSetArgs{
TimeSeriesQuery: &monitoring.TimeSeriesQueryArgs{
OpsAnalyticsQuery: &monitoring.OpsAnalyticsQueryArgs{
Sql: pulumi.String("string"),
},
OutputFullDuration: pulumi.Bool(false),
PrometheusQuery: pulumi.String("string"),
TimeSeriesFilter: &monitoring.TimeSeriesFilterArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesFilterRatio: &monitoring.TimeSeriesFilterRatioArgs{
Denominator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
Numerator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesQueryLanguage: pulumi.String("string"),
UnitOverride: pulumi.String("string"),
},
MinAlignmentPeriod: pulumi.String("string"),
SliceNameTemplate: pulumi.String("string"),
},
},
ShowLabels: pulumi.Bool(false),
},
Scorecard: &monitoring.ScorecardArgs{
TimeSeriesQuery: &monitoring.TimeSeriesQueryArgs{
OpsAnalyticsQuery: &monitoring.OpsAnalyticsQueryArgs{
Sql: pulumi.String("string"),
},
OutputFullDuration: pulumi.Bool(false),
PrometheusQuery: pulumi.String("string"),
TimeSeriesFilter: &monitoring.TimeSeriesFilterArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesFilterRatio: &monitoring.TimeSeriesFilterRatioArgs{
Denominator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
Numerator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesQueryLanguage: pulumi.String("string"),
UnitOverride: pulumi.String("string"),
},
BlankView: &monitoring.EmptyArgs{},
GaugeView: &monitoring.GaugeViewArgs{
LowerBound: pulumi.Float64(0),
UpperBound: pulumi.Float64(0),
},
SparkChartView: &monitoring.SparkChartViewArgs{
SparkChartType: monitoring.SparkChartViewSparkChartTypeSparkChartTypeUnspecified,
MinAlignmentPeriod: pulumi.String("string"),
},
Thresholds: monitoring.ThresholdArray{
&monitoring.ThresholdArgs{
Color: monitoring.ThresholdColorColorUnspecified,
Direction: monitoring.ThresholdDirectionDirectionUnspecified,
Label: pulumi.String("string"),
TargetAxis: monitoring.ThresholdTargetAxisTargetAxisUnspecified,
Value: pulumi.Float64(0),
},
},
},
Text: &monitoring.TextArgs{
Content: pulumi.String("string"),
Format: monitoring.TextFormatFormatUnspecified,
Style: &monitoring.TextStyleArgs{
BackgroundColor: pulumi.String("string"),
FontSize: monitoring.TextStyleFontSizeFontSizeUnspecified,
HorizontalAlignment: monitoring.TextStyleHorizontalAlignmentHorizontalAlignmentUnspecified,
Padding: monitoring.TextStylePaddingPaddingSizeUnspecified,
PointerLocation: monitoring.TextStylePointerLocationPointerLocationUnspecified,
TextColor: pulumi.String("string"),
VerticalAlignment: monitoring.TextStyleVerticalAlignmentVerticalAlignmentUnspecified,
},
},
TimeSeriesTable: &monitoring.TimeSeriesTableArgs{
DataSets: monitoring.TableDataSetArray{
&monitoring.TableDataSetArgs{
TimeSeriesQuery: &monitoring.TimeSeriesQueryArgs{
OpsAnalyticsQuery: &monitoring.OpsAnalyticsQueryArgs{
Sql: pulumi.String("string"),
},
OutputFullDuration: pulumi.Bool(false),
PrometheusQuery: pulumi.String("string"),
TimeSeriesFilter: &monitoring.TimeSeriesFilterArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesFilterRatio: &monitoring.TimeSeriesFilterRatioArgs{
Denominator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
Numerator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesQueryLanguage: pulumi.String("string"),
UnitOverride: pulumi.String("string"),
},
MinAlignmentPeriod: pulumi.String("string"),
TableDisplayOptions: &monitoring.TableDisplayOptionsArgs{
ShownColumns: pulumi.StringArray{
pulumi.String("string"),
},
},
TableTemplate: pulumi.String("string"),
},
},
ColumnSettings: monitoring.ColumnSettingsArray{
&monitoring.ColumnSettingsArgs{
Column: pulumi.String("string"),
Visible: pulumi.Bool(false),
},
},
MetricVisualization: monitoring.TimeSeriesTableMetricVisualizationMetricVisualizationUnspecified,
},
Title: pulumi.String("string"),
XyChart: &monitoring.XyChartArgs{
DataSets: monitoring.DataSetArray{
&monitoring.DataSetArgs{
TimeSeriesQuery: &monitoring.TimeSeriesQueryArgs{
OpsAnalyticsQuery: &monitoring.OpsAnalyticsQueryArgs{
Sql: pulumi.String("string"),
},
OutputFullDuration: pulumi.Bool(false),
PrometheusQuery: pulumi.String("string"),
TimeSeriesFilter: &monitoring.TimeSeriesFilterArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesFilterRatio: &monitoring.TimeSeriesFilterRatioArgs{
Denominator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
Numerator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesQueryLanguage: pulumi.String("string"),
UnitOverride: pulumi.String("string"),
},
Breakdowns: monitoring.BreakdownArray{
&monitoring.BreakdownArgs{
AggregationFunction: &monitoring.AggregationFunctionArgs{
Type: pulumi.String("string"),
Parameters: monitoring.ParameterArray{
&monitoring.ParameterArgs{
DoubleValue: pulumi.Float64(0),
IntValue: pulumi.String("string"),
},
},
},
Column: pulumi.String("string"),
Limit: pulumi.Int(0),
SortOrder: monitoring.BreakdownSortOrderSortOrderUnspecified,
},
},
Dimensions: monitoring.DimensionArray{
&monitoring.DimensionArgs{
Column: pulumi.String("string"),
ColumnType: pulumi.String("string"),
FloatBinSize: pulumi.Float64(0),
MaxBinCount: pulumi.Int(0),
NumericBinSize: pulumi.Int(0),
SortColumn: pulumi.String("string"),
SortOrder: monitoring.DimensionSortOrderSortOrderUnspecified,
TimeBinSize: pulumi.String("string"),
},
},
LegendTemplate: pulumi.String("string"),
Measures: monitoring.MeasureArray{
&monitoring.MeasureArgs{
AggregationFunction: &monitoring.AggregationFunctionArgs{
Type: pulumi.String("string"),
Parameters: monitoring.ParameterArray{
&monitoring.ParameterArgs{
DoubleValue: pulumi.Float64(0),
IntValue: pulumi.String("string"),
},
},
},
Column: pulumi.String("string"),
},
},
MinAlignmentPeriod: pulumi.String("string"),
PlotType: monitoring.DataSetPlotTypePlotTypeUnspecified,
TargetAxis: monitoring.DataSetTargetAxisTargetAxisUnspecified,
},
},
ChartOptions: &monitoring.ChartOptionsArgs{
DisplayHorizontal: pulumi.Bool(false),
Mode: monitoring.ChartOptionsModeModeUnspecified,
},
Thresholds: monitoring.ThresholdArray{
&monitoring.ThresholdArgs{
Color: monitoring.ThresholdColorColorUnspecified,
Direction: monitoring.ThresholdDirectionDirectionUnspecified,
Label: pulumi.String("string"),
TargetAxis: monitoring.ThresholdTargetAxisTargetAxisUnspecified,
Value: pulumi.Float64(0),
},
},
TimeshiftDuration: pulumi.String("string"),
XAxis: &monitoring.AxisArgs{
Label: pulumi.String("string"),
Scale: monitoring.AxisScaleScaleUnspecified,
},
Y2Axis: &monitoring.AxisArgs{
Label: pulumi.String("string"),
Scale: monitoring.AxisScaleScaleUnspecified,
},
YAxis: &monitoring.AxisArgs{
Label: pulumi.String("string"),
Scale: monitoring.AxisScaleScaleUnspecified,
},
},
},
Width: pulumi.Int(0),
XPos: pulumi.Int(0),
YPos: pulumi.Int(0),
},
},
},
Name: pulumi.String("string"),
Project: pulumi.String("string"),
RowLayout: &monitoring.RowLayoutArgs{
Rows: monitoring.RowArray{
&monitoring.RowArgs{
Weight: pulumi.String("string"),
Widgets: monitoring.WidgetArray{
&monitoring.WidgetArgs{
AlertChart: &monitoring.AlertChartArgs{
Name: pulumi.String("string"),
},
Blank: &monitoring.EmptyArgs{},
CollapsibleGroup: &monitoring.CollapsibleGroupArgs{
Collapsed: pulumi.Bool(false),
},
ErrorReportingPanel: &monitoring.ErrorReportingPanelArgs{
ProjectNames: pulumi.StringArray{
pulumi.String("string"),
},
Services: pulumi.StringArray{
pulumi.String("string"),
},
Versions: pulumi.StringArray{
pulumi.String("string"),
},
},
Id: pulumi.String("string"),
IncidentList: &monitoring.IncidentListArgs{
MonitoredResources: monitoring.MonitoredResourceArray{
&monitoring.MonitoredResourceArgs{
Labels: pulumi.StringMap{
"string": pulumi.String("string"),
},
Type: pulumi.String("string"),
},
},
PolicyNames: pulumi.StringArray{
pulumi.String("string"),
},
},
LogsPanel: &monitoring.LogsPanelArgs{
Filter: pulumi.String("string"),
ResourceNames: pulumi.StringArray{
pulumi.String("string"),
},
},
PieChart: &monitoring.PieChartArgs{
ChartType: monitoring.PieChartChartTypePieChartTypeUnspecified,
DataSets: monitoring.PieChartDataSetArray{
&monitoring.PieChartDataSetArgs{
TimeSeriesQuery: &monitoring.TimeSeriesQueryArgs{
OpsAnalyticsQuery: &monitoring.OpsAnalyticsQueryArgs{
Sql: pulumi.String("string"),
},
OutputFullDuration: pulumi.Bool(false),
PrometheusQuery: pulumi.String("string"),
TimeSeriesFilter: &monitoring.TimeSeriesFilterArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesFilterRatio: &monitoring.TimeSeriesFilterRatioArgs{
Denominator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
Numerator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesQueryLanguage: pulumi.String("string"),
UnitOverride: pulumi.String("string"),
},
MinAlignmentPeriod: pulumi.String("string"),
SliceNameTemplate: pulumi.String("string"),
},
},
ShowLabels: pulumi.Bool(false),
},
Scorecard: &monitoring.ScorecardArgs{
TimeSeriesQuery: &monitoring.TimeSeriesQueryArgs{
OpsAnalyticsQuery: &monitoring.OpsAnalyticsQueryArgs{
Sql: pulumi.String("string"),
},
OutputFullDuration: pulumi.Bool(false),
PrometheusQuery: pulumi.String("string"),
TimeSeriesFilter: &monitoring.TimeSeriesFilterArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesFilterRatio: &monitoring.TimeSeriesFilterRatioArgs{
Denominator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
Numerator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesQueryLanguage: pulumi.String("string"),
UnitOverride: pulumi.String("string"),
},
BlankView: &monitoring.EmptyArgs{},
GaugeView: &monitoring.GaugeViewArgs{
LowerBound: pulumi.Float64(0),
UpperBound: pulumi.Float64(0),
},
SparkChartView: &monitoring.SparkChartViewArgs{
SparkChartType: monitoring.SparkChartViewSparkChartTypeSparkChartTypeUnspecified,
MinAlignmentPeriod: pulumi.String("string"),
},
Thresholds: monitoring.ThresholdArray{
&monitoring.ThresholdArgs{
Color: monitoring.ThresholdColorColorUnspecified,
Direction: monitoring.ThresholdDirectionDirectionUnspecified,
Label: pulumi.String("string"),
TargetAxis: monitoring.ThresholdTargetAxisTargetAxisUnspecified,
Value: pulumi.Float64(0),
},
},
},
Text: &monitoring.TextArgs{
Content: pulumi.String("string"),
Format: monitoring.TextFormatFormatUnspecified,
Style: &monitoring.TextStyleArgs{
BackgroundColor: pulumi.String("string"),
FontSize: monitoring.TextStyleFontSizeFontSizeUnspecified,
HorizontalAlignment: monitoring.TextStyleHorizontalAlignmentHorizontalAlignmentUnspecified,
Padding: monitoring.TextStylePaddingPaddingSizeUnspecified,
PointerLocation: monitoring.TextStylePointerLocationPointerLocationUnspecified,
TextColor: pulumi.String("string"),
VerticalAlignment: monitoring.TextStyleVerticalAlignmentVerticalAlignmentUnspecified,
},
},
TimeSeriesTable: &monitoring.TimeSeriesTableArgs{
DataSets: monitoring.TableDataSetArray{
&monitoring.TableDataSetArgs{
TimeSeriesQuery: &monitoring.TimeSeriesQueryArgs{
OpsAnalyticsQuery: &monitoring.OpsAnalyticsQueryArgs{
Sql: pulumi.String("string"),
},
OutputFullDuration: pulumi.Bool(false),
PrometheusQuery: pulumi.String("string"),
TimeSeriesFilter: &monitoring.TimeSeriesFilterArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesFilterRatio: &monitoring.TimeSeriesFilterRatioArgs{
Denominator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
Numerator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesQueryLanguage: pulumi.String("string"),
UnitOverride: pulumi.String("string"),
},
MinAlignmentPeriod: pulumi.String("string"),
TableDisplayOptions: &monitoring.TableDisplayOptionsArgs{
ShownColumns: pulumi.StringArray{
pulumi.String("string"),
},
},
TableTemplate: pulumi.String("string"),
},
},
ColumnSettings: monitoring.ColumnSettingsArray{
&monitoring.ColumnSettingsArgs{
Column: pulumi.String("string"),
Visible: pulumi.Bool(false),
},
},
MetricVisualization: monitoring.TimeSeriesTableMetricVisualizationMetricVisualizationUnspecified,
},
Title: pulumi.String("string"),
XyChart: &monitoring.XyChartArgs{
DataSets: monitoring.DataSetArray{
&monitoring.DataSetArgs{
TimeSeriesQuery: &monitoring.TimeSeriesQueryArgs{
OpsAnalyticsQuery: &monitoring.OpsAnalyticsQueryArgs{
Sql: pulumi.String("string"),
},
OutputFullDuration: pulumi.Bool(false),
PrometheusQuery: pulumi.String("string"),
TimeSeriesFilter: &monitoring.TimeSeriesFilterArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesFilterRatio: &monitoring.TimeSeriesFilterRatioArgs{
Denominator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
Numerator: &monitoring.RatioPartArgs{
Filter: pulumi.String("string"),
Aggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
PickTimeSeriesFilter: &monitoring.PickTimeSeriesFilterArgs{
Direction: monitoring.PickTimeSeriesFilterDirectionDirectionUnspecified,
NumTimeSeries: pulumi.Int(0),
RankingMethod: monitoring.PickTimeSeriesFilterRankingMethodMethodUnspecified,
},
SecondaryAggregation: &monitoring.AggregationArgs{
AlignmentPeriod: pulumi.String("string"),
CrossSeriesReducer: monitoring.AggregationCrossSeriesReducerReduceNone,
GroupByFields: pulumi.StringArray{
pulumi.String("string"),
},
PerSeriesAligner: monitoring.AggregationPerSeriesAlignerAlignNone,
},
},
TimeSeriesQueryLanguage: pulumi.String("string"),
UnitOverride: pulumi.String("string"),
},
Breakdowns: monitoring.BreakdownArray{
&monitoring.BreakdownArgs{
AggregationFunction: &monitoring.AggregationFunctionArgs{
Type: pulumi.String("string"),
Parameters: monitoring.ParameterArray{
&monitoring.ParameterArgs{
DoubleValue: pulumi.Float64(0),
IntValue: pulumi.String("string"),
},
},
},
Column: pulumi.String("string"),
Limit: pulumi.Int(0),
SortOrder: monitoring.BreakdownSortOrderSortOrderUnspecified,
},
},
Dimensions: monitoring.DimensionArray{
&monitoring.DimensionArgs{
Column: pulumi.String("string"),
ColumnType: pulumi.String("string"),
FloatBinSize: pulumi.Float64(0),
MaxBinCount: pulumi.Int(0),
NumericBinSize: pulumi.Int(0),
SortColumn: pulumi.String("string"),
SortOrder: monitoring.DimensionSortOrderSortOrderUnspecified,
TimeBinSize: pulumi.String("string"),
},
},
LegendTemplate: pulumi.String("string"),
Measures: monitoring.MeasureArray{
&monitoring.MeasureArgs{
AggregationFunction: &monitoring.AggregationFunctionArgs{
Type: pulumi.String("string"),
Parameters: monitoring.ParameterArray{
&monitoring.ParameterArgs{
DoubleValue: pulumi.Float64(0),
IntValue: pulumi.String("string"),
},
},
},
Column: pulumi.String("string"),
},
},
MinAlignmentPeriod: pulumi.String("string"),
PlotType: monitoring.DataSetPlotTypePlotTypeUnspecified,
TargetAxis: monitoring.DataSetTargetAxisTargetAxisUnspecified,
},
},
ChartOptions: &monitoring.ChartOptionsArgs{
DisplayHorizontal: pulumi.Bool(false),
Mode: monitoring.ChartOptionsModeModeUnspecified,
},
Thresholds: monitoring.ThresholdArray{
&monitoring.ThresholdArgs{
Color: monitoring.ThresholdColorColorUnspecified,
Direction: monitoring.ThresholdDirectionDirectionUnspecified,
Label: pulumi.String("string"),
TargetAxis: monitoring.ThresholdTargetAxisTargetAxisUnspecified,
Value: pulumi.Float64(0),
},
},
TimeshiftDuration: pulumi.String("string"),
XAxis: &monitoring.AxisArgs{
Label: pulumi.String("string"),
Scale: monitoring.AxisScaleScaleUnspecified,
},
Y2Axis: &monitoring.AxisArgs{
Label: pulumi.String("string"),
Scale: monitoring.AxisScaleScaleUnspecified,
},
YAxis: &monitoring.AxisArgs{
Label: pulumi.String("string"),
Scale: monitoring.AxisScaleScaleUnspecified,
},
},
},
},
},
},
},
})
var dashboardResource = new Dashboard("dashboardResource", DashboardArgs.builder()
.displayName("string")
.columnLayout(ColumnLayoutArgs.builder()
.columns(ColumnArgs.builder()
.weight("string")
.widgets(WidgetArgs.builder()
.alertChart(AlertChartArgs.builder()
.name("string")
.build())
.blank()
.collapsibleGroup(CollapsibleGroupArgs.builder()
.collapsed(false)
.build())
.errorReportingPanel(ErrorReportingPanelArgs.builder()
.projectNames("string")
.services("string")
.versions("string")
.build())
.id("string")
.incidentList(IncidentListArgs.builder()
.monitoredResources(MonitoredResourceArgs.builder()
.labels(Map.of("string", "string"))
.type("string")
.build())
.policyNames("string")
.build())
.logsPanel(LogsPanelArgs.builder()
.filter("string")
.resourceNames("string")
.build())
.pieChart(PieChartArgs.builder()
.chartType("PIE_CHART_TYPE_UNSPECIFIED")
.dataSets(PieChartDataSetArgs.builder()
.timeSeriesQuery(TimeSeriesQueryArgs.builder()
.opsAnalyticsQuery(OpsAnalyticsQueryArgs.builder()
.sql("string")
.build())
.outputFullDuration(false)
.prometheusQuery("string")
.timeSeriesFilter(TimeSeriesFilterArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
.direction("DIRECTION_UNSPECIFIED")
.numTimeSeries(0)
.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesFilterRatio(TimeSeriesFilterRatioArgs.builder()
.denominator(RatioPartArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.numerator(RatioPartArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
.direction("DIRECTION_UNSPECIFIED")
.numTimeSeries(0)
.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesQueryLanguage("string")
.unitOverride("string")
.build())
.minAlignmentPeriod("string")
.sliceNameTemplate("string")
.build())
.showLabels(false)
.build())
.scorecard(ScorecardArgs.builder()
.timeSeriesQuery(TimeSeriesQueryArgs.builder()
.opsAnalyticsQuery(OpsAnalyticsQueryArgs.builder()
.sql("string")
.build())
.outputFullDuration(false)
.prometheusQuery("string")
.timeSeriesFilter(TimeSeriesFilterArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
.direction("DIRECTION_UNSPECIFIED")
.numTimeSeries(0)
.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesFilterRatio(TimeSeriesFilterRatioArgs.builder()
.denominator(RatioPartArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.numerator(RatioPartArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
.direction("DIRECTION_UNSPECIFIED")
.numTimeSeries(0)
.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesQueryLanguage("string")
.unitOverride("string")
.build())
.blankView()
.gaugeView(GaugeViewArgs.builder()
.lowerBound(0)
.upperBound(0)
.build())
.sparkChartView(SparkChartViewArgs.builder()
.sparkChartType("SPARK_CHART_TYPE_UNSPECIFIED")
.minAlignmentPeriod("string")
.build())
.thresholds(ThresholdArgs.builder()
.color("COLOR_UNSPECIFIED")
.direction("DIRECTION_UNSPECIFIED")
.label("string")
.targetAxis("TARGET_AXIS_UNSPECIFIED")
.value(0)
.build())
.build())
.text(TextArgs.builder()
.content("string")
.format("FORMAT_UNSPECIFIED")
.style(TextStyleArgs.builder()
.backgroundColor("string")
.fontSize("FONT_SIZE_UNSPECIFIED")
.horizontalAlignment("HORIZONTAL_ALIGNMENT_UNSPECIFIED")
.padding("PADDING_SIZE_UNSPECIFIED")
.pointerLocation("POINTER_LOCATION_UNSPECIFIED")
.textColor("string")
.verticalAlignment("VERTICAL_ALIGNMENT_UNSPECIFIED")
.build())
.build())
.timeSeriesTable(TimeSeriesTableArgs.builder()
.dataSets(TableDataSetArgs.builder()
.timeSeriesQuery(TimeSeriesQueryArgs.builder()
.opsAnalyticsQuery(OpsAnalyticsQueryArgs.builder()
.sql("string")
.build())
.outputFullDuration(false)
.prometheusQuery("string")
.timeSeriesFilter(TimeSeriesFilterArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
.direction("DIRECTION_UNSPECIFIED")
.numTimeSeries(0)
.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesFilterRatio(TimeSeriesFilterRatioArgs.builder()
.denominator(RatioPartArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.numerator(RatioPartArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
.direction("DIRECTION_UNSPECIFIED")
.numTimeSeries(0)
.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesQueryLanguage("string")
.unitOverride("string")
.build())
.minAlignmentPeriod("string")
.tableDisplayOptions(TableDisplayOptionsArgs.builder()
.shownColumns("string")
.build())
.tableTemplate("string")
.build())
.columnSettings(ColumnSettingsArgs.builder()
.column("string")
.visible(false)
.build())
.metricVisualization("METRIC_VISUALIZATION_UNSPECIFIED")
.build())
.title("string")
.xyChart(XyChartArgs.builder()
.dataSets(DataSetArgs.builder()
.timeSeriesQuery(TimeSeriesQueryArgs.builder()
.opsAnalyticsQuery(OpsAnalyticsQueryArgs.builder()
.sql("string")
.build())
.outputFullDuration(false)
.prometheusQuery("string")
.timeSeriesFilter(TimeSeriesFilterArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
.direction("DIRECTION_UNSPECIFIED")
.numTimeSeries(0)
.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesFilterRatio(TimeSeriesFilterRatioArgs.builder()
.denominator(RatioPartArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.numerator(RatioPartArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
.direction("DIRECTION_UNSPECIFIED")
.numTimeSeries(0)
.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesQueryLanguage("string")
.unitOverride("string")
.build())
.breakdowns(BreakdownArgs.builder()
.aggregationFunction(AggregationFunctionArgs.builder()
.type("string")
.parameters(ParameterArgs.builder()
.doubleValue(0)
.intValue("string")
.build())
.build())
.column("string")
.limit(0)
.sortOrder("SORT_ORDER_UNSPECIFIED")
.build())
.dimensions(DimensionArgs.builder()
.column("string")
.columnType("string")
.floatBinSize(0)
.maxBinCount(0)
.numericBinSize(0)
.sortColumn("string")
.sortOrder("SORT_ORDER_UNSPECIFIED")
.timeBinSize("string")
.build())
.legendTemplate("string")
.measures(MeasureArgs.builder()
.aggregationFunction(AggregationFunctionArgs.builder()
.type("string")
.parameters(ParameterArgs.builder()
.doubleValue(0)
.intValue("string")
.build())
.build())
.column("string")
.build())
.minAlignmentPeriod("string")
.plotType("PLOT_TYPE_UNSPECIFIED")
.targetAxis("TARGET_AXIS_UNSPECIFIED")
.build())
.chartOptions(ChartOptionsArgs.builder()
.displayHorizontal(false)
.mode("MODE_UNSPECIFIED")
.build())
.thresholds(ThresholdArgs.builder()
.color("COLOR_UNSPECIFIED")
.direction("DIRECTION_UNSPECIFIED")
.label("string")
.targetAxis("TARGET_AXIS_UNSPECIFIED")
.value(0)
.build())
.timeshiftDuration("string")
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.build())
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.build())
.etag("string")
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.name("string")
.build())
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.collapsed(false)
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.versions("string")
.build())
.id("string")
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.build())
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.build())
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.filter("string")
.resourceNames("string")
.build())
.pieChart(PieChartArgs.builder()
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.build())
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.build())
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.build())
.minAlignmentPeriod("string")
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.build())
.tableTemplate("string")
.build())
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.column("string")
.visible(false)
.build())
.metricVisualization("METRIC_VISUALIZATION_UNSPECIFIED")
.build())
.title("string")
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.filter("string")
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.build())
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.build())
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.build())
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.build())
.build())
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.build())
.build())
.timeSeriesQueryLanguage("string")
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.build())
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.build())
.build())
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.build())
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.build())
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.value(0)
.build())
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.build())
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.build())
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.build())
.build())
.build())
.build())
.labels(Map.of("string", "string"))
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.build())
.blank()
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.collapsed(false)
.build())
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.projectNames("string")
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.versions("string")
.build())
.id("string")
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.type("string")
.build())
.policyNames("string")
.build())
.logsPanel(LogsPanelArgs.builder()
.filter("string")
.resourceNames("string")
.build())
.pieChart(PieChartArgs.builder()
.chartType("PIE_CHART_TYPE_UNSPECIFIED")
.dataSets(PieChartDataSetArgs.builder()
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.sql("string")
.build())
.outputFullDuration(false)
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.build())
.build())
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.build())
.build())
.numerator(RatioPartArgs.builder()
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.build())
.build())
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.build())
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.build())
.build())
.timeSeriesQueryLanguage("string")
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.build())
.minAlignmentPeriod("string")
.sliceNameTemplate("string")
.build())
.showLabels(false)
.build())
.scorecard(ScorecardArgs.builder()
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.sql("string")
.build())
.outputFullDuration(false)
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.filter("string")
.aggregation(AggregationArgs.builder()
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.perSeriesAligner("ALIGN_NONE")
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
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.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
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.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesFilterRatio(TimeSeriesFilterRatioArgs.builder()
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.filter("string")
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.build())
.build())
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.build())
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
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.build())
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.build())
.build())
.timeSeriesQueryLanguage("string")
.unitOverride("string")
.build())
.blankView()
.gaugeView(GaugeViewArgs.builder()
.lowerBound(0)
.upperBound(0)
.build())
.sparkChartView(SparkChartViewArgs.builder()
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.minAlignmentPeriod("string")
.build())
.thresholds(ThresholdArgs.builder()
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.direction("DIRECTION_UNSPECIFIED")
.label("string")
.targetAxis("TARGET_AXIS_UNSPECIFIED")
.value(0)
.build())
.build())
.text(TextArgs.builder()
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.format("FORMAT_UNSPECIFIED")
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.fontSize("FONT_SIZE_UNSPECIFIED")
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.textColor("string")
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.build())
.build())
.timeSeriesTable(TimeSeriesTableArgs.builder()
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.sql("string")
.build())
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.build())
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.build())
.build())
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.build())
.build())
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.build())
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
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.build())
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.build())
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.build())
.minAlignmentPeriod("string")
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.build())
.tableTemplate("string")
.build())
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.visible(false)
.build())
.metricVisualization("METRIC_VISUALIZATION_UNSPECIFIED")
.build())
.title("string")
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.build())
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.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
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.build())
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.build())
.build())
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.build())
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.build())
.build())
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.build())
.build())
.timeSeriesQueryLanguage("string")
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.build())
.name("string")
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.versions("string")
.build())
.id("string")
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.build())
.policyNames("string")
.build())
.logsPanel(LogsPanelArgs.builder()
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.build())
.pieChart(PieChartArgs.builder()
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.timeSeriesQuery(TimeSeriesQueryArgs.builder()
.opsAnalyticsQuery(OpsAnalyticsQueryArgs.builder()
.sql("string")
.build())
.outputFullDuration(false)
.prometheusQuery("string")
.timeSeriesFilter(TimeSeriesFilterArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
.direction("DIRECTION_UNSPECIFIED")
.numTimeSeries(0)
.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesFilterRatio(TimeSeriesFilterRatioArgs.builder()
.denominator(RatioPartArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.numerator(RatioPartArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
.direction("DIRECTION_UNSPECIFIED")
.numTimeSeries(0)
.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesQueryLanguage("string")
.unitOverride("string")
.build())
.minAlignmentPeriod("string")
.sliceNameTemplate("string")
.build())
.showLabels(false)
.build())
.scorecard(ScorecardArgs.builder()
.timeSeriesQuery(TimeSeriesQueryArgs.builder()
.opsAnalyticsQuery(OpsAnalyticsQueryArgs.builder()
.sql("string")
.build())
.outputFullDuration(false)
.prometheusQuery("string")
.timeSeriesFilter(TimeSeriesFilterArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
.direction("DIRECTION_UNSPECIFIED")
.numTimeSeries(0)
.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesFilterRatio(TimeSeriesFilterRatioArgs.builder()
.denominator(RatioPartArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.numerator(RatioPartArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
.direction("DIRECTION_UNSPECIFIED")
.numTimeSeries(0)
.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesQueryLanguage("string")
.unitOverride("string")
.build())
.blankView()
.gaugeView(GaugeViewArgs.builder()
.lowerBound(0)
.upperBound(0)
.build())
.sparkChartView(SparkChartViewArgs.builder()
.sparkChartType("SPARK_CHART_TYPE_UNSPECIFIED")
.minAlignmentPeriod("string")
.build())
.thresholds(ThresholdArgs.builder()
.color("COLOR_UNSPECIFIED")
.direction("DIRECTION_UNSPECIFIED")
.label("string")
.targetAxis("TARGET_AXIS_UNSPECIFIED")
.value(0)
.build())
.build())
.text(TextArgs.builder()
.content("string")
.format("FORMAT_UNSPECIFIED")
.style(TextStyleArgs.builder()
.backgroundColor("string")
.fontSize("FONT_SIZE_UNSPECIFIED")
.horizontalAlignment("HORIZONTAL_ALIGNMENT_UNSPECIFIED")
.padding("PADDING_SIZE_UNSPECIFIED")
.pointerLocation("POINTER_LOCATION_UNSPECIFIED")
.textColor("string")
.verticalAlignment("VERTICAL_ALIGNMENT_UNSPECIFIED")
.build())
.build())
.timeSeriesTable(TimeSeriesTableArgs.builder()
.dataSets(TableDataSetArgs.builder()
.timeSeriesQuery(TimeSeriesQueryArgs.builder()
.opsAnalyticsQuery(OpsAnalyticsQueryArgs.builder()
.sql("string")
.build())
.outputFullDuration(false)
.prometheusQuery("string")
.timeSeriesFilter(TimeSeriesFilterArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
.direction("DIRECTION_UNSPECIFIED")
.numTimeSeries(0)
.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesFilterRatio(TimeSeriesFilterRatioArgs.builder()
.denominator(RatioPartArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.numerator(RatioPartArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
.direction("DIRECTION_UNSPECIFIED")
.numTimeSeries(0)
.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesQueryLanguage("string")
.unitOverride("string")
.build())
.minAlignmentPeriod("string")
.tableDisplayOptions(TableDisplayOptionsArgs.builder()
.shownColumns("string")
.build())
.tableTemplate("string")
.build())
.columnSettings(ColumnSettingsArgs.builder()
.column("string")
.visible(false)
.build())
.metricVisualization("METRIC_VISUALIZATION_UNSPECIFIED")
.build())
.title("string")
.xyChart(XyChartArgs.builder()
.dataSets(DataSetArgs.builder()
.timeSeriesQuery(TimeSeriesQueryArgs.builder()
.opsAnalyticsQuery(OpsAnalyticsQueryArgs.builder()
.sql("string")
.build())
.outputFullDuration(false)
.prometheusQuery("string")
.timeSeriesFilter(TimeSeriesFilterArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
.direction("DIRECTION_UNSPECIFIED")
.numTimeSeries(0)
.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesFilterRatio(TimeSeriesFilterRatioArgs.builder()
.denominator(RatioPartArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.numerator(RatioPartArgs.builder()
.filter("string")
.aggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.pickTimeSeriesFilter(PickTimeSeriesFilterArgs.builder()
.direction("DIRECTION_UNSPECIFIED")
.numTimeSeries(0)
.rankingMethod("METHOD_UNSPECIFIED")
.build())
.secondaryAggregation(AggregationArgs.builder()
.alignmentPeriod("string")
.crossSeriesReducer("REDUCE_NONE")
.groupByFields("string")
.perSeriesAligner("ALIGN_NONE")
.build())
.build())
.timeSeriesQueryLanguage("string")
.unitOverride("string")
.build())
.breakdowns(BreakdownArgs.builder()
.aggregationFunction(AggregationFunctionArgs.builder()
.type("string")
.parameters(ParameterArgs.builder()
.doubleValue(0)
.intValue("string")
.build())
.build())
.column("string")
.limit(0)
.sortOrder("SORT_ORDER_UNSPECIFIED")
.build())
.dimensions(DimensionArgs.builder()
.column("string")
.columnType("string")
.floatBinSize(0)
.maxBinCount(0)
.numericBinSize(0)
.sortColumn("string")
.sortOrder("SORT_ORDER_UNSPECIFIED")
.timeBinSize("string")
.build())
.legendTemplate("string")
.measures(MeasureArgs.builder()
.aggregationFunction(AggregationFunctionArgs.builder()
.type("string")
.parameters(ParameterArgs.builder()
.doubleValue(0)
.intValue("string")
.build())
.build())
.column("string")
.build())
.minAlignmentPeriod("string")
.plotType("PLOT_TYPE_UNSPECIFIED")
.targetAxis("TARGET_AXIS_UNSPECIFIED")
.build())
.chartOptions(ChartOptionsArgs.builder()
.displayHorizontal(false)
.mode("MODE_UNSPECIFIED")
.build())
.thresholds(ThresholdArgs.builder()
.color("COLOR_UNSPECIFIED")
.direction("DIRECTION_UNSPECIFIED")
.label("string")
.targetAxis("TARGET_AXIS_UNSPECIFIED")
.value(0)
.build())
.timeshiftDuration("string")
.xAxis(AxisArgs.builder()
.label("string")
.scale("SCALE_UNSPECIFIED")
.build())
.y2Axis(AxisArgs.builder()
.label("string")
.scale("SCALE_UNSPECIFIED")
.build())
.yAxis(AxisArgs.builder()
.label("string")
.scale("SCALE_UNSPECIFIED")
.build())
.build())
.build())
.build())
.build())
.build());
dashboard_resource = google_native.monitoring.v1.Dashboard("dashboardResource",
display_name="string",
column_layout={
"columns": [{
"weight": "string",
"widgets": [{
"alert_chart": {
"name": "string",
},
"blank": {},
"collapsible_group": {
"collapsed": False,
},
"error_reporting_panel": {
"project_names": ["string"],
"services": ["string"],
"versions": ["string"],
},
"id": "string",
"incident_list": {
"monitored_resources": [{
"labels": {
"string": "string",
},
"type": "string",
}],
"policy_names": ["string"],
},
"logs_panel": {
"filter": "string",
"resource_names": ["string"],
},
"pie_chart": {
"chart_type": google_native.monitoring.v1.PieChartChartType.PIE_CHART_TYPE_UNSPECIFIED,
"data_sets": [{
"time_series_query": {
"ops_analytics_query": {
"sql": "string",
},
"output_full_duration": False,
"prometheus_query": "string",
"time_series_filter": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_filter_ratio": {
"denominator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"numerator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_query_language": "string",
"unit_override": "string",
},
"min_alignment_period": "string",
"slice_name_template": "string",
}],
"show_labels": False,
},
"scorecard": {
"time_series_query": {
"ops_analytics_query": {
"sql": "string",
},
"output_full_duration": False,
"prometheus_query": "string",
"time_series_filter": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_filter_ratio": {
"denominator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"numerator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_query_language": "string",
"unit_override": "string",
},
"blank_view": {},
"gauge_view": {
"lower_bound": 0,
"upper_bound": 0,
},
"spark_chart_view": {
"spark_chart_type": google_native.monitoring.v1.SparkChartViewSparkChartType.SPARK_CHART_TYPE_UNSPECIFIED,
"min_alignment_period": "string",
},
"thresholds": [{
"color": google_native.monitoring.v1.ThresholdColor.COLOR_UNSPECIFIED,
"direction": google_native.monitoring.v1.ThresholdDirection.DIRECTION_UNSPECIFIED,
"label": "string",
"target_axis": google_native.monitoring.v1.ThresholdTargetAxis.TARGET_AXIS_UNSPECIFIED,
"value": 0,
}],
},
"text": {
"content": "string",
"format": google_native.monitoring.v1.TextFormat.FORMAT_UNSPECIFIED,
"style": {
"background_color": "string",
"font_size": google_native.monitoring.v1.TextStyleFontSize.FONT_SIZE_UNSPECIFIED,
"horizontal_alignment": google_native.monitoring.v1.TextStyleHorizontalAlignment.HORIZONTAL_ALIGNMENT_UNSPECIFIED,
"padding": google_native.monitoring.v1.TextStylePadding.PADDING_SIZE_UNSPECIFIED,
"pointer_location": google_native.monitoring.v1.TextStylePointerLocation.POINTER_LOCATION_UNSPECIFIED,
"text_color": "string",
"vertical_alignment": google_native.monitoring.v1.TextStyleVerticalAlignment.VERTICAL_ALIGNMENT_UNSPECIFIED,
},
},
"time_series_table": {
"data_sets": [{
"time_series_query": {
"ops_analytics_query": {
"sql": "string",
},
"output_full_duration": False,
"prometheus_query": "string",
"time_series_filter": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_filter_ratio": {
"denominator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"numerator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_query_language": "string",
"unit_override": "string",
},
"min_alignment_period": "string",
"table_display_options": {
"shown_columns": ["string"],
},
"table_template": "string",
}],
"column_settings": [{
"column": "string",
"visible": False,
}],
"metric_visualization": google_native.monitoring.v1.TimeSeriesTableMetricVisualization.METRIC_VISUALIZATION_UNSPECIFIED,
},
"title": "string",
"xy_chart": {
"data_sets": [{
"time_series_query": {
"ops_analytics_query": {
"sql": "string",
},
"output_full_duration": False,
"prometheus_query": "string",
"time_series_filter": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_filter_ratio": {
"denominator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"numerator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_query_language": "string",
"unit_override": "string",
},
"breakdowns": [{
"aggregation_function": {
"type": "string",
"parameters": [{
"double_value": 0,
"int_value": "string",
}],
},
"column": "string",
"limit": 0,
"sort_order": google_native.monitoring.v1.BreakdownSortOrder.SORT_ORDER_UNSPECIFIED,
}],
"dimensions": [{
"column": "string",
"column_type": "string",
"float_bin_size": 0,
"max_bin_count": 0,
"numeric_bin_size": 0,
"sort_column": "string",
"sort_order": google_native.monitoring.v1.DimensionSortOrder.SORT_ORDER_UNSPECIFIED,
"time_bin_size": "string",
}],
"legend_template": "string",
"measures": [{
"aggregation_function": {
"type": "string",
"parameters": [{
"double_value": 0,
"int_value": "string",
}],
},
"column": "string",
}],
"min_alignment_period": "string",
"plot_type": google_native.monitoring.v1.DataSetPlotType.PLOT_TYPE_UNSPECIFIED,
"target_axis": google_native.monitoring.v1.DataSetTargetAxis.TARGET_AXIS_UNSPECIFIED,
}],
"chart_options": {
"display_horizontal": False,
"mode": google_native.monitoring.v1.ChartOptionsMode.MODE_UNSPECIFIED,
},
"thresholds": [{
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"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_query_language": "string",
"unit_override": "string",
},
"min_alignment_period": "string",
"table_display_options": {
"shown_columns": ["string"],
},
"table_template": "string",
}],
"column_settings": [{
"column": "string",
"visible": False,
}],
"metric_visualization": google_native.monitoring.v1.TimeSeriesTableMetricVisualization.METRIC_VISUALIZATION_UNSPECIFIED,
},
"title": "string",
"xy_chart": {
"data_sets": [{
"time_series_query": {
"ops_analytics_query": {
"sql": "string",
},
"output_full_duration": False,
"prometheus_query": "string",
"time_series_filter": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_filter_ratio": {
"denominator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"numerator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_query_language": "string",
"unit_override": "string",
},
"breakdowns": [{
"aggregation_function": {
"type": "string",
"parameters": [{
"double_value": 0,
"int_value": "string",
}],
},
"column": "string",
"limit": 0,
"sort_order": google_native.monitoring.v1.BreakdownSortOrder.SORT_ORDER_UNSPECIFIED,
}],
"dimensions": [{
"column": "string",
"column_type": "string",
"float_bin_size": 0,
"max_bin_count": 0,
"numeric_bin_size": 0,
"sort_column": "string",
"sort_order": google_native.monitoring.v1.DimensionSortOrder.SORT_ORDER_UNSPECIFIED,
"time_bin_size": "string",
}],
"legend_template": "string",
"measures": [{
"aggregation_function": {
"type": "string",
"parameters": [{
"double_value": 0,
"int_value": "string",
}],
},
"column": "string",
}],
"min_alignment_period": "string",
"plot_type": google_native.monitoring.v1.DataSetPlotType.PLOT_TYPE_UNSPECIFIED,
"target_axis": google_native.monitoring.v1.DataSetTargetAxis.TARGET_AXIS_UNSPECIFIED,
}],
"chart_options": {
"display_horizontal": False,
"mode": google_native.monitoring.v1.ChartOptionsMode.MODE_UNSPECIFIED,
},
"thresholds": [{
"color": google_native.monitoring.v1.ThresholdColor.COLOR_UNSPECIFIED,
"direction": google_native.monitoring.v1.ThresholdDirection.DIRECTION_UNSPECIFIED,
"label": "string",
"target_axis": google_native.monitoring.v1.ThresholdTargetAxis.TARGET_AXIS_UNSPECIFIED,
"value": 0,
}],
"timeshift_duration": "string",
"x_axis": {
"label": "string",
"scale": google_native.monitoring.v1.AxisScale.SCALE_UNSPECIFIED,
},
"y2_axis": {
"label": "string",
"scale": google_native.monitoring.v1.AxisScale.SCALE_UNSPECIFIED,
},
"y_axis": {
"label": "string",
"scale": google_native.monitoring.v1.AxisScale.SCALE_UNSPECIFIED,
},
},
},
"width": 0,
"x_pos": 0,
"y_pos": 0,
}],
},
name="string",
project="string",
row_layout={
"rows": [{
"weight": "string",
"widgets": [{
"alert_chart": {
"name": "string",
},
"blank": {},
"collapsible_group": {
"collapsed": False,
},
"error_reporting_panel": {
"project_names": ["string"],
"services": ["string"],
"versions": ["string"],
},
"id": "string",
"incident_list": {
"monitored_resources": [{
"labels": {
"string": "string",
},
"type": "string",
}],
"policy_names": ["string"],
},
"logs_panel": {
"filter": "string",
"resource_names": ["string"],
},
"pie_chart": {
"chart_type": google_native.monitoring.v1.PieChartChartType.PIE_CHART_TYPE_UNSPECIFIED,
"data_sets": [{
"time_series_query": {
"ops_analytics_query": {
"sql": "string",
},
"output_full_duration": False,
"prometheus_query": "string",
"time_series_filter": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_filter_ratio": {
"denominator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"numerator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_query_language": "string",
"unit_override": "string",
},
"min_alignment_period": "string",
"slice_name_template": "string",
}],
"show_labels": False,
},
"scorecard": {
"time_series_query": {
"ops_analytics_query": {
"sql": "string",
},
"output_full_duration": False,
"prometheus_query": "string",
"time_series_filter": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_filter_ratio": {
"denominator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"numerator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_query_language": "string",
"unit_override": "string",
},
"blank_view": {},
"gauge_view": {
"lower_bound": 0,
"upper_bound": 0,
},
"spark_chart_view": {
"spark_chart_type": google_native.monitoring.v1.SparkChartViewSparkChartType.SPARK_CHART_TYPE_UNSPECIFIED,
"min_alignment_period": "string",
},
"thresholds": [{
"color": google_native.monitoring.v1.ThresholdColor.COLOR_UNSPECIFIED,
"direction": google_native.monitoring.v1.ThresholdDirection.DIRECTION_UNSPECIFIED,
"label": "string",
"target_axis": google_native.monitoring.v1.ThresholdTargetAxis.TARGET_AXIS_UNSPECIFIED,
"value": 0,
}],
},
"text": {
"content": "string",
"format": google_native.monitoring.v1.TextFormat.FORMAT_UNSPECIFIED,
"style": {
"background_color": "string",
"font_size": google_native.monitoring.v1.TextStyleFontSize.FONT_SIZE_UNSPECIFIED,
"horizontal_alignment": google_native.monitoring.v1.TextStyleHorizontalAlignment.HORIZONTAL_ALIGNMENT_UNSPECIFIED,
"padding": google_native.monitoring.v1.TextStylePadding.PADDING_SIZE_UNSPECIFIED,
"pointer_location": google_native.monitoring.v1.TextStylePointerLocation.POINTER_LOCATION_UNSPECIFIED,
"text_color": "string",
"vertical_alignment": google_native.monitoring.v1.TextStyleVerticalAlignment.VERTICAL_ALIGNMENT_UNSPECIFIED,
},
},
"time_series_table": {
"data_sets": [{
"time_series_query": {
"ops_analytics_query": {
"sql": "string",
},
"output_full_duration": False,
"prometheus_query": "string",
"time_series_filter": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_filter_ratio": {
"denominator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"numerator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_query_language": "string",
"unit_override": "string",
},
"min_alignment_period": "string",
"table_display_options": {
"shown_columns": ["string"],
},
"table_template": "string",
}],
"column_settings": [{
"column": "string",
"visible": False,
}],
"metric_visualization": google_native.monitoring.v1.TimeSeriesTableMetricVisualization.METRIC_VISUALIZATION_UNSPECIFIED,
},
"title": "string",
"xy_chart": {
"data_sets": [{
"time_series_query": {
"ops_analytics_query": {
"sql": "string",
},
"output_full_duration": False,
"prometheus_query": "string",
"time_series_filter": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_filter_ratio": {
"denominator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"numerator": {
"filter": "string",
"aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"pick_time_series_filter": {
"direction": google_native.monitoring.v1.PickTimeSeriesFilterDirection.DIRECTION_UNSPECIFIED,
"num_time_series": 0,
"ranking_method": google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.METHOD_UNSPECIFIED,
},
"secondary_aggregation": {
"alignment_period": "string",
"cross_series_reducer": google_native.monitoring.v1.AggregationCrossSeriesReducer.REDUCE_NONE,
"group_by_fields": ["string"],
"per_series_aligner": google_native.monitoring.v1.AggregationPerSeriesAligner.ALIGN_NONE,
},
},
"time_series_query_language": "string",
"unit_override": "string",
},
"breakdowns": [{
"aggregation_function": {
"type": "string",
"parameters": [{
"double_value": 0,
"int_value": "string",
}],
},
"column": "string",
"limit": 0,
"sort_order": google_native.monitoring.v1.BreakdownSortOrder.SORT_ORDER_UNSPECIFIED,
}],
"dimensions": [{
"column": "string",
"column_type": "string",
"float_bin_size": 0,
"max_bin_count": 0,
"numeric_bin_size": 0,
"sort_column": "string",
"sort_order": google_native.monitoring.v1.DimensionSortOrder.SORT_ORDER_UNSPECIFIED,
"time_bin_size": "string",
}],
"legend_template": "string",
"measures": [{
"aggregation_function": {
"type": "string",
"parameters": [{
"double_value": 0,
"int_value": "string",
}],
},
"column": "string",
}],
"min_alignment_period": "string",
"plot_type": google_native.monitoring.v1.DataSetPlotType.PLOT_TYPE_UNSPECIFIED,
"target_axis": google_native.monitoring.v1.DataSetTargetAxis.TARGET_AXIS_UNSPECIFIED,
}],
"chart_options": {
"display_horizontal": False,
"mode": google_native.monitoring.v1.ChartOptionsMode.MODE_UNSPECIFIED,
},
"thresholds": [{
"color": google_native.monitoring.v1.ThresholdColor.COLOR_UNSPECIFIED,
"direction": google_native.monitoring.v1.ThresholdDirection.DIRECTION_UNSPECIFIED,
"label": "string",
"target_axis": google_native.monitoring.v1.ThresholdTargetAxis.TARGET_AXIS_UNSPECIFIED,
"value": 0,
}],
"timeshift_duration": "string",
"x_axis": {
"label": "string",
"scale": google_native.monitoring.v1.AxisScale.SCALE_UNSPECIFIED,
},
"y2_axis": {
"label": "string",
"scale": google_native.monitoring.v1.AxisScale.SCALE_UNSPECIFIED,
},
"y_axis": {
"label": "string",
"scale": google_native.monitoring.v1.AxisScale.SCALE_UNSPECIFIED,
},
},
}],
}],
})
const dashboardResource = new google_native.monitoring.v1.Dashboard("dashboardResource", {
displayName: "string",
columnLayout: {
columns: [{
weight: "string",
widgets: [{
alertChart: {
name: "string",
},
blank: {},
collapsibleGroup: {
collapsed: false,
},
errorReportingPanel: {
projectNames: ["string"],
services: ["string"],
versions: ["string"],
},
id: "string",
incidentList: {
monitoredResources: [{
labels: {
string: "string",
},
type: "string",
}],
policyNames: ["string"],
},
logsPanel: {
filter: "string",
resourceNames: ["string"],
},
pieChart: {
chartType: google_native.monitoring.v1.PieChartChartType.PieChartTypeUnspecified,
dataSets: [{
timeSeriesQuery: {
opsAnalyticsQuery: {
sql: "string",
},
outputFullDuration: false,
prometheusQuery: "string",
timeSeriesFilter: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
pickTimeSeriesFilter: {
direction: google_native.monitoring.v1.PickTimeSeriesFilterDirection.DirectionUnspecified,
numTimeSeries: 0,
rankingMethod: google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
secondaryAggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
timeSeriesFilterRatio: {
denominator: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
numerator: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
pickTimeSeriesFilter: {
direction: google_native.monitoring.v1.PickTimeSeriesFilterDirection.DirectionUnspecified,
numTimeSeries: 0,
rankingMethod: google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
secondaryAggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
timeSeriesQueryLanguage: "string",
unitOverride: "string",
},
minAlignmentPeriod: "string",
sliceNameTemplate: "string",
}],
showLabels: false,
},
scorecard: {
timeSeriesQuery: {
opsAnalyticsQuery: {
sql: "string",
},
outputFullDuration: false,
prometheusQuery: "string",
timeSeriesFilter: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
pickTimeSeriesFilter: {
direction: google_native.monitoring.v1.PickTimeSeriesFilterDirection.DirectionUnspecified,
numTimeSeries: 0,
rankingMethod: google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
secondaryAggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
timeSeriesFilterRatio: {
denominator: {
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aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
numerator: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
pickTimeSeriesFilter: {
direction: google_native.monitoring.v1.PickTimeSeriesFilterDirection.DirectionUnspecified,
numTimeSeries: 0,
rankingMethod: google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
secondaryAggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
timeSeriesQueryLanguage: "string",
unitOverride: "string",
},
blankView: {},
gaugeView: {
lowerBound: 0,
upperBound: 0,
},
sparkChartView: {
sparkChartType: google_native.monitoring.v1.SparkChartViewSparkChartType.SparkChartTypeUnspecified,
minAlignmentPeriod: "string",
},
thresholds: [{
color: google_native.monitoring.v1.ThresholdColor.ColorUnspecified,
direction: google_native.monitoring.v1.ThresholdDirection.DirectionUnspecified,
label: "string",
targetAxis: google_native.monitoring.v1.ThresholdTargetAxis.TargetAxisUnspecified,
value: 0,
}],
},
text: {
content: "string",
format: google_native.monitoring.v1.TextFormat.FormatUnspecified,
style: {
backgroundColor: "string",
fontSize: google_native.monitoring.v1.TextStyleFontSize.FontSizeUnspecified,
horizontalAlignment: google_native.monitoring.v1.TextStyleHorizontalAlignment.HorizontalAlignmentUnspecified,
padding: google_native.monitoring.v1.TextStylePadding.PaddingSizeUnspecified,
pointerLocation: google_native.monitoring.v1.TextStylePointerLocation.PointerLocationUnspecified,
textColor: "string",
verticalAlignment: google_native.monitoring.v1.TextStyleVerticalAlignment.VerticalAlignmentUnspecified,
},
},
timeSeriesTable: {
dataSets: [{
timeSeriesQuery: {
opsAnalyticsQuery: {
sql: "string",
},
outputFullDuration: false,
prometheusQuery: "string",
timeSeriesFilter: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
pickTimeSeriesFilter: {
direction: google_native.monitoring.v1.PickTimeSeriesFilterDirection.DirectionUnspecified,
numTimeSeries: 0,
rankingMethod: google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
secondaryAggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
timeSeriesFilterRatio: {
denominator: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
numerator: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
pickTimeSeriesFilter: {
direction: google_native.monitoring.v1.PickTimeSeriesFilterDirection.DirectionUnspecified,
numTimeSeries: 0,
rankingMethod: google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
secondaryAggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
timeSeriesQueryLanguage: "string",
unitOverride: "string",
},
minAlignmentPeriod: "string",
tableDisplayOptions: {
shownColumns: ["string"],
},
tableTemplate: "string",
}],
columnSettings: [{
column: "string",
visible: false,
}],
metricVisualization: google_native.monitoring.v1.TimeSeriesTableMetricVisualization.MetricVisualizationUnspecified,
},
title: "string",
xyChart: {
dataSets: [{
timeSeriesQuery: {
opsAnalyticsQuery: {
sql: "string",
},
outputFullDuration: false,
prometheusQuery: "string",
timeSeriesFilter: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
pickTimeSeriesFilter: {
direction: google_native.monitoring.v1.PickTimeSeriesFilterDirection.DirectionUnspecified,
numTimeSeries: 0,
rankingMethod: google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
secondaryAggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
timeSeriesFilterRatio: {
denominator: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
numerator: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
pickTimeSeriesFilter: {
direction: google_native.monitoring.v1.PickTimeSeriesFilterDirection.DirectionUnspecified,
numTimeSeries: 0,
rankingMethod: google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
secondaryAggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
timeSeriesQueryLanguage: "string",
unitOverride: "string",
},
breakdowns: [{
aggregationFunction: {
type: "string",
parameters: [{
doubleValue: 0,
intValue: "string",
}],
},
column: "string",
limit: 0,
sortOrder: google_native.monitoring.v1.BreakdownSortOrder.SortOrderUnspecified,
}],
dimensions: [{
column: "string",
columnType: "string",
floatBinSize: 0,
maxBinCount: 0,
numericBinSize: 0,
sortColumn: "string",
sortOrder: google_native.monitoring.v1.DimensionSortOrder.SortOrderUnspecified,
timeBinSize: "string",
}],
legendTemplate: "string",
measures: [{
aggregationFunction: {
type: "string",
parameters: [{
doubleValue: 0,
intValue: "string",
}],
},
column: "string",
}],
minAlignmentPeriod: "string",
plotType: google_native.monitoring.v1.DataSetPlotType.PlotTypeUnspecified,
targetAxis: google_native.monitoring.v1.DataSetTargetAxis.TargetAxisUnspecified,
}],
chartOptions: {
displayHorizontal: false,
mode: google_native.monitoring.v1.ChartOptionsMode.ModeUnspecified,
},
thresholds: [{
color: google_native.monitoring.v1.ThresholdColor.ColorUnspecified,
direction: google_native.monitoring.v1.ThresholdDirection.DirectionUnspecified,
label: "string",
targetAxis: google_native.monitoring.v1.ThresholdTargetAxis.TargetAxisUnspecified,
value: 0,
}],
timeshiftDuration: "string",
xAxis: {
label: "string",
scale: google_native.monitoring.v1.AxisScale.ScaleUnspecified,
},
y2Axis: {
label: "string",
scale: google_native.monitoring.v1.AxisScale.ScaleUnspecified,
},
yAxis: {
label: "string",
scale: google_native.monitoring.v1.AxisScale.ScaleUnspecified,
},
},
},
width: 0,
xPos: 0,
yPos: 0,
}],
},
name: "string",
project: "string",
rowLayout: {
rows: [{
weight: "string",
widgets: [{
alertChart: {
name: "string",
},
blank: {},
collapsibleGroup: {
collapsed: false,
},
errorReportingPanel: {
projectNames: ["string"],
services: ["string"],
versions: ["string"],
},
id: "string",
incidentList: {
monitoredResources: [{
labels: {
string: "string",
},
type: "string",
}],
policyNames: ["string"],
},
logsPanel: {
filter: "string",
resourceNames: ["string"],
},
pieChart: {
chartType: google_native.monitoring.v1.PieChartChartType.PieChartTypeUnspecified,
dataSets: [{
timeSeriesQuery: {
opsAnalyticsQuery: {
sql: "string",
},
outputFullDuration: false,
prometheusQuery: "string",
timeSeriesFilter: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
pickTimeSeriesFilter: {
direction: google_native.monitoring.v1.PickTimeSeriesFilterDirection.DirectionUnspecified,
numTimeSeries: 0,
rankingMethod: google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
secondaryAggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
timeSeriesFilterRatio: {
denominator: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
numerator: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
pickTimeSeriesFilter: {
direction: google_native.monitoring.v1.PickTimeSeriesFilterDirection.DirectionUnspecified,
numTimeSeries: 0,
rankingMethod: google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
secondaryAggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
timeSeriesQueryLanguage: "string",
unitOverride: "string",
},
minAlignmentPeriod: "string",
sliceNameTemplate: "string",
}],
showLabels: false,
},
scorecard: {
timeSeriesQuery: {
opsAnalyticsQuery: {
sql: "string",
},
outputFullDuration: false,
prometheusQuery: "string",
timeSeriesFilter: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
pickTimeSeriesFilter: {
direction: google_native.monitoring.v1.PickTimeSeriesFilterDirection.DirectionUnspecified,
numTimeSeries: 0,
rankingMethod: google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
secondaryAggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
timeSeriesFilterRatio: {
denominator: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
numerator: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
pickTimeSeriesFilter: {
direction: google_native.monitoring.v1.PickTimeSeriesFilterDirection.DirectionUnspecified,
numTimeSeries: 0,
rankingMethod: google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
secondaryAggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
timeSeriesQueryLanguage: "string",
unitOverride: "string",
},
blankView: {},
gaugeView: {
lowerBound: 0,
upperBound: 0,
},
sparkChartView: {
sparkChartType: google_native.monitoring.v1.SparkChartViewSparkChartType.SparkChartTypeUnspecified,
minAlignmentPeriod: "string",
},
thresholds: [{
color: google_native.monitoring.v1.ThresholdColor.ColorUnspecified,
direction: google_native.monitoring.v1.ThresholdDirection.DirectionUnspecified,
label: "string",
targetAxis: google_native.monitoring.v1.ThresholdTargetAxis.TargetAxisUnspecified,
value: 0,
}],
},
text: {
content: "string",
format: google_native.monitoring.v1.TextFormat.FormatUnspecified,
style: {
backgroundColor: "string",
fontSize: google_native.monitoring.v1.TextStyleFontSize.FontSizeUnspecified,
horizontalAlignment: google_native.monitoring.v1.TextStyleHorizontalAlignment.HorizontalAlignmentUnspecified,
padding: google_native.monitoring.v1.TextStylePadding.PaddingSizeUnspecified,
pointerLocation: google_native.monitoring.v1.TextStylePointerLocation.PointerLocationUnspecified,
textColor: "string",
verticalAlignment: google_native.monitoring.v1.TextStyleVerticalAlignment.VerticalAlignmentUnspecified,
},
},
timeSeriesTable: {
dataSets: [{
timeSeriesQuery: {
opsAnalyticsQuery: {
sql: "string",
},
outputFullDuration: false,
prometheusQuery: "string",
timeSeriesFilter: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
pickTimeSeriesFilter: {
direction: google_native.monitoring.v1.PickTimeSeriesFilterDirection.DirectionUnspecified,
numTimeSeries: 0,
rankingMethod: google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
secondaryAggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
timeSeriesFilterRatio: {
denominator: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
numerator: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
pickTimeSeriesFilter: {
direction: google_native.monitoring.v1.PickTimeSeriesFilterDirection.DirectionUnspecified,
numTimeSeries: 0,
rankingMethod: google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
secondaryAggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
timeSeriesQueryLanguage: "string",
unitOverride: "string",
},
minAlignmentPeriod: "string",
tableDisplayOptions: {
shownColumns: ["string"],
},
tableTemplate: "string",
}],
columnSettings: [{
column: "string",
visible: false,
}],
metricVisualization: google_native.monitoring.v1.TimeSeriesTableMetricVisualization.MetricVisualizationUnspecified,
},
title: "string",
xyChart: {
dataSets: [{
timeSeriesQuery: {
opsAnalyticsQuery: {
sql: "string",
},
outputFullDuration: false,
prometheusQuery: "string",
timeSeriesFilter: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
pickTimeSeriesFilter: {
direction: google_native.monitoring.v1.PickTimeSeriesFilterDirection.DirectionUnspecified,
numTimeSeries: 0,
rankingMethod: google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
secondaryAggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
timeSeriesFilterRatio: {
denominator: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
numerator: {
filter: "string",
aggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
pickTimeSeriesFilter: {
direction: google_native.monitoring.v1.PickTimeSeriesFilterDirection.DirectionUnspecified,
numTimeSeries: 0,
rankingMethod: google_native.monitoring.v1.PickTimeSeriesFilterRankingMethod.MethodUnspecified,
},
secondaryAggregation: {
alignmentPeriod: "string",
crossSeriesReducer: google_native.monitoring.v1.AggregationCrossSeriesReducer.ReduceNone,
groupByFields: ["string"],
perSeriesAligner: google_native.monitoring.v1.AggregationPerSeriesAligner.AlignNone,
},
},
timeSeriesQueryLanguage: "string",
unitOverride: "string",
},
breakdowns: [{
aggregationFunction: {
type: "string",
parameters: [{
doubleValue: 0,
intValue: "string",
}],
},
column: "string",
limit: 0,
sortOrder: google_native.monitoring.v1.BreakdownSortOrder.SortOrderUnspecified,
}],
dimensions: [{
column: "string",
columnType: "string",
floatBinSize: 0,
maxBinCount: 0,
numericBinSize: 0,
sortColumn: "string",
sortOrder: google_native.monitoring.v1.DimensionSortOrder.SortOrderUnspecified,
timeBinSize: "string",
}],
legendTemplate: "string",
measures: [{
aggregationFunction: {
type: "string",
parameters: [{
doubleValue: 0,
intValue: "string",
}],
},
column: "string",
}],
minAlignmentPeriod: "string",
plotType: google_native.monitoring.v1.DataSetPlotType.PlotTypeUnspecified,
targetAxis: google_native.monitoring.v1.DataSetTargetAxis.TargetAxisUnspecified,
}],
chartOptions: {
displayHorizontal: false,
mode: google_native.monitoring.v1.ChartOptionsMode.ModeUnspecified,
},
thresholds: [{
color: google_native.monitoring.v1.ThresholdColor.ColorUnspecified,
direction: google_native.monitoring.v1.ThresholdDirection.DirectionUnspecified,
label: "string",
targetAxis: google_native.monitoring.v1.ThresholdTargetAxis.TargetAxisUnspecified,
value: 0,
}],
timeshiftDuration: "string",
xAxis: {
label: "string",
scale: google_native.monitoring.v1.AxisScale.ScaleUnspecified,
},
y2Axis: {
label: "string",
scale: google_native.monitoring.v1.AxisScale.ScaleUnspecified,
},
yAxis: {
label: "string",
scale: google_native.monitoring.v1.AxisScale.ScaleUnspecified,
},
},
}],
}],
},
});
type: google-native:monitoring/v1:Dashboard
properties:
columnLayout:
columns:
- weight: string
widgets:
- alertChart:
name: string
blank: {}
collapsibleGroup:
collapsed: false
errorReportingPanel:
projectNames:
- string
services:
- string
versions:
- string
id: string
incidentList:
monitoredResources:
- labels:
string: string
type: string
policyNames:
- string
logsPanel:
filter: string
resourceNames:
- string
pieChart:
chartType: PIE_CHART_TYPE_UNSPECIFIED
dataSets:
- minAlignmentPeriod: string
sliceNameTemplate: string
timeSeriesQuery:
opsAnalyticsQuery:
sql: string
outputFullDuration: false
prometheusQuery: string
timeSeriesFilter:
aggregation:
alignmentPeriod: string
crossSeriesReducer: REDUCE_NONE
groupByFields:
- string
perSeriesAligner: ALIGN_NONE
filter: string
pickTimeSeriesFilter:
direction: DIRECTION_UNSPECIFIED
numTimeSeries: 0
rankingMethod: METHOD_UNSPECIFIED
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: REDUCE_NONE
groupByFields:
- string
perSeriesAligner: ALIGN_NONE
timeSeriesFilterRatio:
denominator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: REDUCE_NONE
groupByFields:
- string
perSeriesAligner: ALIGN_NONE
filter: string
numerator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: REDUCE_NONE
groupByFields:
- string
perSeriesAligner: ALIGN_NONE
filter: string
pickTimeSeriesFilter:
direction: DIRECTION_UNSPECIFIED
numTimeSeries: 0
rankingMethod: METHOD_UNSPECIFIED
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: REDUCE_NONE
groupByFields:
- string
perSeriesAligner: ALIGN_NONE
timeSeriesQueryLanguage: string
unitOverride: string
showLabels: false
scorecard:
blankView: {}
gaugeView:
lowerBound: 0
upperBound: 0
sparkChartView:
minAlignmentPeriod: string
sparkChartType: SPARK_CHART_TYPE_UNSPECIFIED
thresholds:
- color: COLOR_UNSPECIFIED
direction: DIRECTION_UNSPECIFIED
label: string
targetAxis: TARGET_AXIS_UNSPECIFIED
value: 0
timeSeriesQuery:
opsAnalyticsQuery:
sql: string
outputFullDuration: false
prometheusQuery: string
timeSeriesFilter:
aggregation:
alignmentPeriod: string
crossSeriesReducer: REDUCE_NONE
groupByFields:
- string
perSeriesAligner: ALIGN_NONE
filter: string
pickTimeSeriesFilter:
direction: DIRECTION_UNSPECIFIED
numTimeSeries: 0
rankingMethod: METHOD_UNSPECIFIED
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: REDUCE_NONE
groupByFields:
- string
perSeriesAligner: ALIGN_NONE
timeSeriesFilterRatio:
denominator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: REDUCE_NONE
groupByFields:
- string
perSeriesAligner: ALIGN_NONE
filter: string
numerator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: REDUCE_NONE
groupByFields:
- string
perSeriesAligner: ALIGN_NONE
filter: string
pickTimeSeriesFilter:
direction: DIRECTION_UNSPECIFIED
numTimeSeries: 0
rankingMethod: METHOD_UNSPECIFIED
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: REDUCE_NONE
groupByFields:
- string
perSeriesAligner: ALIGN_NONE
timeSeriesQueryLanguage: string
unitOverride: string
text:
content: string
format: FORMAT_UNSPECIFIED
style:
backgroundColor: string
fontSize: FONT_SIZE_UNSPECIFIED
horizontalAlignment: HORIZONTAL_ALIGNMENT_UNSPECIFIED
padding: PADDING_SIZE_UNSPECIFIED
pointerLocation: POINTER_LOCATION_UNSPECIFIED
textColor: string
verticalAlignment: VERTICAL_ALIGNMENT_UNSPECIFIED
timeSeriesTable:
columnSettings:
- column: string
visible: false
dataSets:
- minAlignmentPeriod: string
tableDisplayOptions:
shownColumns:
- string
tableTemplate: string
timeSeriesQuery:
opsAnalyticsQuery:
sql: string
outputFullDuration: false
prometheusQuery: string
timeSeriesFilter:
aggregation:
alignmentPeriod: string
crossSeriesReducer: REDUCE_NONE
groupByFields:
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unitOverride: string
thresholds:
- color: COLOR_UNSPECIFIED
direction: DIRECTION_UNSPECIFIED
label: string
targetAxis: TARGET_AXIS_UNSPECIFIED
value: 0
timeshiftDuration: string
xAxis:
label: string
scale: SCALE_UNSPECIFIED
y2Axis:
label: string
scale: SCALE_UNSPECIFIED
yAxis:
label: string
scale: SCALE_UNSPECIFIED
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
In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.
The Dashboard resource accepts the following input properties:
- Display
Name string - The mutable, human-readable name.
- Column
Layout Pulumi.Google Native. Monitoring. V1. Inputs. Column Layout - The content is divided into equally spaced columns and the widgets are arranged vertically.
- Dashboard
Filters List<Pulumi.Google Native. Monitoring. V1. Inputs. Dashboard Filter> - 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.
- Grid
Layout Pulumi.Google Native. Monitoring. V1. Inputs. Grid Layout - 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
- Mosaic
Layout Pulumi.Google Native. Monitoring. V1. Inputs. Mosaic Layout - 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
- Row
Layout Pulumi.Google Native. Monitoring. V1. Inputs. Row Layout - The content is divided into equally spaced rows and the widgets are arranged horizontally.
- Display
Name string - The mutable, human-readable name.
- Column
Layout ColumnLayout Args - The content is divided into equally spaced columns and the widgets are arranged vertically.
- Dashboard
Filters []DashboardFilter Args - 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.
- Grid
Layout GridLayout Args - 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
- Mosaic
Layout MosaicLayout Args - 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
- Row
Layout RowLayout Args - The content is divided into equally spaced rows and the widgets are arranged horizontally.
- display
Name String - The mutable, human-readable name.
- column
Layout ColumnLayout - The content is divided into equally spaced columns and the widgets are arranged vertically.
- dashboard
Filters List<DashboardFilter> - 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.
- grid
Layout GridLayout - 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
- mosaic
Layout MosaicLayout - 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
- row
Layout RowLayout - The content is divided into equally spaced rows and the widgets are arranged horizontally.
- display
Name string - The mutable, human-readable name.
- column
Layout ColumnLayout - The content is divided into equally spaced columns and the widgets are arranged vertically.
- dashboard
Filters DashboardFilter[] - 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.
- grid
Layout GridLayout - 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
- mosaic
Layout MosaicLayout - 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
- row
Layout RowLayout - The content is divided into equally spaced rows and the widgets are arranged horizontally.
- display_
name str - The mutable, human-readable name.
- column_
layout ColumnLayout Args - The content is divided into equally spaced columns and the widgets are arranged vertically.
- dashboard_
filters Sequence[DashboardFilter Args] - 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 GridLayout Args - 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 MosaicLayout Args - 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 RowLayout Args - The content is divided into equally spaced rows and the widgets are arranged horizontally.
- display
Name String - The mutable, human-readable name.
- column
Layout Property Map - The content is divided into equally spaced columns and the widgets are arranged vertically.
- dashboard
Filters 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.
- grid
Layout 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
- mosaic
Layout 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
- row
Layout Property Map - The content is divided into equally spaced rows and the widgets are arranged horizontally.
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, AggregationArgs
- Alignment
Period 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.
- Cross
Series Pulumi.Reducer Google Native. Monitoring. V1. Aggregation Cross Series Reducer - 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 List<string>Fields - 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 Pulumi.Aligner Google Native. Monitoring. V1. Aggregation Per Series Aligner - 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 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.
- Cross
Series AggregationReducer Cross Series Reducer - 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 []stringFields - 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 AggregationAligner Per Series Aligner - 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 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.
- cross
Series AggregationReducer Cross Series Reducer - 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 List<String>Fields - 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 AggregationAligner Per Series Aligner - 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 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.
- cross
Series AggregationReducer Cross Series Reducer - 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 string[]Fields - 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 AggregationAligner Per Series Aligner - 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_ Aggregationreducer Cross Series Reducer - 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_ Sequence[str]fields - 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_ Aggregationaligner Per Series Aligner - 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 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.
- cross
Series "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"Reducer - 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 List<String>Fields - 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 "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"Aligner - An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned.
AggregationCrossSeriesReducer, AggregationCrossSeriesReducerArgs
- Reduce
None - REDUCE_NONENo cross-time series reduction. The output of the Aligner is returned.
- Reduce
Mean - REDUCE_MEANReduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
- Reduce
Min - REDUCE_MINReduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
- Reduce
Max - REDUCE_MAXReduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
- Reduce
Sum - REDUCE_SUMReduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
- Reduce
Stddev - REDUCE_STDDEVReduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
- Reduce
Count - REDUCE_COUNTReduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
- Reduce
Count True - REDUCE_COUNT_TRUEReduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
- Reduce
Count False - REDUCE_COUNT_FALSEReduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
- Reduce
Fraction True - REDUCE_FRACTION_TRUEReduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
- Reduce
Percentile99 - REDUCE_PERCENTILE_99Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- Reduce
Percentile95 - REDUCE_PERCENTILE_95Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- Reduce
Percentile50 - REDUCE_PERCENTILE_50Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- Reduce
Percentile05 - REDUCE_PERCENTILE_05Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- Aggregation
Cross Series Reducer Reduce None - REDUCE_NONENo cross-time series reduction. The output of the Aligner is returned.
- Aggregation
Cross Series Reducer Reduce Mean - REDUCE_MEANReduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
- Aggregation
Cross Series Reducer Reduce Min - REDUCE_MINReduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
- Aggregation
Cross Series Reducer Reduce Max - REDUCE_MAXReduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
- Aggregation
Cross Series Reducer Reduce Sum - REDUCE_SUMReduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
- Aggregation
Cross Series Reducer Reduce Stddev - REDUCE_STDDEVReduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
- Aggregation
Cross Series Reducer Reduce Count - REDUCE_COUNTReduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
- Aggregation
Cross Series Reducer Reduce Count True - REDUCE_COUNT_TRUEReduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
- Aggregation
Cross Series Reducer Reduce Count False - REDUCE_COUNT_FALSEReduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
- Aggregation
Cross Series Reducer Reduce Fraction True - REDUCE_FRACTION_TRUEReduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
- Aggregation
Cross Series Reducer Reduce Percentile99 - REDUCE_PERCENTILE_99Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- Aggregation
Cross Series Reducer Reduce Percentile95 - REDUCE_PERCENTILE_95Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- Aggregation
Cross Series Reducer Reduce Percentile50 - REDUCE_PERCENTILE_50Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- Aggregation
Cross Series Reducer Reduce Percentile05 - REDUCE_PERCENTILE_05Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- Reduce
None - REDUCE_NONENo cross-time series reduction. The output of the Aligner is returned.
- Reduce
Mean - REDUCE_MEANReduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
- Reduce
Min - REDUCE_MINReduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
- Reduce
Max - REDUCE_MAXReduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
- Reduce
Sum - REDUCE_SUMReduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
- Reduce
Stddev - REDUCE_STDDEVReduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
- Reduce
Count - REDUCE_COUNTReduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
- Reduce
Count True - REDUCE_COUNT_TRUEReduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
- Reduce
Count False - REDUCE_COUNT_FALSEReduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
- Reduce
Fraction True - REDUCE_FRACTION_TRUEReduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
- Reduce
Percentile99 - REDUCE_PERCENTILE_99Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- Reduce
Percentile95 - REDUCE_PERCENTILE_95Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- Reduce
Percentile50 - REDUCE_PERCENTILE_50Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- Reduce
Percentile05 - REDUCE_PERCENTILE_05Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- Reduce
None - REDUCE_NONENo cross-time series reduction. The output of the Aligner is returned.
- Reduce
Mean - REDUCE_MEANReduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
- Reduce
Min - REDUCE_MINReduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
- Reduce
Max - REDUCE_MAXReduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
- Reduce
Sum - REDUCE_SUMReduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
- Reduce
Stddev - REDUCE_STDDEVReduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
- Reduce
Count - REDUCE_COUNTReduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
- Reduce
Count True - REDUCE_COUNT_TRUEReduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
- Reduce
Count False - REDUCE_COUNT_FALSEReduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
- Reduce
Fraction True - REDUCE_FRACTION_TRUEReduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
- Reduce
Percentile99 - REDUCE_PERCENTILE_99Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- Reduce
Percentile95 - REDUCE_PERCENTILE_95Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- Reduce
Percentile50 - REDUCE_PERCENTILE_50Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- Reduce
Percentile05 - REDUCE_PERCENTILE_05Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- REDUCE_NONE
- REDUCE_NONENo cross-time series reduction. The output of the Aligner is returned.
- REDUCE_MEAN
- REDUCE_MEANReduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
- REDUCE_MIN
- REDUCE_MINReduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
- REDUCE_MAX
- REDUCE_MAXReduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
- REDUCE_SUM
- REDUCE_SUMReduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
- REDUCE_STDDEV
- REDUCE_STDDEVReduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
- REDUCE_COUNT
- REDUCE_COUNTReduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
- REDUCE_COUNT_TRUE
- REDUCE_COUNT_TRUEReduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
- REDUCE_COUNT_FALSE
- REDUCE_COUNT_FALSEReduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
- REDUCE_FRACTION_TRUE
- REDUCE_FRACTION_TRUEReduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
- REDUCE_PERCENTILE99
- REDUCE_PERCENTILE_99Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- REDUCE_PERCENTILE95
- REDUCE_PERCENTILE_95Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- REDUCE_PERCENTILE50
- REDUCE_PERCENTILE_50Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- REDUCE_PERCENTILE05
- REDUCE_PERCENTILE_05Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- "REDUCE_NONE"
- REDUCE_NONENo cross-time series reduction. The output of the Aligner is returned.
- "REDUCE_MEAN"
- REDUCE_MEANReduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
- "REDUCE_MIN"
- REDUCE_MINReduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
- "REDUCE_MAX"
- REDUCE_MAXReduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
- "REDUCE_SUM"
- REDUCE_SUMReduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
- "REDUCE_STDDEV"
- REDUCE_STDDEVReduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
- "REDUCE_COUNT"
- REDUCE_COUNTReduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
- "REDUCE_COUNT_TRUE"
- REDUCE_COUNT_TRUEReduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
- "REDUCE_COUNT_FALSE"
- REDUCE_COUNT_FALSEReduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
- "REDUCE_FRACTION_TRUE"
- REDUCE_FRACTION_TRUEReduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
- "REDUCE_PERCENTILE_99"
- REDUCE_PERCENTILE_99Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- "REDUCE_PERCENTILE_95"
- REDUCE_PERCENTILE_95Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- "REDUCE_PERCENTILE_50"
- REDUCE_PERCENTILE_50Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
- "REDUCE_PERCENTILE_05"
- REDUCE_PERCENTILE_05Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
AggregationFunction, AggregationFunctionArgs
- Type string
- The type of aggregation function, must be one of the following: "none" - no function. "percentile" - APPROX_QUANTILES() - 1 parameter numeric value "average" - AVG() "count" - COUNT() "count-distinct" - COUNT(DISTINCT) "count-distinct-approx" - APPROX_COUNT_DISTINCT() "max" - MAX() "min" - MIN() "sum" - SUM()
- Parameters
List<Pulumi.
Google Native. Monitoring. V1. Inputs. Parameter> - Optional. Parameters applied to the aggregation function. Only used for functions that require them.
- Type string
- The type of aggregation function, must be one of the following: "none" - no function. "percentile" - APPROX_QUANTILES() - 1 parameter numeric value "average" - AVG() "count" - COUNT() "count-distinct" - COUNT(DISTINCT) "count-distinct-approx" - APPROX_COUNT_DISTINCT() "max" - MAX() "min" - MIN() "sum" - SUM()
- Parameters []Parameter
- Optional. Parameters applied to the aggregation function. Only used for functions that require them.
- type String
- The type of aggregation function, must be one of the following: "none" - no function. "percentile" - APPROX_QUANTILES() - 1 parameter numeric value "average" - AVG() "count" - COUNT() "count-distinct" - COUNT(DISTINCT) "count-distinct-approx" - APPROX_COUNT_DISTINCT() "max" - MAX() "min" - MIN() "sum" - SUM()
- parameters List<Parameter>
- Optional. Parameters applied to the aggregation function. Only used for functions that require them.
- type string
- The type of aggregation function, must be one of the following: "none" - no function. "percentile" - APPROX_QUANTILES() - 1 parameter numeric value "average" - AVG() "count" - COUNT() "count-distinct" - COUNT(DISTINCT) "count-distinct-approx" - APPROX_COUNT_DISTINCT() "max" - MAX() "min" - MIN() "sum" - SUM()
- parameters Parameter[]
- Optional. Parameters applied to the aggregation function. Only used for functions that require them.
- type str
- The type of aggregation function, must be one of the following: "none" - no function. "percentile" - APPROX_QUANTILES() - 1 parameter numeric value "average" - AVG() "count" - COUNT() "count-distinct" - COUNT(DISTINCT) "count-distinct-approx" - APPROX_COUNT_DISTINCT() "max" - MAX() "min" - MIN() "sum" - SUM()
- parameters Sequence[Parameter]
- Optional. Parameters applied to the aggregation function. Only used for functions that require them.
- type String
- The type of aggregation function, must be one of the following: "none" - no function. "percentile" - APPROX_QUANTILES() - 1 parameter numeric value "average" - AVG() "count" - COUNT() "count-distinct" - COUNT(DISTINCT) "count-distinct-approx" - APPROX_COUNT_DISTINCT() "max" - MAX() "min" - MIN() "sum" - SUM()
- parameters List<Property Map>
- Optional. Parameters applied to the aggregation function. Only used for functions that require them.
AggregationFunctionResponse, AggregationFunctionResponseArgs
- Parameters
List<Pulumi.
Google Native. Monitoring. V1. Inputs. Parameter Response> - Optional. Parameters applied to the aggregation function. Only used for functions that require them.
- Type string
- The type of aggregation function, must be one of the following: "none" - no function. "percentile" - APPROX_QUANTILES() - 1 parameter numeric value "average" - AVG() "count" - COUNT() "count-distinct" - COUNT(DISTINCT) "count-distinct-approx" - APPROX_COUNT_DISTINCT() "max" - MAX() "min" - MIN() "sum" - SUM()
- Parameters
[]Parameter
Response - Optional. Parameters applied to the aggregation function. Only used for functions that require them.
- Type string
- The type of aggregation function, must be one of the following: "none" - no function. "percentile" - APPROX_QUANTILES() - 1 parameter numeric value "average" - AVG() "count" - COUNT() "count-distinct" - COUNT(DISTINCT) "count-distinct-approx" - APPROX_COUNT_DISTINCT() "max" - MAX() "min" - MIN() "sum" - SUM()
- parameters
List<Parameter
Response> - Optional. Parameters applied to the aggregation function. Only used for functions that require them.
- type String
- The type of aggregation function, must be one of the following: "none" - no function. "percentile" - APPROX_QUANTILES() - 1 parameter numeric value "average" - AVG() "count" - COUNT() "count-distinct" - COUNT(DISTINCT) "count-distinct-approx" - APPROX_COUNT_DISTINCT() "max" - MAX() "min" - MIN() "sum" - SUM()
- parameters
Parameter
Response[] - Optional. Parameters applied to the aggregation function. Only used for functions that require them.
- type string
- The type of aggregation function, must be one of the following: "none" - no function. "percentile" - APPROX_QUANTILES() - 1 parameter numeric value "average" - AVG() "count" - COUNT() "count-distinct" - COUNT(DISTINCT) "count-distinct-approx" - APPROX_COUNT_DISTINCT() "max" - MAX() "min" - MIN() "sum" - SUM()
- parameters
Sequence[Parameter
Response] - Optional. Parameters applied to the aggregation function. Only used for functions that require them.
- type str
- The type of aggregation function, must be one of the following: "none" - no function. "percentile" - APPROX_QUANTILES() - 1 parameter numeric value "average" - AVG() "count" - COUNT() "count-distinct" - COUNT(DISTINCT) "count-distinct-approx" - APPROX_COUNT_DISTINCT() "max" - MAX() "min" - MIN() "sum" - SUM()
- parameters List<Property Map>
- Optional. Parameters applied to the aggregation function. Only used for functions that require them.
- type String
- The type of aggregation function, must be one of the following: "none" - no function. "percentile" - APPROX_QUANTILES() - 1 parameter numeric value "average" - AVG() "count" - COUNT() "count-distinct" - COUNT(DISTINCT) "count-distinct-approx" - APPROX_COUNT_DISTINCT() "max" - MAX() "min" - MIN() "sum" - SUM()
AggregationPerSeriesAligner, AggregationPerSeriesAlignerArgs
- Align
None - ALIGN_NONENo alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
- Align
Delta - ALIGN_DELTAAlign and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
- Align
Rate - ALIGN_RATEAlign and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.
- Align
Interpolate - ALIGN_INTERPOLATEAlign by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- Align
Next Older - ALIGN_NEXT_OLDERAlign by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
- Align
Min - ALIGN_MINAlign the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- Align
Max - ALIGN_MAXAlign the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- Align
Mean - ALIGN_MEANAlign the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
- Align
Count - ALIGN_COUNTAlign the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
- Align
Sum - ALIGN_SUMAlign the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
- Align
Stddev - ALIGN_STDDEVAlign the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
- Align
Count True - ALIGN_COUNT_TRUEAlign the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
- Align
Count False - ALIGN_COUNT_FALSEAlign the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
- Align
Fraction True - ALIGN_FRACTION_TRUEAlign the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
- Align
Percentile99 - ALIGN_PERCENTILE_99Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- Align
Percentile95 - ALIGN_PERCENTILE_95Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- Align
Percentile50 - ALIGN_PERCENTILE_50Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- Align
Percentile05 - ALIGN_PERCENTILE_05Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- Align
Percent Change - ALIGN_PERCENT_CHANGEAlign and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.
- Aggregation
Per Series Aligner Align None - ALIGN_NONENo alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
- Aggregation
Per Series Aligner Align Delta - ALIGN_DELTAAlign and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
- Aggregation
Per Series Aligner Align Rate - ALIGN_RATEAlign and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.
- Aggregation
Per Series Aligner Align Interpolate - ALIGN_INTERPOLATEAlign by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- Aggregation
Per Series Aligner Align Next Older - ALIGN_NEXT_OLDERAlign by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
- Aggregation
Per Series Aligner Align Min - ALIGN_MINAlign the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- Aggregation
Per Series Aligner Align Max - ALIGN_MAXAlign the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- Aggregation
Per Series Aligner Align Mean - ALIGN_MEANAlign the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
- Aggregation
Per Series Aligner Align Count - ALIGN_COUNTAlign the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
- Aggregation
Per Series Aligner Align Sum - ALIGN_SUMAlign the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
- Aggregation
Per Series Aligner Align Stddev - ALIGN_STDDEVAlign the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
- Aggregation
Per Series Aligner Align Count True - ALIGN_COUNT_TRUEAlign the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
- Aggregation
Per Series Aligner Align Count False - ALIGN_COUNT_FALSEAlign the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
- Aggregation
Per Series Aligner Align Fraction True - ALIGN_FRACTION_TRUEAlign the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
- Aggregation
Per Series Aligner Align Percentile99 - ALIGN_PERCENTILE_99Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- Aggregation
Per Series Aligner Align Percentile95 - ALIGN_PERCENTILE_95Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- Aggregation
Per Series Aligner Align Percentile50 - ALIGN_PERCENTILE_50Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- Aggregation
Per Series Aligner Align Percentile05 - ALIGN_PERCENTILE_05Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- Aggregation
Per Series Aligner Align Percent Change - ALIGN_PERCENT_CHANGEAlign and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.
- Align
None - ALIGN_NONENo alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
- Align
Delta - ALIGN_DELTAAlign and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
- Align
Rate - ALIGN_RATEAlign and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.
- Align
Interpolate - ALIGN_INTERPOLATEAlign by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- Align
Next Older - ALIGN_NEXT_OLDERAlign by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
- Align
Min - ALIGN_MINAlign the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- Align
Max - ALIGN_MAXAlign the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- Align
Mean - ALIGN_MEANAlign the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
- Align
Count - ALIGN_COUNTAlign the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
- Align
Sum - ALIGN_SUMAlign the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
- Align
Stddev - ALIGN_STDDEVAlign the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
- Align
Count True - ALIGN_COUNT_TRUEAlign the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
- Align
Count False - ALIGN_COUNT_FALSEAlign the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
- Align
Fraction True - ALIGN_FRACTION_TRUEAlign the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
- Align
Percentile99 - ALIGN_PERCENTILE_99Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- Align
Percentile95 - ALIGN_PERCENTILE_95Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- Align
Percentile50 - ALIGN_PERCENTILE_50Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- Align
Percentile05 - ALIGN_PERCENTILE_05Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- Align
Percent Change - ALIGN_PERCENT_CHANGEAlign and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.
- Align
None - ALIGN_NONENo alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
- Align
Delta - ALIGN_DELTAAlign and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
- Align
Rate - ALIGN_RATEAlign and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.
- Align
Interpolate - ALIGN_INTERPOLATEAlign by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- Align
Next Older - ALIGN_NEXT_OLDERAlign by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
- Align
Min - ALIGN_MINAlign the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- Align
Max - ALIGN_MAXAlign the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- Align
Mean - ALIGN_MEANAlign the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
- Align
Count - ALIGN_COUNTAlign the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
- Align
Sum - ALIGN_SUMAlign the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
- Align
Stddev - ALIGN_STDDEVAlign the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
- Align
Count True - ALIGN_COUNT_TRUEAlign the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
- Align
Count False - ALIGN_COUNT_FALSEAlign the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
- Align
Fraction True - ALIGN_FRACTION_TRUEAlign the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
- Align
Percentile99 - ALIGN_PERCENTILE_99Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- Align
Percentile95 - ALIGN_PERCENTILE_95Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- Align
Percentile50 - ALIGN_PERCENTILE_50Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- Align
Percentile05 - ALIGN_PERCENTILE_05Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- Align
Percent Change - ALIGN_PERCENT_CHANGEAlign and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.
- ALIGN_NONE
- ALIGN_NONENo alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
- ALIGN_DELTA
- ALIGN_DELTAAlign and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
- ALIGN_RATE
- ALIGN_RATEAlign and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.
- ALIGN_INTERPOLATE
- ALIGN_INTERPOLATEAlign by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- ALIGN_NEXT_OLDER
- ALIGN_NEXT_OLDERAlign by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
- ALIGN_MIN
- ALIGN_MINAlign the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- ALIGN_MAX
- ALIGN_MAXAlign the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- ALIGN_MEAN
- ALIGN_MEANAlign the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
- ALIGN_COUNT
- ALIGN_COUNTAlign the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
- ALIGN_SUM
- ALIGN_SUMAlign the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
- ALIGN_STDDEV
- ALIGN_STDDEVAlign the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
- ALIGN_COUNT_TRUE
- ALIGN_COUNT_TRUEAlign the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
- ALIGN_COUNT_FALSE
- ALIGN_COUNT_FALSEAlign the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
- ALIGN_FRACTION_TRUE
- ALIGN_FRACTION_TRUEAlign the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
- ALIGN_PERCENTILE99
- ALIGN_PERCENTILE_99Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- ALIGN_PERCENTILE95
- ALIGN_PERCENTILE_95Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- ALIGN_PERCENTILE50
- ALIGN_PERCENTILE_50Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- ALIGN_PERCENTILE05
- ALIGN_PERCENTILE_05Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- ALIGN_PERCENT_CHANGE
- ALIGN_PERCENT_CHANGEAlign and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.
- "ALIGN_NONE"
- ALIGN_NONENo alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
- "ALIGN_DELTA"
- ALIGN_DELTAAlign and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
- "ALIGN_RATE"
- ALIGN_RATEAlign and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.
- "ALIGN_INTERPOLATE"
- ALIGN_INTERPOLATEAlign by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- "ALIGN_NEXT_OLDER"
- ALIGN_NEXT_OLDERAlign by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
- "ALIGN_MIN"
- ALIGN_MINAlign the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- "ALIGN_MAX"
- ALIGN_MAXAlign the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
- "ALIGN_MEAN"
- ALIGN_MEANAlign the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
- "ALIGN_COUNT"
- ALIGN_COUNTAlign the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
- "ALIGN_SUM"
- ALIGN_SUMAlign the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
- "ALIGN_STDDEV"
- ALIGN_STDDEVAlign the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
- "ALIGN_COUNT_TRUE"
- ALIGN_COUNT_TRUEAlign the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
- "ALIGN_COUNT_FALSE"
- ALIGN_COUNT_FALSEAlign the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
- "ALIGN_FRACTION_TRUE"
- ALIGN_FRACTION_TRUEAlign the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
- "ALIGN_PERCENTILE_99"
- ALIGN_PERCENTILE_99Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- "ALIGN_PERCENTILE_95"
- ALIGN_PERCENTILE_95Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- "ALIGN_PERCENTILE_50"
- ALIGN_PERCENTILE_50Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- "ALIGN_PERCENTILE_05"
- ALIGN_PERCENTILE_05Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
- "ALIGN_PERCENT_CHANGE"
- ALIGN_PERCENT_CHANGEAlign and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.
AggregationResponse, AggregationResponseArgs
- Alignment
Period 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.
- Cross
Series stringReducer - 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 List<string>Fields - 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 stringAligner - 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 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.
- Cross
Series stringReducer - 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 []stringFields - 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 stringAligner - 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 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.
- cross
Series StringReducer - 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 List<String>Fields - 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 StringAligner - 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 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.
- cross
Series stringReducer - 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 string[]Fields - 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 stringAligner - 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_ strreducer - 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_ Sequence[str]fields - 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_ straligner - 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 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.
- cross
Series StringReducer - 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 List<String>Fields - 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 StringAligner - 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, AlertChartArgs
- 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, AlertChartResponseArgs
- 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, AxisArgs
- Label string
- The label of the axis.
- Scale
Pulumi.
Google Native. Monitoring. V1. Axis Scale - 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, AxisResponseArgs
AxisScale, AxisScaleArgs
- Scale
Unspecified - SCALE_UNSPECIFIEDScale is unspecified. The view will default to LINEAR.
- Linear
- LINEARLinear scale.
- Log10
- LOG10Logarithmic scale (base 10).
- Axis
Scale Scale Unspecified - SCALE_UNSPECIFIEDScale is unspecified. The view will default to LINEAR.
- Axis
Scale Linear - LINEARLinear scale.
- Axis
Scale Log10 - LOG10Logarithmic scale (base 10).
- Scale
Unspecified - SCALE_UNSPECIFIEDScale is unspecified. The view will default to LINEAR.
- Linear
- LINEARLinear scale.
- Log10
- LOG10Logarithmic scale (base 10).
- Scale
Unspecified - SCALE_UNSPECIFIEDScale is unspecified. The view will default to LINEAR.
- Linear
- LINEARLinear scale.
- Log10
- LOG10Logarithmic scale (base 10).
- SCALE_UNSPECIFIED
- SCALE_UNSPECIFIEDScale is unspecified. The view will default to LINEAR.
- LINEAR
- LINEARLinear scale.
- LOG10
- LOG10Logarithmic scale (base 10).
- "SCALE_UNSPECIFIED"
- SCALE_UNSPECIFIEDScale is unspecified. The view will default to LINEAR.
- "LINEAR"
- LINEARLinear scale.
- "LOG10"
- LOG10Logarithmic scale (base 10).
Breakdown, BreakdownArgs
- Aggregation
Function Pulumi.Google Native. Monitoring. V1. Inputs. Aggregation Function - The Aggregation function is applied across all data in each breakdown created.
- Column string
- The name of the column in the dataset containing the breakdown values.
- Limit int
- A limit to the number of breakdowns. If set to zero then all possible breakdowns are applied. The list of breakdowns is dependent on the value of the sort_order field.
- Sort
Order Pulumi.Google Native. Monitoring. V1. Breakdown Sort Order - The sort order is applied to the values of the breakdown column.
- Aggregation
Function AggregationFunction - The Aggregation function is applied across all data in each breakdown created.
- Column string
- The name of the column in the dataset containing the breakdown values.
- Limit int
- A limit to the number of breakdowns. If set to zero then all possible breakdowns are applied. The list of breakdowns is dependent on the value of the sort_order field.
- Sort
Order BreakdownSort Order - The sort order is applied to the values of the breakdown column.
- aggregation
Function AggregationFunction - The Aggregation function is applied across all data in each breakdown created.
- column String
- The name of the column in the dataset containing the breakdown values.
- limit Integer
- A limit to the number of breakdowns. If set to zero then all possible breakdowns are applied. The list of breakdowns is dependent on the value of the sort_order field.
- sort
Order BreakdownSort Order - The sort order is applied to the values of the breakdown column.
- aggregation
Function AggregationFunction - The Aggregation function is applied across all data in each breakdown created.
- column string
- The name of the column in the dataset containing the breakdown values.
- limit number
- A limit to the number of breakdowns. If set to zero then all possible breakdowns are applied. The list of breakdowns is dependent on the value of the sort_order field.
- sort
Order BreakdownSort Order - The sort order is applied to the values of the breakdown column.
- aggregation_
function AggregationFunction - The Aggregation function is applied across all data in each breakdown created.
- column str
- The name of the column in the dataset containing the breakdown values.
- limit int
- A limit to the number of breakdowns. If set to zero then all possible breakdowns are applied. The list of breakdowns is dependent on the value of the sort_order field.
- sort_
order BreakdownSort Order - The sort order is applied to the values of the breakdown column.
- aggregation
Function Property Map - The Aggregation function is applied across all data in each breakdown created.
- column String
- The name of the column in the dataset containing the breakdown values.
- limit Number
- A limit to the number of breakdowns. If set to zero then all possible breakdowns are applied. The list of breakdowns is dependent on the value of the sort_order field.
- sort
Order "SORT_ORDER_UNSPECIFIED" | "SORT_ORDER_NONE" | "SORT_ORDER_ASCENDING" | "SORT_ORDER_DESCENDING" - The sort order is applied to the values of the breakdown column.
BreakdownResponse, BreakdownResponseArgs
- Aggregation
Function Pulumi.Google Native. Monitoring. V1. Inputs. Aggregation Function Response - The Aggregation function is applied across all data in each breakdown created.
- Column string
- The name of the column in the dataset containing the breakdown values.
- Limit int
- A limit to the number of breakdowns. If set to zero then all possible breakdowns are applied. The list of breakdowns is dependent on the value of the sort_order field.
- Sort
Order string - The sort order is applied to the values of the breakdown column.
- Aggregation
Function AggregationFunction Response - The Aggregation function is applied across all data in each breakdown created.
- Column string
- The name of the column in the dataset containing the breakdown values.
- Limit int
- A limit to the number of breakdowns. If set to zero then all possible breakdowns are applied. The list of breakdowns is dependent on the value of the sort_order field.
- Sort
Order string - The sort order is applied to the values of the breakdown column.
- aggregation
Function AggregationFunction Response - The Aggregation function is applied across all data in each breakdown created.
- column String
- The name of the column in the dataset containing the breakdown values.
- limit Integer
- A limit to the number of breakdowns. If set to zero then all possible breakdowns are applied. The list of breakdowns is dependent on the value of the sort_order field.
- sort
Order String - The sort order is applied to the values of the breakdown column.
- aggregation
Function AggregationFunction Response - The Aggregation function is applied across all data in each breakdown created.
- column string
- The name of the column in the dataset containing the breakdown values.
- limit number
- A limit to the number of breakdowns. If set to zero then all possible breakdowns are applied. The list of breakdowns is dependent on the value of the sort_order field.
- sort
Order string - The sort order is applied to the values of the breakdown column.
- aggregation_
function AggregationFunction Response - The Aggregation function is applied across all data in each breakdown created.
- column str
- The name of the column in the dataset containing the breakdown values.
- limit int
- A limit to the number of breakdowns. If set to zero then all possible breakdowns are applied. The list of breakdowns is dependent on the value of the sort_order field.
- sort_
order str - The sort order is applied to the values of the breakdown column.
- aggregation
Function Property Map - The Aggregation function is applied across all data in each breakdown created.
- column String
- The name of the column in the dataset containing the breakdown values.
- limit Number
- A limit to the number of breakdowns. If set to zero then all possible breakdowns are applied. The list of breakdowns is dependent on the value of the sort_order field.
- sort
Order String - The sort order is applied to the values of the breakdown column.
BreakdownSortOrder, BreakdownSortOrderArgs
- Sort
Order Unspecified - SORT_ORDER_UNSPECIFIEDAn unspecified sort order. This option is invalid when sorting is required.
- Sort
Order None - SORT_ORDER_NONENo sorting is applied.
- Sort
Order Ascending - SORT_ORDER_ASCENDINGThe lowest-valued entries are selected first.
- Sort
Order Descending - SORT_ORDER_DESCENDINGThe highest-valued entries are selected first.
- Breakdown
Sort Order Sort Order Unspecified - SORT_ORDER_UNSPECIFIEDAn unspecified sort order. This option is invalid when sorting is required.
- Breakdown
Sort Order Sort Order None - SORT_ORDER_NONENo sorting is applied.
- Breakdown
Sort Order Sort Order Ascending - SORT_ORDER_ASCENDINGThe lowest-valued entries are selected first.
- Breakdown
Sort Order Sort Order Descending - SORT_ORDER_DESCENDINGThe highest-valued entries are selected first.
- Sort
Order Unspecified - SORT_ORDER_UNSPECIFIEDAn unspecified sort order. This option is invalid when sorting is required.
- Sort
Order None - SORT_ORDER_NONENo sorting is applied.
- Sort
Order Ascending - SORT_ORDER_ASCENDINGThe lowest-valued entries are selected first.
- Sort
Order Descending - SORT_ORDER_DESCENDINGThe highest-valued entries are selected first.
- Sort
Order Unspecified - SORT_ORDER_UNSPECIFIEDAn unspecified sort order. This option is invalid when sorting is required.
- Sort
Order None - SORT_ORDER_NONENo sorting is applied.
- Sort
Order Ascending - SORT_ORDER_ASCENDINGThe lowest-valued entries are selected first.
- Sort
Order Descending - SORT_ORDER_DESCENDINGThe highest-valued entries are selected first.
- SORT_ORDER_UNSPECIFIED
- SORT_ORDER_UNSPECIFIEDAn unspecified sort order. This option is invalid when sorting is required.
- SORT_ORDER_NONE
- SORT_ORDER_NONENo sorting is applied.
- SORT_ORDER_ASCENDING
- SORT_ORDER_ASCENDINGThe lowest-valued entries are selected first.
- SORT_ORDER_DESCENDING
- SORT_ORDER_DESCENDINGThe highest-valued entries are selected first.
- "SORT_ORDER_UNSPECIFIED"
- SORT_ORDER_UNSPECIFIEDAn unspecified sort order. This option is invalid when sorting is required.
- "SORT_ORDER_NONE"
- SORT_ORDER_NONENo sorting is applied.
- "SORT_ORDER_ASCENDING"
- SORT_ORDER_ASCENDINGThe lowest-valued entries are selected first.
- "SORT_ORDER_DESCENDING"
- SORT_ORDER_DESCENDINGThe highest-valued entries are selected first.
ChartOptions, ChartOptionsArgs
- Display
Horizontal bool - Preview: Configures whether the charted values are shown on the horizontal or vertical axis. By default, values are represented the vertical axis. This is a preview feature and may be subject to change before final release.
- Mode
Pulumi.
Google Native. Monitoring. V1. Chart Options Mode - The chart mode.
- Display
Horizontal bool - Preview: Configures whether the charted values are shown on the horizontal or vertical axis. By default, values are represented the vertical axis. This is a preview feature and may be subject to change before final release.
- Mode
Chart
Options Mode - The chart mode.
- display
Horizontal Boolean - Preview: Configures whether the charted values are shown on the horizontal or vertical axis. By default, values are represented the vertical axis. This is a preview feature and may be subject to change before final release.
- mode
Chart
Options Mode - The chart mode.
- display
Horizontal boolean - Preview: Configures whether the charted values are shown on the horizontal or vertical axis. By default, values are represented the vertical axis. This is a preview feature and may be subject to change before final release.
- mode
Chart
Options Mode - The chart mode.
- display_
horizontal bool - Preview: Configures whether the charted values are shown on the horizontal or vertical axis. By default, values are represented the vertical axis. This is a preview feature and may be subject to change before final release.
- mode
Chart
Options Mode - The chart mode.
- display
Horizontal Boolean - Preview: Configures whether the charted values are shown on the horizontal or vertical axis. By default, values are represented the vertical axis. This is a preview feature and may be subject to change before final release.
- mode "MODE_UNSPECIFIED" | "COLOR" | "X_RAY" | "STATS"
- The chart mode.
ChartOptionsMode, ChartOptionsModeArgs
- Mode
Unspecified - MODE_UNSPECIFIEDMode is unspecified. The view will default to COLOR.
- Color
- COLORThe chart distinguishes data series using different color. Line colors may get reused when there are many lines in the chart.
- XRay
- X_RAYThe chart uses the Stackdriver x-ray mode, in which each data set is plotted using the same semi-transparent color.
- Stats
- STATSThe chart displays statistics such as average, median, 95th percentile, and more.
- Chart
Options Mode Mode Unspecified - MODE_UNSPECIFIEDMode is unspecified. The view will default to COLOR.
- Chart
Options Mode Color - COLORThe chart distinguishes data series using different color. Line colors may get reused when there are many lines in the chart.
- Chart
Options Mode XRay - X_RAYThe chart uses the Stackdriver x-ray mode, in which each data set is plotted using the same semi-transparent color.
- Chart
Options Mode Stats - STATSThe chart displays statistics such as average, median, 95th percentile, and more.
- Mode
Unspecified - MODE_UNSPECIFIEDMode is unspecified. The view will default to COLOR.
- Color
- COLORThe chart distinguishes data series using different color. Line colors may get reused when there are many lines in the chart.
- XRay
- X_RAYThe chart uses the Stackdriver x-ray mode, in which each data set is plotted using the same semi-transparent color.
- Stats
- STATSThe chart displays statistics such as average, median, 95th percentile, and more.
- Mode
Unspecified - MODE_UNSPECIFIEDMode is unspecified. The view will default to COLOR.
- Color
- COLORThe chart distinguishes data series using different color. Line colors may get reused when there are many lines in the chart.
- XRay
- X_RAYThe chart uses the Stackdriver x-ray mode, in which each data set is plotted using the same semi-transparent color.
- Stats
- STATSThe chart displays statistics such as average, median, 95th percentile, and more.
- MODE_UNSPECIFIED
- MODE_UNSPECIFIEDMode is unspecified. The view will default to COLOR.
- COLOR
- COLORThe chart distinguishes data series using different color. Line colors may get reused when there are many lines in the chart.
- X_RAY
- X_RAYThe chart uses the Stackdriver x-ray mode, in which each data set is plotted using the same semi-transparent color.
- STATS
- STATSThe chart displays statistics such as average, median, 95th percentile, and more.
- "MODE_UNSPECIFIED"
- MODE_UNSPECIFIEDMode is unspecified. The view will default to COLOR.
- "COLOR"
- COLORThe chart distinguishes data series using different color. Line colors may get reused when there are many lines in the chart.
- "X_RAY"
- X_RAYThe chart uses the Stackdriver x-ray mode, in which each data set is plotted using the same semi-transparent color.
- "STATS"
- STATSThe chart displays statistics such as average, median, 95th percentile, and more.
ChartOptionsResponse, ChartOptionsResponseArgs
- Display
Horizontal bool - Preview: Configures whether the charted values are shown on the horizontal or vertical axis. By default, values are represented the vertical axis. This is a preview feature and may be subject to change before final release.
- Mode string
- The chart mode.
- Display
Horizontal bool - Preview: Configures whether the charted values are shown on the horizontal or vertical axis. By default, values are represented the vertical axis. This is a preview feature and may be subject to change before final release.
- Mode string
- The chart mode.
- display
Horizontal Boolean - Preview: Configures whether the charted values are shown on the horizontal or vertical axis. By default, values are represented the vertical axis. This is a preview feature and may be subject to change before final release.
- mode String
- The chart mode.
- display
Horizontal boolean - Preview: Configures whether the charted values are shown on the horizontal or vertical axis. By default, values are represented the vertical axis. This is a preview feature and may be subject to change before final release.
- mode string
- The chart mode.
- display_
horizontal bool - Preview: Configures whether the charted values are shown on the horizontal or vertical axis. By default, values are represented the vertical axis. This is a preview feature and may be subject to change before final release.
- mode str
- The chart mode.
- display
Horizontal Boolean - Preview: Configures whether the charted values are shown on the horizontal or vertical axis. By default, values are represented the vertical axis. This is a preview feature and may be subject to change before final release.
- mode String
- The chart mode.
CollapsibleGroup, CollapsibleGroupArgs
- 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, CollapsibleGroupResponseArgs
- 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, ColumnArgs
- 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.
Google Native. 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, ColumnLayoutArgs
- Columns
List<Pulumi.
Google Native. Monitoring. V1. Inputs. Column> - The columns of content to display.
- columns List<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, ColumnLayoutResponseArgs
- Columns
List<Pulumi.
Google Native. Monitoring. V1. Inputs. Column Response> - The columns of content to display.
- Columns
[]Column
Response - The columns of content to display.
- columns
List<Column
Response> - The columns of content to display.
- columns
Column
Response[] - The columns of content to display.
- columns
Sequence[Column
Response] - The columns of content to display.
- columns List<Property Map>
- The columns of content to display.
ColumnResponse, ColumnResponseArgs
- 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.
Google Native. Monitoring. V1. Inputs. Widget Response> - 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
Response - 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
Response> - 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
Response[] - 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
Response] - 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, ColumnSettingsArgs
ColumnSettingsResponse, ColumnSettingsResponseArgs
DashboardFilter, DashboardFilterArgs
- Label
Key string - The key for the label
- Filter
Type Pulumi.Google Native. Monitoring. V1. Dashboard Filter Filter Type - The specified filter type
- String
Value string - A variable-length string value.
- Template
Variable 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 string - The key for the label
- Filter
Type DashboardFilter Filter Type - The specified filter type
- String
Value string - A variable-length string value.
- Template
Variable 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 String - The key for the label
- filter
Type DashboardFilter Filter Type - The specified filter type
- string
Value String - A variable-length string value.
- template
Variable 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 string - The key for the label
- filter
Type DashboardFilter Filter Type - The specified filter type
- string
Value string - A variable-length string value.
- template
Variable 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 DashboardFilter Filter Type - 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.
- label
Key String - The key for the label
- filter
Type "FILTER_TYPE_UNSPECIFIED" | "RESOURCE_LABEL" | "METRIC_LABEL" | "USER_METADATA_LABEL" | "SYSTEM_METADATA_LABEL" | "GROUP" - The specified filter type
- string
Value String - A variable-length string value.
- template
Variable 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, DashboardFilterFilterTypeArgs
- Filter
Type Unspecified - FILTER_TYPE_UNSPECIFIEDFilter type is unspecified. This is not valid in a well-formed request.
- Resource
Label - RESOURCE_LABELFilter on a resource label value
- Metric
Label - METRIC_LABELFilter on a metrics label value
- User
Metadata Label - USER_METADATA_LABELFilter on a user metadata label value
- System
Metadata Label - SYSTEM_METADATA_LABELFilter on a system metadata label value
- Group
- GROUPFilter on a group id
- Dashboard
Filter Filter Type Filter Type Unspecified - FILTER_TYPE_UNSPECIFIEDFilter type is unspecified. This is not valid in a well-formed request.
- Dashboard
Filter Filter Type Resource Label - RESOURCE_LABELFilter on a resource label value
- Dashboard
Filter Filter Type Metric Label - METRIC_LABELFilter on a metrics label value
- Dashboard
Filter Filter Type User Metadata Label - USER_METADATA_LABELFilter on a user metadata label value
- Dashboard
Filter Filter Type System Metadata Label - SYSTEM_METADATA_LABELFilter on a system metadata label value
- Dashboard
Filter Filter Type Group - GROUPFilter on a group id
- Filter
Type Unspecified - FILTER_TYPE_UNSPECIFIEDFilter type is unspecified. This is not valid in a well-formed request.
- Resource
Label - RESOURCE_LABELFilter on a resource label value
- Metric
Label - METRIC_LABELFilter on a metrics label value
- User
Metadata Label - USER_METADATA_LABELFilter on a user metadata label value
- System
Metadata Label - SYSTEM_METADATA_LABELFilter on a system metadata label value
- Group
- GROUPFilter on a group id
- Filter
Type Unspecified - FILTER_TYPE_UNSPECIFIEDFilter type is unspecified. This is not valid in a well-formed request.
- Resource
Label - RESOURCE_LABELFilter on a resource label value
- Metric
Label - METRIC_LABELFilter on a metrics label value
- User
Metadata Label - USER_METADATA_LABELFilter on a user metadata label value
- System
Metadata Label - SYSTEM_METADATA_LABELFilter on a system metadata label value
- Group
- GROUPFilter on a group id
- FILTER_TYPE_UNSPECIFIED
- FILTER_TYPE_UNSPECIFIEDFilter type is unspecified. This is not valid in a well-formed request.
- RESOURCE_LABEL
- RESOURCE_LABELFilter on a resource label value
- METRIC_LABEL
- METRIC_LABELFilter on a metrics label value
- USER_METADATA_LABEL
- USER_METADATA_LABELFilter on a user metadata label value
- SYSTEM_METADATA_LABEL
- SYSTEM_METADATA_LABELFilter on a system metadata label value
- GROUP
- GROUPFilter on a group id
- "FILTER_TYPE_UNSPECIFIED"
- FILTER_TYPE_UNSPECIFIEDFilter type is unspecified. This is not valid in a well-formed request.
- "RESOURCE_LABEL"
- RESOURCE_LABELFilter on a resource label value
- "METRIC_LABEL"
- METRIC_LABELFilter on a metrics label value
- "USER_METADATA_LABEL"
- USER_METADATA_LABELFilter on a user metadata label value
- "SYSTEM_METADATA_LABEL"
- SYSTEM_METADATA_LABELFilter on a system metadata label value
- "GROUP"
- GROUPFilter on a group id
DashboardFilterResponse, DashboardFilterResponseArgs
- Filter
Type string - The specified filter type
- Label
Key string - The key for the label
- String
Value string - A variable-length string value.
- Template
Variable 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 string - The specified filter type
- Label
Key string - The key for the label
- String
Value string - A variable-length string value.
- Template
Variable 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 String - The specified filter type
- label
Key String - The key for the label
- string
Value String - A variable-length string value.
- template
Variable 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 string - The specified filter type
- label
Key string - The key for the label
- string
Value string - A variable-length string value.
- template
Variable 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.
- filter
Type String - The specified filter type
- label
Key String - The key for the label
- string
Value String - A variable-length string value.
- template
Variable 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, DataSetArgs
- Time
Series Pulumi.Query Google Native. Monitoring. V1. Inputs. Time Series Query - Fields for querying time series data from the Stackdriver metrics API.
- Breakdowns
List<Pulumi.
Google Native. Monitoring. V1. Inputs. Breakdown> - Optional. The collection of breakdowns to be applied to the dataset.
- Dimensions
List<Pulumi.
Google Native. Monitoring. V1. Inputs. Dimension> - Optional. A collection of dimension columns.
- Legend
Template 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.
- Measures
List<Pulumi.
Google Native. Monitoring. V1. Inputs. Measure> - Optional. A collection of measures.
- Min
Alignment stringPeriod - 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 Pulumi.Google Native. Monitoring. V1. Data Set Plot Type - How this data should be plotted on the chart.
- Target
Axis Pulumi.Google Native. Monitoring. V1. Data Set Target Axis - Optional. The target axis to use for plotting the metric.
- Time
Series TimeQuery Series Query - Fields for querying time series data from the Stackdriver metrics API.
- Breakdowns []Breakdown
- Optional. The collection of breakdowns to be applied to the dataset.
- Dimensions []Dimension
- Optional. A collection of dimension columns.
- Legend
Template 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.
- Measures []Measure
- Optional. A collection of measures.
- Min
Alignment stringPeriod - 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 DataSet Plot Type - How this data should be plotted on the chart.
- Target
Axis DataSet Target Axis - Optional. The target axis to use for plotting the metric.
- time
Series TimeQuery Series Query - Fields for querying time series data from the Stackdriver metrics API.
- breakdowns List<Breakdown>
- Optional. The collection of breakdowns to be applied to the dataset.
- dimensions List<Dimension>
- Optional. A collection of dimension columns.
- legend
Template 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.
- measures List<Measure>
- Optional. A collection of measures.
- min
Alignment StringPeriod - 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 DataSet Plot Type - How this data should be plotted on the chart.
- target
Axis DataSet Target Axis - Optional. The target axis to use for plotting the metric.
- time
Series TimeQuery Series Query - Fields for querying time series data from the Stackdriver metrics API.
- breakdowns Breakdown[]
- Optional. The collection of breakdowns to be applied to the dataset.
- dimensions Dimension[]
- Optional. A collection of dimension columns.
- legend
Template 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.
- measures Measure[]
- Optional. A collection of measures.
- min
Alignment stringPeriod - 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 DataSet Plot Type - How this data should be plotted on the chart.
- target
Axis DataSet Target Axis - Optional. The target axis to use for plotting the metric.
- time_
series_ Timequery Series Query - Fields for querying time series data from the Stackdriver metrics API.
- breakdowns Sequence[Breakdown]
- Optional. The collection of breakdowns to be applied to the dataset.
- dimensions Sequence[Dimension]
- Optional. A collection of dimension columns.
- 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.
- measures Sequence[Measure]
- Optional. A collection of measures.
- min_
alignment_ strperiod - 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 DataSet Plot Type - How this data should be plotted on the chart.
- target_
axis DataSet Target Axis - Optional. The target axis to use for plotting the metric.
- time
Series Property MapQuery - Fields for querying time series data from the Stackdriver metrics API.
- breakdowns List<Property Map>
- Optional. The collection of breakdowns to be applied to the dataset.
- dimensions List<Property Map>
- Optional. A collection of dimension columns.
- legend
Template 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.
- measures List<Property Map>
- Optional. A collection of measures.
- min
Alignment StringPeriod - 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 "PLOT_TYPE_UNSPECIFIED" | "LINE" | "STACKED_AREA" | "STACKED_BAR" | "HEATMAP" - How this data should be plotted on the chart.
- target
Axis "TARGET_AXIS_UNSPECIFIED" | "Y1" | "Y2" - Optional. The target axis to use for plotting the metric.
DataSetPlotType, DataSetPlotTypeArgs
- Plot
Type Unspecified - PLOT_TYPE_UNSPECIFIEDPlot type is unspecified. The view will default to LINE.
- Line
- LINEThe data is plotted as a set of lines (one line per series).
- Stacked
Area - STACKED_AREAThe 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_BARThe 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
- HEATMAPThe 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.
- Data
Set Plot Type Plot Type Unspecified - PLOT_TYPE_UNSPECIFIEDPlot type is unspecified. The view will default to LINE.
- Data
Set Plot Type Line - LINEThe data is plotted as a set of lines (one line per series).
- Data
Set Plot Type Stacked Area - STACKED_AREAThe 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.
- Data
Set Plot Type Stacked Bar - STACKED_BARThe 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.
- Data
Set Plot Type Heatmap - HEATMAPThe 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_UNSPECIFIEDPlot type is unspecified. The view will default to LINE.
- Line
- LINEThe data is plotted as a set of lines (one line per series).
- Stacked
Area - STACKED_AREAThe 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_BARThe 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
- HEATMAPThe 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_UNSPECIFIEDPlot type is unspecified. The view will default to LINE.
- Line
- LINEThe data is plotted as a set of lines (one line per series).
- Stacked
Area - STACKED_AREAThe 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_BARThe 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
- HEATMAPThe 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_UNSPECIFIEDPlot type is unspecified. The view will default to LINE.
- LINE
- LINEThe data is plotted as a set of lines (one line per series).
- STACKED_AREA
- STACKED_AREAThe 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_BARThe 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
- HEATMAPThe 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_UNSPECIFIEDPlot type is unspecified. The view will default to LINE.
- "LINE"
- LINEThe data is plotted as a set of lines (one line per series).
- "STACKED_AREA"
- STACKED_AREAThe 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_BARThe 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"
- HEATMAPThe 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, DataSetResponseArgs
- Breakdowns
List<Pulumi.
Google Native. Monitoring. V1. Inputs. Breakdown Response> - Optional. The collection of breakdowns to be applied to the dataset.
- Dimensions
List<Pulumi.
Google Native. Monitoring. V1. Inputs. Dimension Response> - Optional. A collection of dimension columns.
- Legend
Template 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.
- Measures
List<Pulumi.
Google Native. Monitoring. V1. Inputs. Measure Response> - Optional. A collection of measures.
- Min
Alignment stringPeriod - 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 string - How this data should be plotted on the chart.
- Target
Axis string - Optional. The target axis to use for plotting the metric.
- Time
Series Pulumi.Query Google Native. Monitoring. V1. Inputs. Time Series Query Response - Fields for querying time series data from the Stackdriver metrics API.
- Breakdowns
[]Breakdown
Response - Optional. The collection of breakdowns to be applied to the dataset.
- Dimensions
[]Dimension
Response - Optional. A collection of dimension columns.
- Legend
Template 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.
- Measures
[]Measure
Response - Optional. A collection of measures.
- Min
Alignment stringPeriod - 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 string - How this data should be plotted on the chart.
- Target
Axis string - Optional. The target axis to use for plotting the metric.
- Time
Series TimeQuery Series Query Response - Fields for querying time series data from the Stackdriver metrics API.
- breakdowns
List<Breakdown
Response> - Optional. The collection of breakdowns to be applied to the dataset.
- dimensions
List<Dimension
Response> - Optional. A collection of dimension columns.
- legend
Template 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.
- measures
List<Measure
Response> - Optional. A collection of measures.
- min
Alignment StringPeriod - 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 String - How this data should be plotted on the chart.
- target
Axis String - Optional. The target axis to use for plotting the metric.
- time
Series TimeQuery Series Query Response - Fields for querying time series data from the Stackdriver metrics API.
- breakdowns
Breakdown
Response[] - Optional. The collection of breakdowns to be applied to the dataset.
- dimensions
Dimension
Response[] - Optional. A collection of dimension columns.
- legend
Template 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.
- measures
Measure
Response[] - Optional. A collection of measures.
- min
Alignment stringPeriod - 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 string - How this data should be plotted on the chart.
- target
Axis string - Optional. The target axis to use for plotting the metric.
- time
Series TimeQuery Series Query Response - Fields for querying time series data from the Stackdriver metrics API.
- breakdowns
Sequence[Breakdown
Response] - Optional. The collection of breakdowns to be applied to the dataset.
- dimensions
Sequence[Dimension
Response] - Optional. A collection of dimension columns.
- 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.
- measures
Sequence[Measure
Response] - Optional. A collection of measures.
- min_
alignment_ strperiod - 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_ Timequery Series Query Response - Fields for querying time series data from the Stackdriver metrics API.
- breakdowns List<Property Map>
- Optional. The collection of breakdowns to be applied to the dataset.
- dimensions List<Property Map>
- Optional. A collection of dimension columns.
- legend
Template 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.
- measures List<Property Map>
- Optional. A collection of measures.
- min
Alignment StringPeriod - 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 String - How this data should be plotted on the chart.
- target
Axis String - Optional. The target axis to use for plotting the metric.
- time
Series Property MapQuery - Fields for querying time series data from the Stackdriver metrics API.
DataSetTargetAxis, DataSetTargetAxisArgs
- Target
Axis Unspecified - TARGET_AXIS_UNSPECIFIEDThe target axis was not specified. Defaults to Y1.
- Y1
- Y1The y_axis (the right axis of chart).
- Y2
- Y2The y2_axis (the left axis of chart).
- Data
Set Target Axis Target Axis Unspecified - TARGET_AXIS_UNSPECIFIEDThe target axis was not specified. Defaults to Y1.
- Data
Set Target Axis Y1 - Y1The y_axis (the right axis of chart).
- Data
Set Target Axis Y2 - Y2The y2_axis (the left axis of chart).
- Target
Axis Unspecified - TARGET_AXIS_UNSPECIFIEDThe target axis was not specified. Defaults to Y1.
- Y1
- Y1The y_axis (the right axis of chart).
- Y2
- Y2The y2_axis (the left axis of chart).
- Target
Axis Unspecified - TARGET_AXIS_UNSPECIFIEDThe target axis was not specified. Defaults to Y1.
- Y1
- Y1The y_axis (the right axis of chart).
- Y2
- Y2The y2_axis (the left axis of chart).
- TARGET_AXIS_UNSPECIFIED
- TARGET_AXIS_UNSPECIFIEDThe target axis was not specified. Defaults to Y1.
- Y1
- Y1The y_axis (the right axis of chart).
- Y2
- Y2The y2_axis (the left axis of chart).
- "TARGET_AXIS_UNSPECIFIED"
- TARGET_AXIS_UNSPECIFIEDThe target axis was not specified. Defaults to Y1.
- "Y1"
- Y1The y_axis (the right axis of chart).
- "Y2"
- Y2The y2_axis (the left axis of chart).
Dimension, DimensionArgs
- Column string
- The name of the column in the source SQL query that is used to chart the dimension.
- Column
Type string - Optional. The type of the dimension column. This is relevant only if one of the bin_size fields is set. If it is empty, the type TIMESTAMP or INT64 will be assumed based on which bin_size field is set. If populated, this should be set to one of the following types: DATE, TIME, DATETIME, TIMESTAMP, BIGNUMERIC, INT64, NUMERIC, FLOAT64.
- Float
Bin doubleSize - Optional. float_bin_size is used when the column type used for a dimension is a floating point numeric column.
- Max
Bin intCount - A limit to the number of bins generated. When 0 is specified, the maximum count is not enforced.
- Numeric
Bin intSize - numeric_bin_size is used when the column type used for a dimension is numeric or string.
- Sort
Column string - The column name to sort on for binning. This column can be the same column as this dimension or any other column used as a measure in the results. If sort_order is set to NONE, then this value is not used.
- Sort
Order Pulumi.Google Native. Monitoring. V1. Dimension Sort Order - The sort order applied to the sort column.
- Time
Bin stringSize - time_bin_size is used when the data type specified by column is a time type and the bin size is determined by a time duration. If column_type is DATE, this must be a whole value multiple of 1 day. If column_type is TIME, this must be less than or equal to 24 hours.
- Column string
- The name of the column in the source SQL query that is used to chart the dimension.
- Column
Type string - Optional. The type of the dimension column. This is relevant only if one of the bin_size fields is set. If it is empty, the type TIMESTAMP or INT64 will be assumed based on which bin_size field is set. If populated, this should be set to one of the following types: DATE, TIME, DATETIME, TIMESTAMP, BIGNUMERIC, INT64, NUMERIC, FLOAT64.
- Float
Bin float64Size - Optional. float_bin_size is used when the column type used for a dimension is a floating point numeric column.
- Max
Bin intCount - A limit to the number of bins generated. When 0 is specified, the maximum count is not enforced.
- Numeric
Bin intSize - numeric_bin_size is used when the column type used for a dimension is numeric or string.
- Sort
Column string - The column name to sort on for binning. This column can be the same column as this dimension or any other column used as a measure in the results. If sort_order is set to NONE, then this value is not used.
- Sort
Order DimensionSort Order - The sort order applied to the sort column.
- Time
Bin stringSize - time_bin_size is used when the data type specified by column is a time type and the bin size is determined by a time duration. If column_type is DATE, this must be a whole value multiple of 1 day. If column_type is TIME, this must be less than or equal to 24 hours.
- column String
- The name of the column in the source SQL query that is used to chart the dimension.
- column
Type String - Optional. The type of the dimension column. This is relevant only if one of the bin_size fields is set. If it is empty, the type TIMESTAMP or INT64 will be assumed based on which bin_size field is set. If populated, this should be set to one of the following types: DATE, TIME, DATETIME, TIMESTAMP, BIGNUMERIC, INT64, NUMERIC, FLOAT64.
- float
Bin DoubleSize - Optional. float_bin_size is used when the column type used for a dimension is a floating point numeric column.
- max
Bin IntegerCount - A limit to the number of bins generated. When 0 is specified, the maximum count is not enforced.
- numeric
Bin IntegerSize - numeric_bin_size is used when the column type used for a dimension is numeric or string.
- sort
Column String - The column name to sort on for binning. This column can be the same column as this dimension or any other column used as a measure in the results. If sort_order is set to NONE, then this value is not used.
- sort
Order DimensionSort Order - The sort order applied to the sort column.
- time
Bin StringSize - time_bin_size is used when the data type specified by column is a time type and the bin size is determined by a time duration. If column_type is DATE, this must be a whole value multiple of 1 day. If column_type is TIME, this must be less than or equal to 24 hours.
- column string
- The name of the column in the source SQL query that is used to chart the dimension.
- column
Type string - Optional. The type of the dimension column. This is relevant only if one of the bin_size fields is set. If it is empty, the type TIMESTAMP or INT64 will be assumed based on which bin_size field is set. If populated, this should be set to one of the following types: DATE, TIME, DATETIME, TIMESTAMP, BIGNUMERIC, INT64, NUMERIC, FLOAT64.
- float
Bin numberSize - Optional. float_bin_size is used when the column type used for a dimension is a floating point numeric column.
- max
Bin numberCount - A limit to the number of bins generated. When 0 is specified, the maximum count is not enforced.
- numeric
Bin numberSize - numeric_bin_size is used when the column type used for a dimension is numeric or string.
- sort
Column string - The column name to sort on for binning. This column can be the same column as this dimension or any other column used as a measure in the results. If sort_order is set to NONE, then this value is not used.
- sort
Order DimensionSort Order - The sort order applied to the sort column.
- time
Bin stringSize - time_bin_size is used when the data type specified by column is a time type and the bin size is determined by a time duration. If column_type is DATE, this must be a whole value multiple of 1 day. If column_type is TIME, this must be less than or equal to 24 hours.
- column str
- The name of the column in the source SQL query that is used to chart the dimension.
- column_
type str - Optional. The type of the dimension column. This is relevant only if one of the bin_size fields is set. If it is empty, the type TIMESTAMP or INT64 will be assumed based on which bin_size field is set. If populated, this should be set to one of the following types: DATE, TIME, DATETIME, TIMESTAMP, BIGNUMERIC, INT64, NUMERIC, FLOAT64.
- float_
bin_ floatsize - Optional. float_bin_size is used when the column type used for a dimension is a floating point numeric column.
- max_
bin_ intcount - A limit to the number of bins generated. When 0 is specified, the maximum count is not enforced.
- numeric_
bin_ intsize - numeric_bin_size is used when the column type used for a dimension is numeric or string.
- sort_
column str - The column name to sort on for binning. This column can be the same column as this dimension or any other column used as a measure in the results. If sort_order is set to NONE, then this value is not used.
- sort_
order DimensionSort Order - The sort order applied to the sort column.
- time_
bin_ strsize - time_bin_size is used when the data type specified by column is a time type and the bin size is determined by a time duration. If column_type is DATE, this must be a whole value multiple of 1 day. If column_type is TIME, this must be less than or equal to 24 hours.
- column String
- The name of the column in the source SQL query that is used to chart the dimension.
- column
Type String - Optional. The type of the dimension column. This is relevant only if one of the bin_size fields is set. If it is empty, the type TIMESTAMP or INT64 will be assumed based on which bin_size field is set. If populated, this should be set to one of the following types: DATE, TIME, DATETIME, TIMESTAMP, BIGNUMERIC, INT64, NUMERIC, FLOAT64.
- float
Bin NumberSize - Optional. float_bin_size is used when the column type used for a dimension is a floating point numeric column.
- max
Bin NumberCount - A limit to the number of bins generated. When 0 is specified, the maximum count is not enforced.
- numeric
Bin NumberSize - numeric_bin_size is used when the column type used for a dimension is numeric or string.
- sort
Column String - The column name to sort on for binning. This column can be the same column as this dimension or any other column used as a measure in the results. If sort_order is set to NONE, then this value is not used.
- sort
Order "SORT_ORDER_UNSPECIFIED" | "SORT_ORDER_NONE" | "SORT_ORDER_ASCENDING" | "SORT_ORDER_DESCENDING" - The sort order applied to the sort column.
- time
Bin StringSize - time_bin_size is used when the data type specified by column is a time type and the bin size is determined by a time duration. If column_type is DATE, this must be a whole value multiple of 1 day. If column_type is TIME, this must be less than or equal to 24 hours.
DimensionResponse, DimensionResponseArgs
- Column string
- The name of the column in the source SQL query that is used to chart the dimension.
- Column
Type string - Optional. The type of the dimension column. This is relevant only if one of the bin_size fields is set. If it is empty, the type TIMESTAMP or INT64 will be assumed based on which bin_size field is set. If populated, this should be set to one of the following types: DATE, TIME, DATETIME, TIMESTAMP, BIGNUMERIC, INT64, NUMERIC, FLOAT64.
- Float
Bin doubleSize - Optional. float_bin_size is used when the column type used for a dimension is a floating point numeric column.
- Max
Bin intCount - A limit to the number of bins generated. When 0 is specified, the maximum count is not enforced.
- Numeric
Bin intSize - numeric_bin_size is used when the column type used for a dimension is numeric or string.
- Sort
Column string - The column name to sort on for binning. This column can be the same column as this dimension or any other column used as a measure in the results. If sort_order is set to NONE, then this value is not used.
- Sort
Order string - The sort order applied to the sort column.
- Time
Bin stringSize - time_bin_size is used when the data type specified by column is a time type and the bin size is determined by a time duration. If column_type is DATE, this must be a whole value multiple of 1 day. If column_type is TIME, this must be less than or equal to 24 hours.
- Column string
- The name of the column in the source SQL query that is used to chart the dimension.
- Column
Type string - Optional. The type of the dimension column. This is relevant only if one of the bin_size fields is set. If it is empty, the type TIMESTAMP or INT64 will be assumed based on which bin_size field is set. If populated, this should be set to one of the following types: DATE, TIME, DATETIME, TIMESTAMP, BIGNUMERIC, INT64, NUMERIC, FLOAT64.
- Float
Bin float64Size - Optional. float_bin_size is used when the column type used for a dimension is a floating point numeric column.
- Max
Bin intCount - A limit to the number of bins generated. When 0 is specified, the maximum count is not enforced.
- Numeric
Bin intSize - numeric_bin_size is used when the column type used for a dimension is numeric or string.
- Sort
Column string - The column name to sort on for binning. This column can be the same column as this dimension or any other column used as a measure in the results. If sort_order is set to NONE, then this value is not used.
- Sort
Order string - The sort order applied to the sort column.
- Time
Bin stringSize - time_bin_size is used when the data type specified by column is a time type and the bin size is determined by a time duration. If column_type is DATE, this must be a whole value multiple of 1 day. If column_type is TIME, this must be less than or equal to 24 hours.
- column String
- The name of the column in the source SQL query that is used to chart the dimension.
- column
Type String - Optional. The type of the dimension column. This is relevant only if one of the bin_size fields is set. If it is empty, the type TIMESTAMP or INT64 will be assumed based on which bin_size field is set. If populated, this should be set to one of the following types: DATE, TIME, DATETIME, TIMESTAMP, BIGNUMERIC, INT64, NUMERIC, FLOAT64.
- float
Bin DoubleSize - Optional. float_bin_size is used when the column type used for a dimension is a floating point numeric column.
- max
Bin IntegerCount - A limit to the number of bins generated. When 0 is specified, the maximum count is not enforced.
- numeric
Bin IntegerSize - numeric_bin_size is used when the column type used for a dimension is numeric or string.
- sort
Column String - The column name to sort on for binning. This column can be the same column as this dimension or any other column used as a measure in the results. If sort_order is set to NONE, then this value is not used.
- sort
Order String - The sort order applied to the sort column.
- time
Bin StringSize - time_bin_size is used when the data type specified by column is a time type and the bin size is determined by a time duration. If column_type is DATE, this must be a whole value multiple of 1 day. If column_type is TIME, this must be less than or equal to 24 hours.
- column string
- The name of the column in the source SQL query that is used to chart the dimension.
- column
Type string - Optional. The type of the dimension column. This is relevant only if one of the bin_size fields is set. If it is empty, the type TIMESTAMP or INT64 will be assumed based on which bin_size field is set. If populated, this should be set to one of the following types: DATE, TIME, DATETIME, TIMESTAMP, BIGNUMERIC, INT64, NUMERIC, FLOAT64.
- float
Bin numberSize - Optional. float_bin_size is used when the column type used for a dimension is a floating point numeric column.
- max
Bin numberCount - A limit to the number of bins generated. When 0 is specified, the maximum count is not enforced.
- numeric
Bin numberSize - numeric_bin_size is used when the column type used for a dimension is numeric or string.
- sort
Column string - The column name to sort on for binning. This column can be the same column as this dimension or any other column used as a measure in the results. If sort_order is set to NONE, then this value is not used.
- sort
Order string - The sort order applied to the sort column.
- time
Bin stringSize - time_bin_size is used when the data type specified by column is a time type and the bin size is determined by a time duration. If column_type is DATE, this must be a whole value multiple of 1 day. If column_type is TIME, this must be less than or equal to 24 hours.
- column str
- The name of the column in the source SQL query that is used to chart the dimension.
- column_
type str - Optional. The type of the dimension column. This is relevant only if one of the bin_size fields is set. If it is empty, the type TIMESTAMP or INT64 will be assumed based on which bin_size field is set. If populated, this should be set to one of the following types: DATE, TIME, DATETIME, TIMESTAMP, BIGNUMERIC, INT64, NUMERIC, FLOAT64.
- float_
bin_ floatsize - Optional. float_bin_size is used when the column type used for a dimension is a floating point numeric column.
- max_
bin_ intcount - A limit to the number of bins generated. When 0 is specified, the maximum count is not enforced.
- numeric_
bin_ intsize - numeric_bin_size is used when the column type used for a dimension is numeric or string.
- sort_
column str - The column name to sort on for binning. This column can be the same column as this dimension or any other column used as a measure in the results. If sort_order is set to NONE, then this value is not used.
- sort_
order str - The sort order applied to the sort column.
- time_
bin_ strsize - time_bin_size is used when the data type specified by column is a time type and the bin size is determined by a time duration. If column_type is DATE, this must be a whole value multiple of 1 day. If column_type is TIME, this must be less than or equal to 24 hours.
- column String
- The name of the column in the source SQL query that is used to chart the dimension.
- column
Type String - Optional. The type of the dimension column. This is relevant only if one of the bin_size fields is set. If it is empty, the type TIMESTAMP or INT64 will be assumed based on which bin_size field is set. If populated, this should be set to one of the following types: DATE, TIME, DATETIME, TIMESTAMP, BIGNUMERIC, INT64, NUMERIC, FLOAT64.
- float
Bin NumberSize - Optional. float_bin_size is used when the column type used for a dimension is a floating point numeric column.
- max
Bin NumberCount - A limit to the number of bins generated. When 0 is specified, the maximum count is not enforced.
- numeric
Bin NumberSize - numeric_bin_size is used when the column type used for a dimension is numeric or string.
- sort
Column String - The column name to sort on for binning. This column can be the same column as this dimension or any other column used as a measure in the results. If sort_order is set to NONE, then this value is not used.
- sort
Order String - The sort order applied to the sort column.
- time
Bin StringSize - time_bin_size is used when the data type specified by column is a time type and the bin size is determined by a time duration. If column_type is DATE, this must be a whole value multiple of 1 day. If column_type is TIME, this must be less than or equal to 24 hours.
DimensionSortOrder, DimensionSortOrderArgs
- Sort
Order Unspecified - SORT_ORDER_UNSPECIFIEDAn unspecified sort order. This option is invalid when sorting is required.
- Sort
Order None - SORT_ORDER_NONENo sorting is applied.
- Sort
Order Ascending - SORT_ORDER_ASCENDINGThe lowest-valued entries are selected first.
- Sort
Order Descending - SORT_ORDER_DESCENDINGThe highest-valued entries are selected first.
- Dimension
Sort Order Sort Order Unspecified - SORT_ORDER_UNSPECIFIEDAn unspecified sort order. This option is invalid when sorting is required.
- Dimension
Sort Order Sort Order None - SORT_ORDER_NONENo sorting is applied.
- Dimension
Sort Order Sort Order Ascending - SORT_ORDER_ASCENDINGThe lowest-valued entries are selected first.
- Dimension
Sort Order Sort Order Descending - SORT_ORDER_DESCENDINGThe highest-valued entries are selected first.
- Sort
Order Unspecified - SORT_ORDER_UNSPECIFIEDAn unspecified sort order. This option is invalid when sorting is required.
- Sort
Order None - SORT_ORDER_NONENo sorting is applied.
- Sort
Order Ascending - SORT_ORDER_ASCENDINGThe lowest-valued entries are selected first.
- Sort
Order Descending - SORT_ORDER_DESCENDINGThe highest-valued entries are selected first.
- Sort
Order Unspecified - SORT_ORDER_UNSPECIFIEDAn unspecified sort order. This option is invalid when sorting is required.
- Sort
Order None - SORT_ORDER_NONENo sorting is applied.
- Sort
Order Ascending - SORT_ORDER_ASCENDINGThe lowest-valued entries are selected first.
- Sort
Order Descending - SORT_ORDER_DESCENDINGThe highest-valued entries are selected first.
- SORT_ORDER_UNSPECIFIED
- SORT_ORDER_UNSPECIFIEDAn unspecified sort order. This option is invalid when sorting is required.
- SORT_ORDER_NONE
- SORT_ORDER_NONENo sorting is applied.
- SORT_ORDER_ASCENDING
- SORT_ORDER_ASCENDINGThe lowest-valued entries are selected first.
- SORT_ORDER_DESCENDING
- SORT_ORDER_DESCENDINGThe highest-valued entries are selected first.
- "SORT_ORDER_UNSPECIFIED"
- SORT_ORDER_UNSPECIFIEDAn unspecified sort order. This option is invalid when sorting is required.
- "SORT_ORDER_NONE"
- SORT_ORDER_NONENo sorting is applied.
- "SORT_ORDER_ASCENDING"
- SORT_ORDER_ASCENDINGThe lowest-valued entries are selected first.
- "SORT_ORDER_DESCENDING"
- SORT_ORDER_DESCENDINGThe highest-valued entries are selected first.
ErrorReportingPanel, ErrorReportingPanelArgs
- Project
Names List<string> - The resource name of the Google Cloud Platform project. Written as projects/{projectID} or projects/{projectNumber}, where {projectID} and {projectNumber} can be found in the Google Cloud console (https://support.google.com/cloud/answer/6158840).Examples: projects/my-project-123, projects/5551234.
- Services List<string>
- An identifier of the service, such as the name of the executable, job, or Google App Engine service name. This field is expected to have a low number of values that are relatively stable over time, as opposed to version, which can be changed whenever new code is deployed.Contains the service name for error reports extracted from Google App Engine logs or default if the App Engine default service is used.
- Versions List<string>
- Represents the source code version that the developer provided, which could represent a version label or a Git SHA-1 hash, for example. For App Engine standard environment, the version is set to the version of the app.
- Project
Names []string - The resource name of the Google Cloud Platform project. Written as projects/{projectID} or projects/{projectNumber}, where {projectID} and {projectNumber} can be found in the Google Cloud console (https://support.google.com/cloud/answer/6158840).Examples: projects/my-project-123, projects/5551234.
- Services []string
- An identifier of the service, such as the name of the executable, job, or Google App Engine service name. This field is expected to have a low number of values that are relatively stable over time, as opposed to version, which can be changed whenever new code is deployed.Contains the service name for error reports extracted from Google App Engine logs or default if the App Engine default service is used.
- Versions []string
- Represents the source code version that the developer provided, which could represent a version label or a Git SHA-1 hash, for example. For App Engine standard environment, the version is set to the version of the app.
- project
Names List<String> - The resource name of the Google Cloud Platform project. Written as projects/{projectID} or projects/{projectNumber}, where {projectID} and {projectNumber} can be found in the Google Cloud console (https://support.google.com/cloud/answer/6158840).Examples: projects/my-project-123, projects/5551234.
- services List<String>
- An identifier of the service, such as the name of the executable, job, or Google App Engine service name. This field is expected to have a low number of values that are relatively stable over time, as opposed to version, which can be changed whenever new code is deployed.Contains the service name for error reports extracted from Google App Engine logs or default if the App Engine default service is used.
- versions List<String>
- Represents the source code version that the developer provided, which could represent a version label or a Git SHA-1 hash, for example. For App Engine standard environment, the version is set to the version of the app.
- project
Names string[] - The resource name of the Google Cloud Platform project. Written as projects/{projectID} or projects/{projectNumber}, where {projectID} and {projectNumber} can be found in the Google Cloud console (https://support.google.com/cloud/answer/6158840).Examples: projects/my-project-123, projects/5551234.
- services string[]
- An identifier of the service, such as the name of the executable, job, or Google App Engine service name. This field is expected to have a low number of values that are relatively stable over time, as opposed to version, which can be changed whenever new code is deployed.Contains the service name for error reports extracted from Google App Engine logs or default if the App Engine default service is used.
- versions string[]
- Represents the source code version that the developer provided, which could represent a version label or a Git SHA-1 hash, for example. For App Engine standard environment, the version is set to the version of the app.
- project_
names Sequence[str] - The resource name of the Google Cloud Platform project. Written as projects/{projectID} or projects/{projectNumber}, where {projectID} and {projectNumber} can be found in the Google Cloud console (https://support.google.com/cloud/answer/6158840).Examples: projects/my-project-123, projects/5551234.
- services Sequence[str]
- An identifier of the service, such as the name of the executable, job, or Google App Engine service name. This field is expected to have a low number of values that are relatively stable over time, as opposed to version, which can be changed whenever new code is deployed.Contains the service name for error reports extracted from Google App Engine logs or default if the App Engine default service is used.
- versions Sequence[str]
- Represents the source code version that the developer provided, which could represent a version label or a Git SHA-1 hash, for example. For App Engine standard environment, the version is set to the version of the app.
- project
Names List<String> - The resource name of the Google Cloud Platform project. Written as projects/{projectID} or projects/{projectNumber}, where {projectID} and {projectNumber} can be found in the Google Cloud console (https://support.google.com/cloud/answer/6158840).Examples: projects/my-project-123, projects/5551234.
- services List<String>
- An identifier of the service, such as the name of the executable, job, or Google App Engine service name. This field is expected to have a low number of values that are relatively stable over time, as opposed to version, which can be changed whenever new code is deployed.Contains the service name for error reports extracted from Google App Engine logs or default if the App Engine default service is used.
- versions List<String>
- Represents the source code version that the developer provided, which could represent a version label or a Git SHA-1 hash, for example. For App Engine standard environment, the version is set to the version of the app.
ErrorReportingPanelResponse, ErrorReportingPanelResponseArgs
- Project
Names List<string> - The resource name of the Google Cloud Platform project. Written as projects/{projectID} or projects/{projectNumber}, where {projectID} and {projectNumber} can be found in the Google Cloud console (https://support.google.com/cloud/answer/6158840).Examples: projects/my-project-123, projects/5551234.
- Services List<string>
- An identifier of the service, such as the name of the executable, job, or Google App Engine service name. This field is expected to have a low number of values that are relatively stable over time, as opposed to version, which can be changed whenever new code is deployed.Contains the service name for error reports extracted from Google App Engine logs or default if the App Engine default service is used.
- Versions List<string>
- Represents the source code version that the developer provided, which could represent a version label or a Git SHA-1 hash, for example. For App Engine standard environment, the version is set to the version of the app.
- Project
Names []string - The resource name of the Google Cloud Platform project. Written as projects/{projectID} or projects/{projectNumber}, where {projectID} and {projectNumber} can be found in the Google Cloud console (https://support.google.com/cloud/answer/6158840).Examples: projects/my-project-123, projects/5551234.
- Services []string
- An identifier of the service, such as the name of the executable, job, or Google App Engine service name. This field is expected to have a low number of values that are relatively stable over time, as opposed to version, which can be changed whenever new code is deployed.Contains the service name for error reports extracted from Google App Engine logs or default if the App Engine default service is used.
- Versions []string
- Represents the source code version that the developer provided, which could represent a version label or a Git SHA-1 hash, for example. For App Engine standard environment, the version is set to the version of the app.
- project
Names List<String> - The resource name of the Google Cloud Platform project. Written as projects/{projectID} or projects/{projectNumber}, where {projectID} and {projectNumber} can be found in the Google Cloud console (https://support.google.com/cloud/answer/6158840).Examples: projects/my-project-123, projects/5551234.
- services List<String>
- An identifier of the service, such as the name of the executable, job, or Google App Engine service name. This field is expected to have a low number of values that are relatively stable over time, as opposed to version, which can be changed whenever new code is deployed.Contains the service name for error reports extracted from Google App Engine logs or default if the App Engine default service is used.
- versions List<String>
- Represents the source code version that the developer provided, which could represent a version label or a Git SHA-1 hash, for example. For App Engine standard environment, the version is set to the version of the app.
- project
Names string[] - The resource name of the Google Cloud Platform project. Written as projects/{projectID} or projects/{projectNumber}, where {projectID} and {projectNumber} can be found in the Google Cloud console (https://support.google.com/cloud/answer/6158840).Examples: projects/my-project-123, projects/5551234.
- services string[]
- An identifier of the service, such as the name of the executable, job, or Google App Engine service name. This field is expected to have a low number of values that are relatively stable over time, as opposed to version, which can be changed whenever new code is deployed.Contains the service name for error reports extracted from Google App Engine logs or default if the App Engine default service is used.
- versions string[]
- Represents the source code version that the developer provided, which could represent a version label or a Git SHA-1 hash, for example. For App Engine standard environment, the version is set to the version of the app.
- project_
names Sequence[str] - The resource name of the Google Cloud Platform project. Written as projects/{projectID} or projects/{projectNumber}, where {projectID} and {projectNumber} can be found in the Google Cloud console (https://support.google.com/cloud/answer/6158840).Examples: projects/my-project-123, projects/5551234.
- services Sequence[str]
- An identifier of the service, such as the name of the executable, job, or Google App Engine service name. This field is expected to have a low number of values that are relatively stable over time, as opposed to version, which can be changed whenever new code is deployed.Contains the service name for error reports extracted from Google App Engine logs or default if the App Engine default service is used.
- versions Sequence[str]
- Represents the source code version that the developer provided, which could represent a version label or a Git SHA-1 hash, for example. For App Engine standard environment, the version is set to the version of the app.
- project
Names List<String> - The resource name of the Google Cloud Platform project. Written as projects/{projectID} or projects/{projectNumber}, where {projectID} and {projectNumber} can be found in the Google Cloud console (https://support.google.com/cloud/answer/6158840).Examples: projects/my-project-123, projects/5551234.
- services List<String>
- An identifier of the service, such as the name of the executable, job, or Google App Engine service name. This field is expected to have a low number of values that are relatively stable over time, as opposed to version, which can be changed whenever new code is deployed.Contains the service name for error reports extracted from Google App Engine logs or default if the App Engine default service is used.
- versions List<String>
- Represents the source code version that the developer provided, which could represent a version label or a Git SHA-1 hash, for example. For App Engine standard environment, the version is set to the version of the app.
GaugeView, GaugeViewArgs
- Lower
Bound double - The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
- Upper
Bound double - The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.
- Lower
Bound float64 - The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
- Upper
Bound float64 - The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.
- lower
Bound Double - The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
- upper
Bound Double - The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.
- lower
Bound number - The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
- upper
Bound 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.
- lower
Bound Number - The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
- upper
Bound Number - The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.
GaugeViewResponse, GaugeViewResponseArgs
- Lower
Bound double - The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
- Upper
Bound double - The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.
- Lower
Bound float64 - The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
- Upper
Bound float64 - The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.
- lower
Bound Double - The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
- upper
Bound Double - The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.
- lower
Bound number - The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
- upper
Bound 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.
- lower
Bound Number - The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.
- upper
Bound Number - The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.
GridLayout, GridLayoutArgs
- 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.
Google Native. 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 List<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, GridLayoutResponseArgs
- 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.
Google Native. Monitoring. V1. Inputs. Widget Response> - 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
Response - 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
Response> - 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
Response[] - 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
Response] - 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.
IncidentList, IncidentListArgs
- Monitored
Resources List<Pulumi.Google Native. Monitoring. V1. Inputs. Monitored Resource> - Optional. The monitored resource for which incidents are listed. The resource doesn't need to be fully specified. That is, you can specify the resource type but not the values of the resource labels. The resource type and labels are used for filtering.
- Policy
Names List<string> - Optional. A list of alert policy names to filter the incident list by. Don't include the project ID prefix in the policy name. For example, use alertPolicies/utilization.
- Monitored
Resources []MonitoredResource - Optional. The monitored resource for which incidents are listed. The resource doesn't need to be fully specified. That is, you can specify the resource type but not the values of the resource labels. The resource type and labels are used for filtering.
- Policy
Names []string - Optional. A list of alert policy names to filter the incident list by. Don't include the project ID prefix in the policy name. For example, use alertPolicies/utilization.
- monitored
Resources List<MonitoredResource> - Optional. The monitored resource for which incidents are listed. The resource doesn't need to be fully specified. That is, you can specify the resource type but not the values of the resource labels. The resource type and labels are used for filtering.
- policy
Names List<String> - Optional. A list of alert policy names to filter the incident list by. Don't include the project ID prefix in the policy name. For example, use alertPolicies/utilization.
- monitored
Resources MonitoredResource[] - Optional. The monitored resource for which incidents are listed. The resource doesn't need to be fully specified. That is, you can specify the resource type but not the values of the resource labels. The resource type and labels are used for filtering.
- policy
Names string[] - Optional. A list of alert policy names to filter the incident list by. Don't include the project ID prefix in the policy name. For example, use alertPolicies/utilization.
- monitored_
resources Sequence[MonitoredResource] - Optional. The monitored resource for which incidents are listed. The resource doesn't need to be fully specified. That is, you can specify the resource type but not the values of the resource labels. The resource type and labels are used for filtering.
- policy_
names Sequence[str] - Optional. A list of alert policy names to filter the incident list by. Don't include the project ID prefix in the policy name. For example, use alertPolicies/utilization.
- monitored
Resources List<Property Map> - Optional. The monitored resource for which incidents are listed. The resource doesn't need to be fully specified. That is, you can specify the resource type but not the values of the resource labels. The resource type and labels are used for filtering.
- policy
Names List<String> - Optional. A list of alert policy names to filter the incident list by. Don't include the project ID prefix in the policy name. For example, use alertPolicies/utilization.
IncidentListResponse, IncidentListResponseArgs
- Monitored
Resources List<Pulumi.Google Native. Monitoring. V1. Inputs. Monitored Resource Response> - Optional. The monitored resource for which incidents are listed. The resource doesn't need to be fully specified. That is, you can specify the resource type but not the values of the resource labels. The resource type and labels are used for filtering.
- Policy
Names List<string> - Optional. A list of alert policy names to filter the incident list by. Don't include the project ID prefix in the policy name. For example, use alertPolicies/utilization.
- Monitored
Resources []MonitoredResource Response - Optional. The monitored resource for which incidents are listed. The resource doesn't need to be fully specified. That is, you can specify the resource type but not the values of the resource labels. The resource type and labels are used for filtering.
- Policy
Names []string - Optional. A list of alert policy names to filter the incident list by. Don't include the project ID prefix in the policy name. For example, use alertPolicies/utilization.
- monitored
Resources List<MonitoredResource Response> - Optional. The monitored resource for which incidents are listed. The resource doesn't need to be fully specified. That is, you can specify the resource type but not the values of the resource labels. The resource type and labels are used for filtering.
- policy
Names List<String> - Optional. A list of alert policy names to filter the incident list by. Don't include the project ID prefix in the policy name. For example, use alertPolicies/utilization.
- monitored
Resources MonitoredResource Response[] - Optional. The monitored resource for which incidents are listed. The resource doesn't need to be fully specified. That is, you can specify the resource type but not the values of the resource labels. The resource type and labels are used for filtering.
- policy
Names string[] - Optional. A list of alert policy names to filter the incident list by. Don't include the project ID prefix in the policy name. For example, use alertPolicies/utilization.
- monitored_
resources Sequence[MonitoredResource Response] - Optional. The monitored resource for which incidents are listed. The resource doesn't need to be fully specified. That is, you can specify the resource type but not the values of the resource labels. The resource type and labels are used for filtering.
- policy_
names Sequence[str] - Optional. A list of alert policy names to filter the incident list by. Don't include the project ID prefix in the policy name. For example, use alertPolicies/utilization.
- monitored
Resources List<Property Map> - Optional. The monitored resource for which incidents are listed. The resource doesn't need to be fully specified. That is, you can specify the resource type but not the values of the resource labels. The resource type and labels are used for filtering.
- policy
Names List<String> - Optional. A list of alert policy names to filter the incident list by. Don't include the project ID prefix in the policy name. For example, use alertPolicies/utilization.
LogsPanel, LogsPanelArgs
- 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.
- Resource
Names 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.
- Resource
Names []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.
- resource
Names 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.
- resource
Names 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.
- resource
Names 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, LogsPanelResponseArgs
- 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.
- Resource
Names 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.
- Resource
Names []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.
- resource
Names 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.
- resource
Names 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.
- resource
Names 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.
Measure, MeasureArgs
- Aggregation
Function Pulumi.Google Native. Monitoring. V1. Inputs. Aggregation Function - The aggregation function applied to the input column. This must not be set to "none" unless binning is disabled on the dimension. The aggregation function is used to group points on the dimension bins.
- Column string
- The column name within in the dataset used for the measure.
- Aggregation
Function AggregationFunction - The aggregation function applied to the input column. This must not be set to "none" unless binning is disabled on the dimension. The aggregation function is used to group points on the dimension bins.
- Column string
- The column name within in the dataset used for the measure.
- aggregation
Function AggregationFunction - The aggregation function applied to the input column. This must not be set to "none" unless binning is disabled on the dimension. The aggregation function is used to group points on the dimension bins.
- column String
- The column name within in the dataset used for the measure.
- aggregation
Function AggregationFunction - The aggregation function applied to the input column. This must not be set to "none" unless binning is disabled on the dimension. The aggregation function is used to group points on the dimension bins.
- column string
- The column name within in the dataset used for the measure.
- aggregation_
function AggregationFunction - The aggregation function applied to the input column. This must not be set to "none" unless binning is disabled on the dimension. The aggregation function is used to group points on the dimension bins.
- column str
- The column name within in the dataset used for the measure.
- aggregation
Function Property Map - The aggregation function applied to the input column. This must not be set to "none" unless binning is disabled on the dimension. The aggregation function is used to group points on the dimension bins.
- column String
- The column name within in the dataset used for the measure.
MeasureResponse, MeasureResponseArgs
- Aggregation
Function Pulumi.Google Native. Monitoring. V1. Inputs. Aggregation Function Response - The aggregation function applied to the input column. This must not be set to "none" unless binning is disabled on the dimension. The aggregation function is used to group points on the dimension bins.
- Column string
- The column name within in the dataset used for the measure.
- Aggregation
Function AggregationFunction Response - The aggregation function applied to the input column. This must not be set to "none" unless binning is disabled on the dimension. The aggregation function is used to group points on the dimension bins.
- Column string
- The column name within in the dataset used for the measure.
- aggregation
Function AggregationFunction Response - The aggregation function applied to the input column. This must not be set to "none" unless binning is disabled on the dimension. The aggregation function is used to group points on the dimension bins.
- column String
- The column name within in the dataset used for the measure.
- aggregation
Function AggregationFunction Response - The aggregation function applied to the input column. This must not be set to "none" unless binning is disabled on the dimension. The aggregation function is used to group points on the dimension bins.
- column string
- The column name within in the dataset used for the measure.
- aggregation_
function AggregationFunction Response - The aggregation function applied to the input column. This must not be set to "none" unless binning is disabled on the dimension. The aggregation function is used to group points on the dimension bins.
- column str
- The column name within in the dataset used for the measure.
- aggregation
Function Property Map - The aggregation function applied to the input column. This must not be set to "none" unless binning is disabled on the dimension. The aggregation function is used to group points on the dimension bins.
- column String
- The column name within in the dataset used for the measure.
MonitoredResource, MonitoredResourceArgs
- Labels Dictionary<string, string>
- Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels "project_id", "instance_id", and "zone".
- Type string
- The monitored resource type. This field must match the type field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is gce_instance. For a list of types, see Monitoring resource types (https://cloud.google.com/monitoring/api/resources) and Logging resource types (https://cloud.google.com/logging/docs/api/v2/resource-list).
- Labels map[string]string
- Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels "project_id", "instance_id", and "zone".
- Type string
- The monitored resource type. This field must match the type field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is gce_instance. For a list of types, see Monitoring resource types (https://cloud.google.com/monitoring/api/resources) and Logging resource types (https://cloud.google.com/logging/docs/api/v2/resource-list).
- labels Map<String,String>
- Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels "project_id", "instance_id", and "zone".
- type String
- The monitored resource type. This field must match the type field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is gce_instance. For a list of types, see Monitoring resource types (https://cloud.google.com/monitoring/api/resources) and Logging resource types (https://cloud.google.com/logging/docs/api/v2/resource-list).
- labels {[key: string]: string}
- Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels "project_id", "instance_id", and "zone".
- type string
- The monitored resource type. This field must match the type field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is gce_instance. For a list of types, see Monitoring resource types (https://cloud.google.com/monitoring/api/resources) and Logging resource types (https://cloud.google.com/logging/docs/api/v2/resource-list).
- labels Mapping[str, str]
- Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels "project_id", "instance_id", and "zone".
- type str
- The monitored resource type. This field must match the type field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is gce_instance. For a list of types, see Monitoring resource types (https://cloud.google.com/monitoring/api/resources) and Logging resource types (https://cloud.google.com/logging/docs/api/v2/resource-list).
- labels Map<String>
- Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels "project_id", "instance_id", and "zone".
- type String
- The monitored resource type. This field must match the type field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is gce_instance. For a list of types, see Monitoring resource types (https://cloud.google.com/monitoring/api/resources) and Logging resource types (https://cloud.google.com/logging/docs/api/v2/resource-list).
MonitoredResourceResponse, MonitoredResourceResponseArgs
- Labels Dictionary<string, string>
- Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels "project_id", "instance_id", and "zone".
- Type string
- The monitored resource type. This field must match the type field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is gce_instance. For a list of types, see Monitoring resource types (https://cloud.google.com/monitoring/api/resources) and Logging resource types (https://cloud.google.com/logging/docs/api/v2/resource-list).
- Labels map[string]string
- Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels "project_id", "instance_id", and "zone".
- Type string
- The monitored resource type. This field must match the type field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is gce_instance. For a list of types, see Monitoring resource types (https://cloud.google.com/monitoring/api/resources) and Logging resource types (https://cloud.google.com/logging/docs/api/v2/resource-list).
- labels Map<String,String>
- Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels "project_id", "instance_id", and "zone".
- type String
- The monitored resource type. This field must match the type field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is gce_instance. For a list of types, see Monitoring resource types (https://cloud.google.com/monitoring/api/resources) and Logging resource types (https://cloud.google.com/logging/docs/api/v2/resource-list).
- labels {[key: string]: string}
- Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels "project_id", "instance_id", and "zone".
- type string
- The monitored resource type. This field must match the type field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is gce_instance. For a list of types, see Monitoring resource types (https://cloud.google.com/monitoring/api/resources) and Logging resource types (https://cloud.google.com/logging/docs/api/v2/resource-list).
- labels Mapping[str, str]
- Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels "project_id", "instance_id", and "zone".
- type str
- The monitored resource type. This field must match the type field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is gce_instance. For a list of types, see Monitoring resource types (https://cloud.google.com/monitoring/api/resources) and Logging resource types (https://cloud.google.com/logging/docs/api/v2/resource-list).
- labels Map<String>
- Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels "project_id", "instance_id", and "zone".
- type String
- The monitored resource type. This field must match the type field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is gce_instance. For a list of types, see Monitoring resource types (https://cloud.google.com/monitoring/api/resources) and Logging resource types (https://cloud.google.com/logging/docs/api/v2/resource-list).
MosaicLayout, MosaicLayoutArgs
- Columns int
- The number of columns in the mosaic grid. The number of columns must be between 1 and 12, inclusive.
- Tiles
List<Pulumi.
Google Native. Monitoring. V1. Inputs. 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 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, MosaicLayoutResponseArgs
- Columns int
- The number of columns in the mosaic grid. The number of columns must be between 1 and 12, inclusive.
- Tiles
List<Pulumi.
Google Native. Monitoring. V1. Inputs. Tile Response> - 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
Response - 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
Response> - 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
Response[] - 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
Response] - 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.
OpsAnalyticsQuery, OpsAnalyticsQueryArgs
- Sql string
- A SQL query to fetch time series, category series, or numeric series data.
- Sql string
- A SQL query to fetch time series, category series, or numeric series data.
- sql String
- A SQL query to fetch time series, category series, or numeric series data.
- sql string
- A SQL query to fetch time series, category series, or numeric series data.
- sql str
- A SQL query to fetch time series, category series, or numeric series data.
- sql String
- A SQL query to fetch time series, category series, or numeric series data.
OpsAnalyticsQueryResponse, OpsAnalyticsQueryResponseArgs
- Sql string
- A SQL query to fetch time series, category series, or numeric series data.
- Sql string
- A SQL query to fetch time series, category series, or numeric series data.
- sql String
- A SQL query to fetch time series, category series, or numeric series data.
- sql string
- A SQL query to fetch time series, category series, or numeric series data.
- sql str
- A SQL query to fetch time series, category series, or numeric series data.
- sql String
- A SQL query to fetch time series, category series, or numeric series data.
Parameter, ParameterArgs
- Double
Value double - A floating-point parameter value.
- Int
Value string - An integer parameter value.
- Double
Value float64 - A floating-point parameter value.
- Int
Value string - An integer parameter value.
- double
Value Double - A floating-point parameter value.
- int
Value String - An integer parameter value.
- double
Value number - A floating-point parameter value.
- int
Value string - An integer parameter value.
- double_
value float - A floating-point parameter value.
- int_
value str - An integer parameter value.
- double
Value Number - A floating-point parameter value.
- int
Value String - An integer parameter value.
ParameterResponse, ParameterResponseArgs
- Double
Value double - A floating-point parameter value.
- Int
Value string - An integer parameter value.
- Double
Value float64 - A floating-point parameter value.
- Int
Value string - An integer parameter value.
- double
Value Double - A floating-point parameter value.
- int
Value String - An integer parameter value.
- double
Value number - A floating-point parameter value.
- int
Value string - An integer parameter value.
- double_
value float - A floating-point parameter value.
- int_
value str - An integer parameter value.
- double
Value Number - A floating-point parameter value.
- int
Value String - An integer parameter value.
PickTimeSeriesFilter, PickTimeSeriesFilterArgs
- Direction
Pulumi.
Google Native. Monitoring. V1. Pick Time Series Filter Direction - How to use the ranking to select time series that pass through the filter.
- Num
Time intSeries - How many time series to allow to pass through the filter.
- Ranking
Method Pulumi.Google Native. Monitoring. V1. Pick Time Series Filter Ranking Method - 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
Pick
Time Series Filter Direction - How to use the ranking to select time series that pass through the filter.
- Num
Time intSeries - How many time series to allow to pass through the filter.
- Ranking
Method PickTime Series Filter Ranking Method - 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
Pick
Time Series Filter Direction - How to use the ranking to select time series that pass through the filter.
- num
Time IntegerSeries - How many time series to allow to pass through the filter.
- ranking
Method PickTime Series Filter Ranking Method - 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
Pick
Time Series Filter Direction - How to use the ranking to select time series that pass through the filter.
- num
Time numberSeries - How many time series to allow to pass through the filter.
- ranking
Method PickTime Series Filter Ranking Method - 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
Pick
Time Series Filter Direction - How to use the ranking to select time series that pass through the filter.
- num_
time_ intseries - How many time series to allow to pass through the filter.
- ranking_
method PickTime Series Filter Ranking Method - 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.
- num
Time NumberSeries - How many time series to allow to pass through the filter.
- ranking
Method "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, PickTimeSeriesFilterDirectionArgs
- Direction
Unspecified - DIRECTION_UNSPECIFIEDNot allowed. You must specify a different Direction if you specify a PickTimeSeriesFilter.
- Top
- TOPPass the highest num_time_series ranking inputs.
- Bottom
- BOTTOMPass the lowest num_time_series ranking inputs.
- Pick
Time Series Filter Direction Direction Unspecified - DIRECTION_UNSPECIFIEDNot allowed. You must specify a different Direction if you specify a PickTimeSeriesFilter.
- Pick
Time Series Filter Direction Top - TOPPass the highest num_time_series ranking inputs.
- Pick
Time Series Filter Direction Bottom - BOTTOMPass the lowest num_time_series ranking inputs.
- Direction
Unspecified - DIRECTION_UNSPECIFIEDNot allowed. You must specify a different Direction if you specify a PickTimeSeriesFilter.
- Top
- TOPPass the highest num_time_series ranking inputs.
- Bottom
- BOTTOMPass the lowest num_time_series ranking inputs.
- Direction
Unspecified - DIRECTION_UNSPECIFIEDNot allowed. You must specify a different Direction if you specify a PickTimeSeriesFilter.
- Top
- TOPPass the highest num_time_series ranking inputs.
- Bottom
- BOTTOMPass the lowest num_time_series ranking inputs.
- DIRECTION_UNSPECIFIED
- DIRECTION_UNSPECIFIEDNot allowed. You must specify a different Direction if you specify a PickTimeSeriesFilter.
- TOP
- TOPPass the highest num_time_series ranking inputs.
- BOTTOM
- BOTTOMPass the lowest num_time_series ranking inputs.
- "DIRECTION_UNSPECIFIED"
- DIRECTION_UNSPECIFIEDNot allowed. You must specify a different Direction if you specify a PickTimeSeriesFilter.
- "TOP"
- TOPPass the highest num_time_series ranking inputs.
- "BOTTOM"
- BOTTOMPass the lowest num_time_series ranking inputs.
PickTimeSeriesFilterRankingMethod, PickTimeSeriesFilterRankingMethodArgs
- Method
Unspecified - METHOD_UNSPECIFIEDNot allowed. You must specify a different Method if you specify a PickTimeSeriesFilter.
- Method
Mean - METHOD_MEANSelect the mean of all values.
- Method
Max - METHOD_MAXSelect the maximum value.
- Method
Min - METHOD_MINSelect the minimum value.
- Method
Sum - METHOD_SUMCompute the sum of all values.
- Method
Latest - METHOD_LATESTSelect the most recent value.
- Pick
Time Series Filter Ranking Method Method Unspecified - METHOD_UNSPECIFIEDNot allowed. You must specify a different Method if you specify a PickTimeSeriesFilter.
- Pick
Time Series Filter Ranking Method Method Mean - METHOD_MEANSelect the mean of all values.
- Pick
Time Series Filter Ranking Method Method Max - METHOD_MAXSelect the maximum value.
- Pick
Time Series Filter Ranking Method Method Min - METHOD_MINSelect the minimum value.
- Pick
Time Series Filter Ranking Method Method Sum - METHOD_SUMCompute the sum of all values.
- Pick
Time Series Filter Ranking Method Method Latest - METHOD_LATESTSelect the most recent value.
- Method
Unspecified - METHOD_UNSPECIFIEDNot allowed. You must specify a different Method if you specify a PickTimeSeriesFilter.
- Method
Mean - METHOD_MEANSelect the mean of all values.
- Method
Max - METHOD_MAXSelect the maximum value.
- Method
Min - METHOD_MINSelect the minimum value.
- Method
Sum - METHOD_SUMCompute the sum of all values.
- Method
Latest - METHOD_LATESTSelect the most recent value.
- Method
Unspecified - METHOD_UNSPECIFIEDNot allowed. You must specify a different Method if you specify a PickTimeSeriesFilter.
- Method
Mean - METHOD_MEANSelect the mean of all values.
- Method
Max - METHOD_MAXSelect the maximum value.
- Method
Min - METHOD_MINSelect the minimum value.
- Method
Sum - METHOD_SUMCompute the sum of all values.
- Method
Latest - METHOD_LATESTSelect the most recent value.
- METHOD_UNSPECIFIED
- METHOD_UNSPECIFIEDNot allowed. You must specify a different Method if you specify a PickTimeSeriesFilter.
- METHOD_MEAN
- METHOD_MEANSelect the mean of all values.
- METHOD_MAX
- METHOD_MAXSelect the maximum value.
- METHOD_MIN
- METHOD_MINSelect the minimum value.
- METHOD_SUM
- METHOD_SUMCompute the sum of all values.
- METHOD_LATEST
- METHOD_LATESTSelect the most recent value.
- "METHOD_UNSPECIFIED"
- METHOD_UNSPECIFIEDNot allowed. You must specify a different Method if you specify a PickTimeSeriesFilter.
- "METHOD_MEAN"
- METHOD_MEANSelect the mean of all values.
- "METHOD_MAX"
- METHOD_MAXSelect the maximum value.
- "METHOD_MIN"
- METHOD_MINSelect the minimum value.
- "METHOD_SUM"
- METHOD_SUMCompute the sum of all values.
- "METHOD_LATEST"
- METHOD_LATESTSelect the most recent value.
PickTimeSeriesFilterResponse, PickTimeSeriesFilterResponseArgs
- Direction string
- How to use the ranking to select time series that pass through the filter.
- Num
Time intSeries - How many time series to allow to pass through the filter.
- Ranking
Method 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.
- Num
Time intSeries - How many time series to allow to pass through the filter.
- Ranking
Method 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.
- num
Time IntegerSeries - How many time series to allow to pass through the filter.
- ranking
Method 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.
- num
Time numberSeries - How many time series to allow to pass through the filter.
- ranking
Method 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_ intseries - 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.
- num
Time NumberSeries - How many time series to allow to pass through the filter.
- ranking
Method 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.
PieChart, PieChartArgs
- Chart
Type Pulumi.Google Native. Monitoring. V1. Pie Chart Chart Type - Indicates the visualization type for the PieChart.
- Data
Sets List<Pulumi.Google Native. Monitoring. V1. Inputs. Pie Chart Data Set> - The queries for the chart's data.
- Show
Labels bool - Optional. Indicates whether or not the pie chart should show slices' labels
- Chart
Type PieChart Chart Type - Indicates the visualization type for the PieChart.
- Data
Sets []PieChart Data Set - The queries for the chart's data.
- Show
Labels bool - Optional. Indicates whether or not the pie chart should show slices' labels
- chart
Type PieChart Chart Type - Indicates the visualization type for the PieChart.
- data
Sets List<PieChart Data Set> - The queries for the chart's data.
- show
Labels Boolean - Optional. Indicates whether or not the pie chart should show slices' labels
- chart
Type PieChart Chart Type - Indicates the visualization type for the PieChart.
- data
Sets PieChart Data Set[] - The queries for the chart's data.
- show
Labels boolean - Optional. Indicates whether or not the pie chart should show slices' labels
- chart_
type PieChart Chart Type - Indicates the visualization type for the PieChart.
- data_
sets Sequence[PieChart Data Set] - The queries for the chart's data.
- show_
labels bool - Optional. Indicates whether or not the pie chart should show slices' labels
- chart
Type "PIE_CHART_TYPE_UNSPECIFIED" | "PIE" | "DONUT" - Indicates the visualization type for the PieChart.
- data
Sets List<Property Map> - The queries for the chart's data.
- show
Labels Boolean - Optional. Indicates whether or not the pie chart should show slices' labels
PieChartChartType, PieChartChartTypeArgs
- Pie
Chart Type Unspecified - PIE_CHART_TYPE_UNSPECIFIEDThe zero value. No type specified. Do not use.
- Pie
- PIEA Pie type PieChart.
- Donut
- DONUTSimilar to PIE, but the DONUT type PieChart has a hole in the middle.
- Pie
Chart Chart Type Pie Chart Type Unspecified - PIE_CHART_TYPE_UNSPECIFIEDThe zero value. No type specified. Do not use.
- Pie
Chart Chart Type Pie - PIEA Pie type PieChart.
- Pie
Chart Chart Type Donut - DONUTSimilar to PIE, but the DONUT type PieChart has a hole in the middle.
- Pie
Chart Type Unspecified - PIE_CHART_TYPE_UNSPECIFIEDThe zero value. No type specified. Do not use.
- Pie
- PIEA Pie type PieChart.
- Donut
- DONUTSimilar to PIE, but the DONUT type PieChart has a hole in the middle.
- Pie
Chart Type Unspecified - PIE_CHART_TYPE_UNSPECIFIEDThe zero value. No type specified. Do not use.
- Pie
- PIEA Pie type PieChart.
- Donut
- DONUTSimilar to PIE, but the DONUT type PieChart has a hole in the middle.
- PIE_CHART_TYPE_UNSPECIFIED
- PIE_CHART_TYPE_UNSPECIFIEDThe zero value. No type specified. Do not use.
- PIE
- PIEA Pie type PieChart.
- DONUT
- DONUTSimilar to PIE, but the DONUT type PieChart has a hole in the middle.
- "PIE_CHART_TYPE_UNSPECIFIED"
- PIE_CHART_TYPE_UNSPECIFIEDThe zero value. No type specified. Do not use.
- "PIE"
- PIEA Pie type PieChart.
- "DONUT"
- DONUTSimilar to PIE, but the DONUT type PieChart has a hole in the middle.
PieChartDataSet, PieChartDataSetArgs
- Time
Series Pulumi.Query Google Native. Monitoring. V1. Inputs. Time Series Query - The query for the PieChart. See, google.monitoring.dashboard.v1.TimeSeriesQuery.
- Min
Alignment stringPeriod - 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.
- Slice
Name stringTemplate - Optional. A template for the name of the slice. This name will be displayed in the legend and the tooltip of the pie chart. It replaces the auto-generated names for the slices. For example, if the template is set to ${resource.labels.zone}, the zone's value will be used for the name instead of the default name.
- Time
Series TimeQuery Series Query - The query for the PieChart. See, google.monitoring.dashboard.v1.TimeSeriesQuery.
- Min
Alignment stringPeriod - 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.
- Slice
Name stringTemplate - Optional. A template for the name of the slice. This name will be displayed in the legend and the tooltip of the pie chart. It replaces the auto-generated names for the slices. For example, if the template is set to ${resource.labels.zone}, the zone's value will be used for the name instead of the default name.
- time
Series TimeQuery Series Query - The query for the PieChart. See, google.monitoring.dashboard.v1.TimeSeriesQuery.
- min
Alignment StringPeriod - 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.
- slice
Name StringTemplate - Optional. A template for the name of the slice. This name will be displayed in the legend and the tooltip of the pie chart. It replaces the auto-generated names for the slices. For example, if the template is set to ${resource.labels.zone}, the zone's value will be used for the name instead of the default name.
- time
Series TimeQuery Series Query - The query for the PieChart. See, google.monitoring.dashboard.v1.TimeSeriesQuery.
- min
Alignment stringPeriod - 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.
- slice
Name stringTemplate - Optional. A template for the name of the slice. This name will be displayed in the legend and the tooltip of the pie chart. It replaces the auto-generated names for the slices. For example, if the template is set to ${resource.labels.zone}, the zone's value will be used for the name instead of the default name.
- time_
series_ Timequery Series Query - The query for the PieChart. See, google.monitoring.dashboard.v1.TimeSeriesQuery.
- min_
alignment_ strperiod - 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.
- slice_
name_ strtemplate - Optional. A template for the name of the slice. This name will be displayed in the legend and the tooltip of the pie chart. It replaces the auto-generated names for the slices. For example, if the template is set to ${resource.labels.zone}, the zone's value will be used for the name instead of the default name.
- time
Series Property MapQuery - The query for the PieChart. See, google.monitoring.dashboard.v1.TimeSeriesQuery.
- min
Alignment StringPeriod - 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.
- slice
Name StringTemplate - Optional. A template for the name of the slice. This name will be displayed in the legend and the tooltip of the pie chart. It replaces the auto-generated names for the slices. For example, if the template is set to ${resource.labels.zone}, the zone's value will be used for the name instead of the default name.
PieChartDataSetResponse, PieChartDataSetResponseArgs
- Min
Alignment stringPeriod - 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.
- Slice
Name stringTemplate - Optional. A template for the name of the slice. This name will be displayed in the legend and the tooltip of the pie chart. It replaces the auto-generated names for the slices. For example, if the template is set to ${resource.labels.zone}, the zone's value will be used for the name instead of the default name.
- Time
Series Pulumi.Query Google Native. Monitoring. V1. Inputs. Time Series Query Response - The query for the PieChart. See, google.monitoring.dashboard.v1.TimeSeriesQuery.
- Min
Alignment stringPeriod - 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.
- Slice
Name stringTemplate - Optional. A template for the name of the slice. This name will be displayed in the legend and the tooltip of the pie chart. It replaces the auto-generated names for the slices. For example, if the template is set to ${resource.labels.zone}, the zone's value will be used for the name instead of the default name.
- Time
Series TimeQuery Series Query Response - The query for the PieChart. See, google.monitoring.dashboard.v1.TimeSeriesQuery.
- min
Alignment StringPeriod - 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.
- slice
Name StringTemplate - Optional. A template for the name of the slice. This name will be displayed in the legend and the tooltip of the pie chart. It replaces the auto-generated names for the slices. For example, if the template is set to ${resource.labels.zone}, the zone's value will be used for the name instead of the default name.
- time
Series TimeQuery Series Query Response - The query for the PieChart. See, google.monitoring.dashboard.v1.TimeSeriesQuery.
- min
Alignment stringPeriod - 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.
- slice
Name stringTemplate - Optional. A template for the name of the slice. This name will be displayed in the legend and the tooltip of the pie chart. It replaces the auto-generated names for the slices. For example, if the template is set to ${resource.labels.zone}, the zone's value will be used for the name instead of the default name.
- time
Series TimeQuery Series Query Response - The query for the PieChart. See, google.monitoring.dashboard.v1.TimeSeriesQuery.
- min_
alignment_ strperiod - 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.
- slice_
name_ strtemplate - Optional. A template for the name of the slice. This name will be displayed in the legend and the tooltip of the pie chart. It replaces the auto-generated names for the slices. For example, if the template is set to ${resource.labels.zone}, the zone's value will be used for the name instead of the default name.
- time_
series_ Timequery Series Query Response - The query for the PieChart. See, google.monitoring.dashboard.v1.TimeSeriesQuery.
- min
Alignment StringPeriod - 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.
- slice
Name StringTemplate - Optional. A template for the name of the slice. This name will be displayed in the legend and the tooltip of the pie chart. It replaces the auto-generated names for the slices. For example, if the template is set to ${resource.labels.zone}, the zone's value will be used for the name instead of the default name.
- time
Series Property MapQuery - The query for the PieChart. See, google.monitoring.dashboard.v1.TimeSeriesQuery.
PieChartResponse, PieChartResponseArgs
- Chart
Type string - Indicates the visualization type for the PieChart.
- Data
Sets List<Pulumi.Google Native. Monitoring. V1. Inputs. Pie Chart Data Set Response> - The queries for the chart's data.
- Show
Labels bool - Optional. Indicates whether or not the pie chart should show slices' labels
- Chart
Type string - Indicates the visualization type for the PieChart.
- Data
Sets []PieChart Data Set Response - The queries for the chart's data.
- Show
Labels bool - Optional. Indicates whether or not the pie chart should show slices' labels
- chart
Type String - Indicates the visualization type for the PieChart.
- data
Sets List<PieChart Data Set Response> - The queries for the chart's data.
- show
Labels Boolean - Optional. Indicates whether or not the pie chart should show slices' labels
- chart
Type string - Indicates the visualization type for the PieChart.
- data
Sets PieChart Data Set Response[] - The queries for the chart's data.
- show
Labels boolean - Optional. Indicates whether or not the pie chart should show slices' labels
- chart_
type str - Indicates the visualization type for the PieChart.
- data_
sets Sequence[PieChart Data Set Response] - The queries for the chart's data.
- show_
labels bool - Optional. Indicates whether or not the pie chart should show slices' labels
- chart
Type String - Indicates the visualization type for the PieChart.
- data
Sets List<Property Map> - The queries for the chart's data.
- show
Labels Boolean - Optional. Indicates whether or not the pie chart should show slices' labels
RatioPart, RatioPartArgs
- 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.
Google Native. 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, RatioPartResponseArgs
- Aggregation
Pulumi.
Google Native. Monitoring. V1. Inputs. Aggregation Response - 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
Response - 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
Response - 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
Response - 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
Response - 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, RowArgs
- 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.
Google Native. 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, RowLayoutArgs
- Rows
List<Pulumi.
Google Native. Monitoring. V1. Inputs. 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, RowLayoutResponseArgs
- Rows
List<Pulumi.
Google Native. Monitoring. V1. Inputs. Row Response> - The rows of content to display.
- Rows
[]Row
Response - The rows of content to display.
- rows
List<Row
Response> - The rows of content to display.
- rows
Row
Response[] - The rows of content to display.
- rows
Sequence[Row
Response] - The rows of content to display.
- rows List<Property Map>
- The rows of content to display.
RowResponse, RowResponseArgs
- 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.
Google Native. Monitoring. V1. Inputs. Widget Response> - 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
Response - 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
Response> - 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
Response[] - 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
Response] - 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, ScorecardArgs
- Time
Series Pulumi.Query Google Native. Monitoring. V1. Inputs. Time Series Query - Fields for querying time series data from the Stackdriver metrics API.
- Blank
View Pulumi.Google Native. Monitoring. V1. Inputs. Empty - Will cause the Scorecard to show only the value, with no indicator to its value relative to its thresholds.
- Gauge
View Pulumi.Google Native. Monitoring. V1. Inputs. Gauge View - Will cause the scorecard to show a gauge chart.
- Spark
Chart Pulumi.View Google Native. Monitoring. V1. Inputs. Spark Chart View - Will cause the scorecard to show a spark chart.
- Thresholds
List<Pulumi.
Google Native. 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.
- Time
Series TimeQuery Series Query - Fields for querying time series data from the Stackdriver metrics API.
- Blank
View Empty - Will cause the Scorecard to show only the value, with no indicator to its value relative to its thresholds.
- Gauge
View GaugeView - Will cause the scorecard to show a gauge chart.
- Spark
Chart SparkView Chart View - 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 TimeQuery Series Query - Fields for querying time series data from the Stackdriver metrics API.
- blank
View Empty - Will cause the Scorecard to show only the value, with no indicator to its value relative to its thresholds.
- gauge
View GaugeView - Will cause the scorecard to show a gauge chart.
- spark
Chart SparkView Chart View - 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.
- time
Series TimeQuery Series Query - Fields for querying time series data from the Stackdriver metrics API.
- blank
View Empty - Will cause the Scorecard to show only the value, with no indicator to its value relative to its thresholds.
- gauge
View GaugeView - Will cause the scorecard to show a gauge chart.
- spark
Chart SparkView Chart View - 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_ Timequery Series Query - Fields for querying time series data from the Stackdriver metrics API.
- blank_
view Empty - Will cause the Scorecard to show only the value, with no indicator to its value relative to its thresholds.
- gauge_
view GaugeView - Will cause the scorecard to show a gauge chart.
- spark_
chart_ Sparkview Chart View - 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.
- time
Series Property MapQuery - Fields for querying time series data from the Stackdriver metrics API.
- blank
View Property Map - Will cause the Scorecard to show only the value, with no indicator to its value relative to its thresholds.
- gauge
View Property Map - Will cause the scorecard to show a gauge chart.
- spark
Chart Property MapView - 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, ScorecardResponseArgs
- Blank
View Pulumi.Google Native. Monitoring. V1. Inputs. Empty Response - Will cause the Scorecard to show only the value, with no indicator to its value relative to its thresholds.
- Gauge
View Pulumi.Google Native. Monitoring. V1. Inputs. Gauge View Response - Will cause the scorecard to show a gauge chart.
- Spark
Chart Pulumi.View Google Native. Monitoring. V1. Inputs. Spark Chart View Response - Will cause the scorecard to show a spark chart.
- Thresholds
List<Pulumi.
Google Native. Monitoring. V1. Inputs. Threshold Response> - 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 Pulumi.Query Google Native. Monitoring. V1. Inputs. Time Series Query Response - Fields for querying time series data from the Stackdriver metrics API.
- Blank
View EmptyResponse - Will cause the Scorecard to show only the value, with no indicator to its value relative to its thresholds.
- Gauge
View GaugeView Response - Will cause the scorecard to show a gauge chart.
- Spark
Chart SparkView Chart View Response - Will cause the scorecard to show a spark chart.
- Thresholds
[]Threshold
Response - 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 TimeQuery Series Query Response - Fields for querying time series data from the Stackdriver metrics API.
- blank
View EmptyResponse - Will cause the Scorecard to show only the value, with no indicator to its value relative to its thresholds.
- gauge
View GaugeView Response - Will cause the scorecard to show a gauge chart.
- spark
Chart SparkView Chart View Response - Will cause the scorecard to show a spark chart.
- thresholds
List<Threshold
Response> - 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 TimeQuery Series Query Response - Fields for querying time series data from the Stackdriver metrics API.
- blank
View EmptyResponse - Will cause the Scorecard to show only the value, with no indicator to its value relative to its thresholds.
- gauge
View GaugeView Response - Will cause the scorecard to show a gauge chart.
- spark
Chart SparkView Chart View Response - Will cause the scorecard to show a spark chart.
- thresholds
Threshold
Response[] - 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 TimeQuery Series Query Response - Fields for querying time series data from the Stackdriver metrics API.
- blank_
view EmptyResponse - Will cause the Scorecard to show only the value, with no indicator to its value relative to its thresholds.
- gauge_
view GaugeView Response - Will cause the scorecard to show a gauge chart.
- spark_
chart_ Sparkview Chart View Response - Will cause the scorecard to show a spark chart.
- thresholds
Sequence[Threshold
Response] - 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_ Timequery Series Query Response - Fields for querying time series data from the Stackdriver metrics API.
- blank
View Property Map - Will cause the Scorecard to show only the value, with no indicator to its value relative to its thresholds.
- gauge
View Property Map - Will cause the scorecard to show a gauge chart.
- spark
Chart Property MapView - 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.
- time
Series Property MapQuery - Fields for querying time series data from the Stackdriver metrics API.
SparkChartView, SparkChartViewArgs
- Spark
Chart Pulumi.Type Google Native. Monitoring. V1. Spark Chart View Spark Chart Type - The type of sparkchart to show in this chartView.
- Min
Alignment stringPeriod - 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 SparkType Chart View Spark Chart Type - The type of sparkchart to show in this chartView.
- Min
Alignment stringPeriod - 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 SparkType Chart View Spark Chart Type - The type of sparkchart to show in this chartView.
- min
Alignment StringPeriod - 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 SparkType Chart View Spark Chart Type - The type of sparkchart to show in this chartView.
- min
Alignment stringPeriod - 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_ Sparktype Chart View Spark Chart Type - The type of sparkchart to show in this chartView.
- min_
alignment_ strperiod - 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 "SPARK_CHART_TYPE_UNSPECIFIED" | "SPARK_LINE" | "SPARK_BAR"Type - The type of sparkchart to show in this chartView.
- min
Alignment StringPeriod - 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, SparkChartViewResponseArgs
- Min
Alignment stringPeriod - 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 stringType - The type of sparkchart to show in this chartView.
- Min
Alignment stringPeriod - 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 stringType - The type of sparkchart to show in this chartView.
- min
Alignment StringPeriod - 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 StringType - The type of sparkchart to show in this chartView.
- min
Alignment stringPeriod - 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 stringType - The type of sparkchart to show in this chartView.
- min_
alignment_ strperiod - 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_ strtype - The type of sparkchart to show in this chartView.
- min
Alignment StringPeriod - 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 StringType - The type of sparkchart to show in this chartView.
SparkChartViewSparkChartType, SparkChartViewSparkChartTypeArgs
- Spark
Chart Type Unspecified - SPARK_CHART_TYPE_UNSPECIFIEDNot allowed in well-formed requests.
- Spark
Line - SPARK_LINEThe sparkline will be rendered as a small line chart.
- Spark
Bar - SPARK_BARThe sparkbar will be rendered as a small bar chart.
- Spark
Chart View Spark Chart Type Spark Chart Type Unspecified - SPARK_CHART_TYPE_UNSPECIFIEDNot allowed in well-formed requests.
- Spark
Chart View Spark Chart Type Spark Line - SPARK_LINEThe sparkline will be rendered as a small line chart.
- Spark
Chart View Spark Chart Type Spark Bar - SPARK_BARThe sparkbar will be rendered as a small bar chart.
- Spark
Chart Type Unspecified - SPARK_CHART_TYPE_UNSPECIFIEDNot allowed in well-formed requests.
- Spark
Line - SPARK_LINEThe sparkline will be rendered as a small line chart.
- Spark
Bar - SPARK_BARThe sparkbar will be rendered as a small bar chart.
- Spark
Chart Type Unspecified - SPARK_CHART_TYPE_UNSPECIFIEDNot allowed in well-formed requests.
- Spark
Line - SPARK_LINEThe sparkline will be rendered as a small line chart.
- Spark
Bar - SPARK_BARThe sparkbar will be rendered as a small bar chart.
- SPARK_CHART_TYPE_UNSPECIFIED
- SPARK_CHART_TYPE_UNSPECIFIEDNot allowed in well-formed requests.
- SPARK_LINE
- SPARK_LINEThe sparkline will be rendered as a small line chart.
- SPARK_BAR
- SPARK_BARThe sparkbar will be rendered as a small bar chart.
- "SPARK_CHART_TYPE_UNSPECIFIED"
- SPARK_CHART_TYPE_UNSPECIFIEDNot allowed in well-formed requests.
- "SPARK_LINE"
- SPARK_LINEThe sparkline will be rendered as a small line chart.
- "SPARK_BAR"
- SPARK_BARThe sparkbar will be rendered as a small bar chart.
StatisticalTimeSeriesFilter, StatisticalTimeSeriesFilterArgs
- Num
Time intSeries - How many time series to output.
- Ranking
Method Pulumi.Google Native. Monitoring. V1. Statistical Time Series Filter Ranking Method - 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 intSeries - How many time series to output.
- Ranking
Method StatisticalTime Series Filter Ranking Method - 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 IntegerSeries - How many time series to output.
- ranking
Method StatisticalTime Series Filter Ranking Method - 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 numberSeries - How many time series to output.
- ranking
Method StatisticalTime Series Filter Ranking Method - 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_ intseries - How many time series to output.
- ranking_
method StatisticalTime Series Filter Ranking Method - 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 NumberSeries - How many time series to output.
- ranking
Method "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, StatisticalTimeSeriesFilterRankingMethodArgs
- Method
Unspecified - METHOD_UNSPECIFIEDNot allowed in well-formed requests.
- Method
Cluster Outlier - METHOD_CLUSTER_OUTLIERCompute the outlier score of each stream.
- Statistical
Time Series Filter Ranking Method Method Unspecified - METHOD_UNSPECIFIEDNot allowed in well-formed requests.
- Statistical
Time Series Filter Ranking Method Method Cluster Outlier - METHOD_CLUSTER_OUTLIERCompute the outlier score of each stream.
- Method
Unspecified - METHOD_UNSPECIFIEDNot allowed in well-formed requests.
- Method
Cluster Outlier - METHOD_CLUSTER_OUTLIERCompute the outlier score of each stream.
- Method
Unspecified - METHOD_UNSPECIFIEDNot allowed in well-formed requests.
- Method
Cluster Outlier - METHOD_CLUSTER_OUTLIERCompute the outlier score of each stream.
- METHOD_UNSPECIFIED
- METHOD_UNSPECIFIEDNot allowed in well-formed requests.
- METHOD_CLUSTER_OUTLIER
- METHOD_CLUSTER_OUTLIERCompute the outlier score of each stream.
- "METHOD_UNSPECIFIED"
- METHOD_UNSPECIFIEDNot allowed in well-formed requests.
- "METHOD_CLUSTER_OUTLIER"
- METHOD_CLUSTER_OUTLIERCompute the outlier score of each stream.
StatisticalTimeSeriesFilterResponse, StatisticalTimeSeriesFilterResponseArgs
- Num
Time intSeries - How many time series to output.
- Ranking
Method 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 intSeries - How many time series to output.
- Ranking
Method 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 IntegerSeries - How many time series to output.
- ranking
Method 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 numberSeries - How many time series to output.
- ranking
Method 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_ intseries - 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.
- num
Time NumberSeries - How many time series to output.
- ranking
Method 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, TableDataSetArgs
- Time
Series Pulumi.Query Google Native. Monitoring. V1. Inputs. Time Series Query - Fields for querying time series data from the Stackdriver metrics API.
- Min
Alignment stringPeriod - 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 Pulumi.Options Google Native. Monitoring. V1. Inputs. Table Display Options - Optional. Table display options for configuring how the table is rendered.
- Table
Template 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 TimeQuery Series Query - Fields for querying time series data from the Stackdriver metrics API.
- Min
Alignment stringPeriod - 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 TableOptions Display Options - Optional. Table display options for configuring how the table is rendered.
- Table
Template 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 TimeQuery Series Query - Fields for querying time series data from the Stackdriver metrics API.
- min
Alignment StringPeriod - 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 TableOptions Display Options - Optional. Table display options for configuring how the table is rendered.
- table
Template 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 TimeQuery Series Query - Fields for querying time series data from the Stackdriver metrics API.
- min
Alignment stringPeriod - 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 TableOptions Display Options - Optional. Table display options for configuring how the table is rendered.
- table
Template 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_ Timequery Series Query - Fields for querying time series data from the Stackdriver metrics API.
- min_
alignment_ strperiod - 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_ Tableoptions Display Options - 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 Property MapQuery - Fields for querying time series data from the Stackdriver metrics API.
- min
Alignment StringPeriod - 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 Property MapOptions - Optional. Table display options for configuring how the table is rendered.
- table
Template 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, TableDataSetResponseArgs
- Min
Alignment stringPeriod - 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 Pulumi.Options Google Native. Monitoring. V1. Inputs. Table Display Options Response - Optional. Table display options for configuring how the table is rendered.
- Table
Template 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 Pulumi.Query Google Native. Monitoring. V1. Inputs. Time Series Query Response - Fields for querying time series data from the Stackdriver metrics API.
- Min
Alignment stringPeriod - 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 TableOptions Display Options Response - Optional. Table display options for configuring how the table is rendered.
- Table
Template 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 TimeQuery Series Query Response - Fields for querying time series data from the Stackdriver metrics API.
- min
Alignment StringPeriod - 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 TableOptions Display Options Response - Optional. Table display options for configuring how the table is rendered.
- table
Template 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 TimeQuery Series Query Response - Fields for querying time series data from the Stackdriver metrics API.
- min
Alignment stringPeriod - 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 TableOptions Display Options Response - Optional. Table display options for configuring how the table is rendered.
- table
Template 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 TimeQuery Series Query Response - Fields for querying time series data from the Stackdriver metrics API.
- min_
alignment_ strperiod - 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_ Tableoptions Display Options Response - 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_ Timequery Series Query Response - Fields for querying time series data from the Stackdriver metrics API.
- min
Alignment StringPeriod - 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 Property MapOptions - Optional. Table display options for configuring how the table is rendered.
- table
Template 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 Property MapQuery - Fields for querying time series data from the Stackdriver metrics API.
TableDisplayOptions, TableDisplayOptionsArgs
- Shown
Columns List<string> - Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings
- Shown
Columns []string - Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings
- shown
Columns List<String> - Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings
- shown
Columns 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
- shown
Columns List<String> - Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings
TableDisplayOptionsResponse, TableDisplayOptionsResponseArgs
- Shown
Columns List<string> - Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings
- Shown
Columns []string - Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings
- shown
Columns List<String> - Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings
- shown
Columns 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
- shown
Columns List<String> - Optional. This field is unused and has been replaced by TimeSeriesTable.column_settings
Text, TextArgs
- Content string
- The text content to be displayed.
- Format
Pulumi.
Google Native. Monitoring. V1. Text Format - How the text content is formatted.
- Style
Pulumi.
Google Native. Monitoring. V1. Inputs. Text Style - How the text is styled
- Content string
- The text content to be displayed.
- Format
Text
Format - How the text content is formatted.
- Style
Text
Style - How the text is styled
- content String
- The text content to be displayed.
- format
Text
Format - How the text content is formatted.
- style
Text
Style - How the text is styled
- content string
- The text content to be displayed.
- format
Text
Format - How the text content is formatted.
- style
Text
Style - How the text is styled
- content str
- The text content to be displayed.
- format
Text
Format - How the text content is formatted.
- style
Text
Style - How the text is styled
- content String
- The text content to be displayed.
- format "FORMAT_UNSPECIFIED" | "MARKDOWN" | "RAW"
- How the text content is formatted.
- style Property Map
- How the text is styled
TextFormat, TextFormatArgs
- Format
Unspecified - FORMAT_UNSPECIFIEDFormat is unspecified. Defaults to MARKDOWN.
- Markdown
- MARKDOWNThe text contains Markdown formatting.
- Raw
- RAWThe text contains no special formatting.
- Text
Format Format Unspecified - FORMAT_UNSPECIFIEDFormat is unspecified. Defaults to MARKDOWN.
- Text
Format Markdown - MARKDOWNThe text contains Markdown formatting.
- Text
Format Raw - RAWThe text contains no special formatting.
- Format
Unspecified - FORMAT_UNSPECIFIEDFormat is unspecified. Defaults to MARKDOWN.
- Markdown
- MARKDOWNThe text contains Markdown formatting.
- Raw
- RAWThe text contains no special formatting.
- Format
Unspecified - FORMAT_UNSPECIFIEDFormat is unspecified. Defaults to MARKDOWN.
- Markdown
- MARKDOWNThe text contains Markdown formatting.
- Raw
- RAWThe text contains no special formatting.
- FORMAT_UNSPECIFIED
- FORMAT_UNSPECIFIEDFormat is unspecified. Defaults to MARKDOWN.
- MARKDOWN
- MARKDOWNThe text contains Markdown formatting.
- RAW
- RAWThe text contains no special formatting.
- "FORMAT_UNSPECIFIED"
- FORMAT_UNSPECIFIEDFormat is unspecified. Defaults to MARKDOWN.
- "MARKDOWN"
- MARKDOWNThe text contains Markdown formatting.
- "RAW"
- RAWThe text contains no special formatting.
TextResponse, TextResponseArgs
- Content string
- The text content to be displayed.
- Format string
- How the text content is formatted.
- Style
Pulumi.
Google Native. Monitoring. V1. Inputs. Text Style Response - How the text is styled
- Content string
- The text content to be displayed.
- Format string
- How the text content is formatted.
- Style
Text
Style Response - How the text is styled
- content String
- The text content to be displayed.
- format String
- How the text content is formatted.
- style
Text
Style Response - How the text is styled
- content string
- The text content to be displayed.
- format string
- How the text content is formatted.
- style
Text
Style Response - How the text is styled
- content str
- The text content to be displayed.
- format str
- How the text content is formatted.
- style
Text
Style Response - How the text is styled
- content String
- The text content to be displayed.
- format String
- How the text content is formatted.
- style Property Map
- How the text is styled
TextStyle, TextStyleArgs
- Background
Color string - The background color as a hex string. "#RRGGBB" or "#RGB"
- Font
Size Pulumi.Google Native. Monitoring. V1. Text Style Font Size - Font sizes for both the title and content. The title will still be larger relative to the content.
- Horizontal
Alignment Pulumi.Google Native. Monitoring. V1. Text Style Horizontal Alignment - The horizontal alignment of both the title and content
- Padding
Pulumi.
Google Native. Monitoring. V1. Text Style Padding - The amount of padding around the widget
- Pointer
Location Pulumi.Google Native. Monitoring. V1. Text Style Pointer Location - The pointer location for this widget (also sometimes called a "tail")
- Text
Color string - The text color as a hex string. "#RRGGBB" or "#RGB"
- Vertical
Alignment Pulumi.Google Native. Monitoring. V1. Text Style Vertical Alignment - The vertical alignment of both the title and content
- Background
Color string - The background color as a hex string. "#RRGGBB" or "#RGB"
- Font
Size TextStyle Font Size - Font sizes for both the title and content. The title will still be larger relative to the content.
- Horizontal
Alignment TextStyle Horizontal Alignment - The horizontal alignment of both the title and content
- Padding
Text
Style Padding - The amount of padding around the widget
- Pointer
Location TextStyle Pointer Location - The pointer location for this widget (also sometimes called a "tail")
- Text
Color string - The text color as a hex string. "#RRGGBB" or "#RGB"
- Vertical
Alignment TextStyle Vertical Alignment - The vertical alignment of both the title and content
- background
Color String - The background color as a hex string. "#RRGGBB" or "#RGB"
- font
Size TextStyle Font Size - Font sizes for both the title and content. The title will still be larger relative to the content.
- horizontal
Alignment TextStyle Horizontal Alignment - The horizontal alignment of both the title and content
- padding
Text
Style Padding - The amount of padding around the widget
- pointer
Location TextStyle Pointer Location - The pointer location for this widget (also sometimes called a "tail")
- text
Color String - The text color as a hex string. "#RRGGBB" or "#RGB"
- vertical
Alignment TextStyle Vertical Alignment - The vertical alignment of both the title and content
- background
Color string - The background color as a hex string. "#RRGGBB" or "#RGB"
- font
Size TextStyle Font Size - Font sizes for both the title and content. The title will still be larger relative to the content.
- horizontal
Alignment TextStyle Horizontal Alignment - The horizontal alignment of both the title and content
- padding
Text
Style Padding - The amount of padding around the widget
- pointer
Location TextStyle Pointer Location - The pointer location for this widget (also sometimes called a "tail")
- text
Color string - The text color as a hex string. "#RRGGBB" or "#RGB"
- vertical
Alignment TextStyle Vertical Alignment - The vertical alignment of both the title and content
- background_
color str - The background color as a hex string. "#RRGGBB" or "#RGB"
- font_
size TextStyle Font Size - Font sizes for both the title and content. The title will still be larger relative to the content.
- horizontal_
alignment TextStyle Horizontal Alignment - The horizontal alignment of both the title and content
- padding
Text
Style Padding - The amount of padding around the widget
- pointer_
location TextStyle Pointer Location - The pointer location for this widget (also sometimes called a "tail")
- text_
color str - The text color as a hex string. "#RRGGBB" or "#RGB"
- vertical_
alignment TextStyle Vertical Alignment - The vertical alignment of both the title and content
- background
Color String - The background color as a hex string. "#RRGGBB" or "#RGB"
- font
Size "FONT_SIZE_UNSPECIFIED" | "FS_EXTRA_SMALL" | "FS_SMALL" | "FS_MEDIUM" | "FS_LARGE" | "FS_EXTRA_LARGE" - Font sizes for both the title and content. The title will still be larger relative to the content.
- horizontal
Alignment "HORIZONTAL_ALIGNMENT_UNSPECIFIED" | "H_LEFT" | "H_CENTER" | "H_RIGHT" - The horizontal alignment of both the title and content
- padding "PADDING_SIZE_UNSPECIFIED" | "P_EXTRA_SMALL" | "P_SMALL" | "P_MEDIUM" | "P_LARGE" | "P_EXTRA_LARGE"
- The amount of padding around the widget
- pointer
Location "POINTER_LOCATION_UNSPECIFIED" | "PL_TOP" | "PL_RIGHT" | "PL_BOTTOM" | "PL_LEFT" | "PL_TOP_LEFT" | "PL_TOP_RIGHT" | "PL_RIGHT_TOP" | "PL_RIGHT_BOTTOM" | "PL_BOTTOM_RIGHT" | "PL_BOTTOM_LEFT" | "PL_LEFT_BOTTOM" | "PL_LEFT_TOP" - The pointer location for this widget (also sometimes called a "tail")
- text
Color String - The text color as a hex string. "#RRGGBB" or "#RGB"
- vertical
Alignment "VERTICAL_ALIGNMENT_UNSPECIFIED" | "V_TOP" | "V_CENTER" | "V_BOTTOM" - The vertical alignment of both the title and content
TextStyleFontSize, TextStyleFontSizeArgs
- Font
Size Unspecified - FONT_SIZE_UNSPECIFIEDNo font size specified, will default to FS_LARGE
- Fs
Extra Small - FS_EXTRA_SMALLExtra small font size
- Fs
Small - FS_SMALLSmall font size
- Fs
Medium - FS_MEDIUMMedium font size
- Fs
Large - FS_LARGELarge font size
- Fs
Extra Large - FS_EXTRA_LARGEExtra large font size
- Text
Style Font Size Font Size Unspecified - FONT_SIZE_UNSPECIFIEDNo font size specified, will default to FS_LARGE
- Text
Style Font Size Fs Extra Small - FS_EXTRA_SMALLExtra small font size
- Text
Style Font Size Fs Small - FS_SMALLSmall font size
- Text
Style Font Size Fs Medium - FS_MEDIUMMedium font size
- Text
Style Font Size Fs Large - FS_LARGELarge font size
- Text
Style Font Size Fs Extra Large - FS_EXTRA_LARGEExtra large font size
- Font
Size Unspecified - FONT_SIZE_UNSPECIFIEDNo font size specified, will default to FS_LARGE
- Fs
Extra Small - FS_EXTRA_SMALLExtra small font size
- Fs
Small - FS_SMALLSmall font size
- Fs
Medium - FS_MEDIUMMedium font size
- Fs
Large - FS_LARGELarge font size
- Fs
Extra Large - FS_EXTRA_LARGEExtra large font size
- Font
Size Unspecified - FONT_SIZE_UNSPECIFIEDNo font size specified, will default to FS_LARGE
- Fs
Extra Small - FS_EXTRA_SMALLExtra small font size
- Fs
Small - FS_SMALLSmall font size
- Fs
Medium - FS_MEDIUMMedium font size
- Fs
Large - FS_LARGELarge font size
- Fs
Extra Large - FS_EXTRA_LARGEExtra large font size
- FONT_SIZE_UNSPECIFIED
- FONT_SIZE_UNSPECIFIEDNo font size specified, will default to FS_LARGE
- FS_EXTRA_SMALL
- FS_EXTRA_SMALLExtra small font size
- FS_SMALL
- FS_SMALLSmall font size
- FS_MEDIUM
- FS_MEDIUMMedium font size
- FS_LARGE
- FS_LARGELarge font size
- FS_EXTRA_LARGE
- FS_EXTRA_LARGEExtra large font size
- "FONT_SIZE_UNSPECIFIED"
- FONT_SIZE_UNSPECIFIEDNo font size specified, will default to FS_LARGE
- "FS_EXTRA_SMALL"
- FS_EXTRA_SMALLExtra small font size
- "FS_SMALL"
- FS_SMALLSmall font size
- "FS_MEDIUM"
- FS_MEDIUMMedium font size
- "FS_LARGE"
- FS_LARGELarge font size
- "FS_EXTRA_LARGE"
- FS_EXTRA_LARGEExtra large font size
TextStyleHorizontalAlignment, TextStyleHorizontalAlignmentArgs
- Horizontal
Alignment Unspecified - HORIZONTAL_ALIGNMENT_UNSPECIFIEDNo horizontal alignment specified, will default to H_LEFT
- HLeft
- H_LEFTLeft-align
- HCenter
- H_CENTERCenter-align
- HRight
- H_RIGHTRight-align
- Text
Style Horizontal Alignment Horizontal Alignment Unspecified - HORIZONTAL_ALIGNMENT_UNSPECIFIEDNo horizontal alignment specified, will default to H_LEFT
- Text
Style Horizontal Alignment HLeft - H_LEFTLeft-align
- Text
Style Horizontal Alignment HCenter - H_CENTERCenter-align
- Text
Style Horizontal Alignment HRight - H_RIGHTRight-align
- Horizontal
Alignment Unspecified - HORIZONTAL_ALIGNMENT_UNSPECIFIEDNo horizontal alignment specified, will default to H_LEFT
- HLeft
- H_LEFTLeft-align
- HCenter
- H_CENTERCenter-align
- HRight
- H_RIGHTRight-align
- Horizontal
Alignment Unspecified - HORIZONTAL_ALIGNMENT_UNSPECIFIEDNo horizontal alignment specified, will default to H_LEFT
- HLeft
- H_LEFTLeft-align
- HCenter
- H_CENTERCenter-align
- HRight
- H_RIGHTRight-align
- HORIZONTAL_ALIGNMENT_UNSPECIFIED
- HORIZONTAL_ALIGNMENT_UNSPECIFIEDNo horizontal alignment specified, will default to H_LEFT
- H_LEFT
- H_LEFTLeft-align
- H_CENTER
- H_CENTERCenter-align
- H_RIGHT
- H_RIGHTRight-align
- "HORIZONTAL_ALIGNMENT_UNSPECIFIED"
- HORIZONTAL_ALIGNMENT_UNSPECIFIEDNo horizontal alignment specified, will default to H_LEFT
- "H_LEFT"
- H_LEFTLeft-align
- "H_CENTER"
- H_CENTERCenter-align
- "H_RIGHT"
- H_RIGHTRight-align
TextStylePadding, TextStylePaddingArgs
- Padding
Size Unspecified - PADDING_SIZE_UNSPECIFIEDNo padding size specified, will default to P_EXTRA_SMALL
- PExtra
Small - P_EXTRA_SMALLExtra small padding
- PSmall
- P_SMALLSmall padding
- PMedium
- P_MEDIUMMedium padding
- PLarge
- P_LARGELarge padding
- PExtra
Large - P_EXTRA_LARGEExtra large padding
- Text
Style Padding Padding Size Unspecified - PADDING_SIZE_UNSPECIFIEDNo padding size specified, will default to P_EXTRA_SMALL
- Text
Style Padding PExtra Small - P_EXTRA_SMALLExtra small padding
- Text
Style Padding PSmall - P_SMALLSmall padding
- Text
Style Padding PMedium - P_MEDIUMMedium padding
- Text
Style Padding PLarge - P_LARGELarge padding
- Text
Style Padding PExtra Large - P_EXTRA_LARGEExtra large padding
- Padding
Size Unspecified - PADDING_SIZE_UNSPECIFIEDNo padding size specified, will default to P_EXTRA_SMALL
- PExtra
Small - P_EXTRA_SMALLExtra small padding
- PSmall
- P_SMALLSmall padding
- PMedium
- P_MEDIUMMedium padding
- PLarge
- P_LARGELarge padding
- PExtra
Large - P_EXTRA_LARGEExtra large padding
- Padding
Size Unspecified - PADDING_SIZE_UNSPECIFIEDNo padding size specified, will default to P_EXTRA_SMALL
- PExtra
Small - P_EXTRA_SMALLExtra small padding
- PSmall
- P_SMALLSmall padding
- PMedium
- P_MEDIUMMedium padding
- PLarge
- P_LARGELarge padding
- PExtra
Large - P_EXTRA_LARGEExtra large padding
- PADDING_SIZE_UNSPECIFIED
- PADDING_SIZE_UNSPECIFIEDNo padding size specified, will default to P_EXTRA_SMALL
- P_EXTRA_SMALL
- P_EXTRA_SMALLExtra small padding
- P_SMALL
- P_SMALLSmall padding
- P_MEDIUM
- P_MEDIUMMedium padding
- P_LARGE
- P_LARGELarge padding
- P_EXTRA_LARGE
- P_EXTRA_LARGEExtra large padding
- "PADDING_SIZE_UNSPECIFIED"
- PADDING_SIZE_UNSPECIFIEDNo padding size specified, will default to P_EXTRA_SMALL
- "P_EXTRA_SMALL"
- P_EXTRA_SMALLExtra small padding
- "P_SMALL"
- P_SMALLSmall padding
- "P_MEDIUM"
- P_MEDIUMMedium padding
- "P_LARGE"
- P_LARGELarge padding
- "P_EXTRA_LARGE"
- P_EXTRA_LARGEExtra large padding
TextStylePointerLocation, TextStylePointerLocationArgs
- Pointer
Location Unspecified - POINTER_LOCATION_UNSPECIFIEDNo visual pointer
- Pl
Top - PL_TOPPlaced in the middle of the top of the widget
- Pl
Right - PL_RIGHTPlaced in the middle of the right side of the widget
- Pl
Bottom - PL_BOTTOMPlaced in the middle of the bottom of the widget
- Pl
Left - PL_LEFTPlaced in the middle of the left side of the widget
- Pl
Top Left - PL_TOP_LEFTPlaced on the left side of the top of the widget
- Pl
Top Right - PL_TOP_RIGHTPlaced on the right side of the top of the widget
- Pl
Right Top - PL_RIGHT_TOPPlaced on the top of the right side of the widget
- Pl
Right Bottom - PL_RIGHT_BOTTOMPlaced on the bottom of the right side of the widget
- Pl
Bottom Right - PL_BOTTOM_RIGHTPlaced on the right side of the bottom of the widget
- Pl
Bottom Left - PL_BOTTOM_LEFTPlaced on the left side of the bottom of the widget
- Pl
Left Bottom - PL_LEFT_BOTTOMPlaced on the bottom of the left side of the widget
- Pl
Left Top - PL_LEFT_TOPPlaced on the top of the left side of the widget
- Text
Style Pointer Location Pointer Location Unspecified - POINTER_LOCATION_UNSPECIFIEDNo visual pointer
- Text
Style Pointer Location Pl Top - PL_TOPPlaced in the middle of the top of the widget
- Text
Style Pointer Location Pl Right - PL_RIGHTPlaced in the middle of the right side of the widget
- Text
Style Pointer Location Pl Bottom - PL_BOTTOMPlaced in the middle of the bottom of the widget
- Text
Style Pointer Location Pl Left - PL_LEFTPlaced in the middle of the left side of the widget
- Text
Style Pointer Location Pl Top Left - PL_TOP_LEFTPlaced on the left side of the top of the widget
- Text
Style Pointer Location Pl Top Right - PL_TOP_RIGHTPlaced on the right side of the top of the widget
- Text
Style Pointer Location Pl Right Top - PL_RIGHT_TOPPlaced on the top of the right side of the widget
- Text
Style Pointer Location Pl Right Bottom - PL_RIGHT_BOTTOMPlaced on the bottom of the right side of the widget
- Text
Style Pointer Location Pl Bottom Right - PL_BOTTOM_RIGHTPlaced on the right side of the bottom of the widget
- Text
Style Pointer Location Pl Bottom Left - PL_BOTTOM_LEFTPlaced on the left side of the bottom of the widget
- Text
Style Pointer Location Pl Left Bottom - PL_LEFT_BOTTOMPlaced on the bottom of the left side of the widget
- Text
Style Pointer Location Pl Left Top - PL_LEFT_TOPPlaced on the top of the left side of the widget
- Pointer
Location Unspecified - POINTER_LOCATION_UNSPECIFIEDNo visual pointer
- Pl
Top - PL_TOPPlaced in the middle of the top of the widget
- Pl
Right - PL_RIGHTPlaced in the middle of the right side of the widget
- Pl
Bottom - PL_BOTTOMPlaced in the middle of the bottom of the widget
- Pl
Left - PL_LEFTPlaced in the middle of the left side of the widget
- Pl
Top Left - PL_TOP_LEFTPlaced on the left side of the top of the widget
- Pl
Top Right - PL_TOP_RIGHTPlaced on the right side of the top of the widget
- Pl
Right Top - PL_RIGHT_TOPPlaced on the top of the right side of the widget
- Pl
Right Bottom - PL_RIGHT_BOTTOMPlaced on the bottom of the right side of the widget
- Pl
Bottom Right - PL_BOTTOM_RIGHTPlaced on the right side of the bottom of the widget
- Pl
Bottom Left - PL_BOTTOM_LEFTPlaced on the left side of the bottom of the widget
- Pl
Left Bottom - PL_LEFT_BOTTOMPlaced on the bottom of the left side of the widget
- Pl
Left Top - PL_LEFT_TOPPlaced on the top of the left side of the widget
- Pointer
Location Unspecified - POINTER_LOCATION_UNSPECIFIEDNo visual pointer
- Pl
Top - PL_TOPPlaced in the middle of the top of the widget
- Pl
Right - PL_RIGHTPlaced in the middle of the right side of the widget
- Pl
Bottom - PL_BOTTOMPlaced in the middle of the bottom of the widget
- Pl
Left - PL_LEFTPlaced in the middle of the left side of the widget
- Pl
Top Left - PL_TOP_LEFTPlaced on the left side of the top of the widget
- Pl
Top Right - PL_TOP_RIGHTPlaced on the right side of the top of the widget
- Pl
Right Top - PL_RIGHT_TOPPlaced on the top of the right side of the widget
- Pl
Right Bottom - PL_RIGHT_BOTTOMPlaced on the bottom of the right side of the widget
- Pl
Bottom Right - PL_BOTTOM_RIGHTPlaced on the right side of the bottom of the widget
- Pl
Bottom Left - PL_BOTTOM_LEFTPlaced on the left side of the bottom of the widget
- Pl
Left Bottom - PL_LEFT_BOTTOMPlaced on the bottom of the left side of the widget
- Pl
Left Top - PL_LEFT_TOPPlaced on the top of the left side of the widget
- POINTER_LOCATION_UNSPECIFIED
- POINTER_LOCATION_UNSPECIFIEDNo visual pointer
- PL_TOP
- PL_TOPPlaced in the middle of the top of the widget
- PL_RIGHT
- PL_RIGHTPlaced in the middle of the right side of the widget
- PL_BOTTOM
- PL_BOTTOMPlaced in the middle of the bottom of the widget
- PL_LEFT
- PL_LEFTPlaced in the middle of the left side of the widget
- PL_TOP_LEFT
- PL_TOP_LEFTPlaced on the left side of the top of the widget
- PL_TOP_RIGHT
- PL_TOP_RIGHTPlaced on the right side of the top of the widget
- PL_RIGHT_TOP
- PL_RIGHT_TOPPlaced on the top of the right side of the widget
- PL_RIGHT_BOTTOM
- PL_RIGHT_BOTTOMPlaced on the bottom of the right side of the widget
- PL_BOTTOM_RIGHT
- PL_BOTTOM_RIGHTPlaced on the right side of the bottom of the widget
- PL_BOTTOM_LEFT
- PL_BOTTOM_LEFTPlaced on the left side of the bottom of the widget
- PL_LEFT_BOTTOM
- PL_LEFT_BOTTOMPlaced on the bottom of the left side of the widget
- PL_LEFT_TOP
- PL_LEFT_TOPPlaced on the top of the left side of the widget
- "POINTER_LOCATION_UNSPECIFIED"
- POINTER_LOCATION_UNSPECIFIEDNo visual pointer
- "PL_TOP"
- PL_TOPPlaced in the middle of the top of the widget
- "PL_RIGHT"
- PL_RIGHTPlaced in the middle of the right side of the widget
- "PL_BOTTOM"
- PL_BOTTOMPlaced in the middle of the bottom of the widget
- "PL_LEFT"
- PL_LEFTPlaced in the middle of the left side of the widget
- "PL_TOP_LEFT"
- PL_TOP_LEFTPlaced on the left side of the top of the widget
- "PL_TOP_RIGHT"
- PL_TOP_RIGHTPlaced on the right side of the top of the widget
- "PL_RIGHT_TOP"
- PL_RIGHT_TOPPlaced on the top of the right side of the widget
- "PL_RIGHT_BOTTOM"
- PL_RIGHT_BOTTOMPlaced on the bottom of the right side of the widget
- "PL_BOTTOM_RIGHT"
- PL_BOTTOM_RIGHTPlaced on the right side of the bottom of the widget
- "PL_BOTTOM_LEFT"
- PL_BOTTOM_LEFTPlaced on the left side of the bottom of the widget
- "PL_LEFT_BOTTOM"
- PL_LEFT_BOTTOMPlaced on the bottom of the left side of the widget
- "PL_LEFT_TOP"
- PL_LEFT_TOPPlaced on the top of the left side of the widget
TextStyleResponse, TextStyleResponseArgs
- Background
Color string - The background color as a hex string. "#RRGGBB" or "#RGB"
- Font
Size string - Font sizes for both the title and content. The title will still be larger relative to the content.
- Horizontal
Alignment string - The horizontal alignment of both the title and content
- Padding string
- The amount of padding around the widget
- Pointer
Location string - The pointer location for this widget (also sometimes called a "tail")
- Text
Color string - The text color as a hex string. "#RRGGBB" or "#RGB"
- Vertical
Alignment string - The vertical alignment of both the title and content
- Background
Color string - The background color as a hex string. "#RRGGBB" or "#RGB"
- Font
Size string - Font sizes for both the title and content. The title will still be larger relative to the content.
- Horizontal
Alignment string - The horizontal alignment of both the title and content
- Padding string
- The amount of padding around the widget
- Pointer
Location string - The pointer location for this widget (also sometimes called a "tail")
- Text
Color string - The text color as a hex string. "#RRGGBB" or "#RGB"
- Vertical
Alignment string - The vertical alignment of both the title and content
- background
Color String - The background color as a hex string. "#RRGGBB" or "#RGB"
- font
Size String - Font sizes for both the title and content. The title will still be larger relative to the content.
- horizontal
Alignment String - The horizontal alignment of both the title and content
- padding String
- The amount of padding around the widget
- pointer
Location String - The pointer location for this widget (also sometimes called a "tail")
- text
Color String - The text color as a hex string. "#RRGGBB" or "#RGB"
- vertical
Alignment String - The vertical alignment of both the title and content
- background
Color string - The background color as a hex string. "#RRGGBB" or "#RGB"
- font
Size string - Font sizes for both the title and content. The title will still be larger relative to the content.
- horizontal
Alignment string - The horizontal alignment of both the title and content
- padding string
- The amount of padding around the widget
- pointer
Location string - The pointer location for this widget (also sometimes called a "tail")
- text
Color string - The text color as a hex string. "#RRGGBB" or "#RGB"
- vertical
Alignment string - The vertical alignment of both the title and content
- background_
color str - The background color as a hex string. "#RRGGBB" or "#RGB"
- font_
size str - Font sizes for both the title and content. The title will still be larger relative to the content.
- horizontal_
alignment str - The horizontal alignment of both the title and content
- padding str
- The amount of padding around the widget
- pointer_
location str - The pointer location for this widget (also sometimes called a "tail")
- text_
color str - The text color as a hex string. "#RRGGBB" or "#RGB"
- vertical_
alignment str - The vertical alignment of both the title and content
- background
Color String - The background color as a hex string. "#RRGGBB" or "#RGB"
- font
Size String - Font sizes for both the title and content. The title will still be larger relative to the content.
- horizontal
Alignment String - The horizontal alignment of both the title and content
- padding String
- The amount of padding around the widget
- pointer
Location String - The pointer location for this widget (also sometimes called a "tail")
- text
Color String - The text color as a hex string. "#RRGGBB" or "#RGB"
- vertical
Alignment String - The vertical alignment of both the title and content
TextStyleVerticalAlignment, TextStyleVerticalAlignmentArgs
- Vertical
Alignment Unspecified - VERTICAL_ALIGNMENT_UNSPECIFIEDNo vertical alignment specified, will default to V_TOP
- VTop
- V_TOPTop-align
- VCenter
- V_CENTERCenter-align
- VBottom
- V_BOTTOMBottom-align
- Text
Style Vertical Alignment Vertical Alignment Unspecified - VERTICAL_ALIGNMENT_UNSPECIFIEDNo vertical alignment specified, will default to V_TOP
- Text
Style Vertical Alignment VTop - V_TOPTop-align
- Text
Style Vertical Alignment VCenter - V_CENTERCenter-align
- Text
Style Vertical Alignment VBottom - V_BOTTOMBottom-align
- Vertical
Alignment Unspecified - VERTICAL_ALIGNMENT_UNSPECIFIEDNo vertical alignment specified, will default to V_TOP
- VTop
- V_TOPTop-align
- VCenter
- V_CENTERCenter-align
- VBottom
- V_BOTTOMBottom-align
- Vertical
Alignment Unspecified - VERTICAL_ALIGNMENT_UNSPECIFIEDNo vertical alignment specified, will default to V_TOP
- VTop
- V_TOPTop-align
- VCenter
- V_CENTERCenter-align
- VBottom
- V_BOTTOMBottom-align
- VERTICAL_ALIGNMENT_UNSPECIFIED
- VERTICAL_ALIGNMENT_UNSPECIFIEDNo vertical alignment specified, will default to V_TOP
- V_TOP
- V_TOPTop-align
- V_CENTER
- V_CENTERCenter-align
- V_BOTTOM
- V_BOTTOMBottom-align
- "VERTICAL_ALIGNMENT_UNSPECIFIED"
- VERTICAL_ALIGNMENT_UNSPECIFIEDNo vertical alignment specified, will default to V_TOP
- "V_TOP"
- V_TOPTop-align
- "V_CENTER"
- V_CENTERCenter-align
- "V_BOTTOM"
- V_BOTTOMBottom-align
Threshold, ThresholdArgs
- Color
Pulumi.
Google Native. Monitoring. V1. Threshold Color - The state color for this threshold. Color is not allowed in a XyChart.
- Direction
Pulumi.
Google Native. Monitoring. V1. Threshold Direction - The direction for the current threshold. Direction is not allowed in a XyChart.
- Label string
- A label for the threshold.
- Target
Axis Pulumi.Google Native. Monitoring. V1. Threshold Target Axis - 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
Threshold
Color - The state color for this threshold. Color is not allowed in a XyChart.
- Direction
Threshold
Direction - The direction for the current threshold. Direction is not allowed in a XyChart.
- Label string
- A label for the threshold.
- Target
Axis ThresholdTarget Axis - 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
Threshold
Color - The state color for this threshold. Color is not allowed in a XyChart.
- direction
Threshold
Direction - The direction for the current threshold. Direction is not allowed in a XyChart.
- label String
- A label for the threshold.
- target
Axis ThresholdTarget Axis - 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
Threshold
Color - The state color for this threshold. Color is not allowed in a XyChart.
- direction
Threshold
Direction - The direction for the current threshold. Direction is not allowed in a XyChart.
- label string
- A label for the threshold.
- target
Axis ThresholdTarget Axis - 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
Threshold
Color - The state color for this threshold. Color is not allowed in a XyChart.
- direction
Threshold
Direction - The direction for the current threshold. Direction is not allowed in a XyChart.
- label str
- A label for the threshold.
- target_
axis ThresholdTarget Axis - 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.
- target
Axis "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, ThresholdColorArgs
- Color
Unspecified - COLOR_UNSPECIFIEDColor is unspecified. Not allowed in well-formed requests.
- Yellow
- YELLOWCrossing the threshold is "concerning" behavior.
- Red
- REDCrossing the threshold is "emergency" behavior.
- Threshold
Color Color Unspecified - COLOR_UNSPECIFIEDColor is unspecified. Not allowed in well-formed requests.
- Threshold
Color Yellow - YELLOWCrossing the threshold is "concerning" behavior.
- Threshold
Color Red - REDCrossing the threshold is "emergency" behavior.
- Color
Unspecified - COLOR_UNSPECIFIEDColor is unspecified. Not allowed in well-formed requests.
- Yellow
- YELLOWCrossing the threshold is "concerning" behavior.
- Red
- REDCrossing the threshold is "emergency" behavior.
- Color
Unspecified - COLOR_UNSPECIFIEDColor is unspecified. Not allowed in well-formed requests.
- Yellow
- YELLOWCrossing the threshold is "concerning" behavior.
- Red
- REDCrossing the threshold is "emergency" behavior.
- COLOR_UNSPECIFIED
- COLOR_UNSPECIFIEDColor is unspecified. Not allowed in well-formed requests.
- YELLOW
- YELLOWCrossing the threshold is "concerning" behavior.
- RED
- REDCrossing the threshold is "emergency" behavior.
- "COLOR_UNSPECIFIED"
- COLOR_UNSPECIFIEDColor is unspecified. Not allowed in well-formed requests.
- "YELLOW"
- YELLOWCrossing the threshold is "concerning" behavior.
- "RED"
- REDCrossing the threshold is "emergency" behavior.
ThresholdDirection, ThresholdDirectionArgs
- Direction
Unspecified - DIRECTION_UNSPECIFIEDNot allowed in well-formed requests.
- Above
- ABOVEThe threshold will be considered crossed if the actual value is above the threshold value.
- Below
- BELOWThe threshold will be considered crossed if the actual value is below the threshold value.
- Threshold
Direction Direction Unspecified - DIRECTION_UNSPECIFIEDNot allowed in well-formed requests.
- Threshold
Direction Above - ABOVEThe threshold will be considered crossed if the actual value is above the threshold value.
- Threshold
Direction Below - BELOWThe threshold will be considered crossed if the actual value is below the threshold value.
- Direction
Unspecified - DIRECTION_UNSPECIFIEDNot allowed in well-formed requests.
- Above
- ABOVEThe threshold will be considered crossed if the actual value is above the threshold value.
- Below
- BELOWThe threshold will be considered crossed if the actual value is below the threshold value.
- Direction
Unspecified - DIRECTION_UNSPECIFIEDNot allowed in well-formed requests.
- Above
- ABOVEThe threshold will be considered crossed if the actual value is above the threshold value.
- Below
- BELOWThe threshold will be considered crossed if the actual value is below the threshold value.
- DIRECTION_UNSPECIFIED
- DIRECTION_UNSPECIFIEDNot allowed in well-formed requests.
- ABOVE
- ABOVEThe threshold will be considered crossed if the actual value is above the threshold value.
- BELOW
- BELOWThe threshold will be considered crossed if the actual value is below the threshold value.
- "DIRECTION_UNSPECIFIED"
- DIRECTION_UNSPECIFIEDNot allowed in well-formed requests.
- "ABOVE"
- ABOVEThe threshold will be considered crossed if the actual value is above the threshold value.
- "BELOW"
- BELOWThe threshold will be considered crossed if the actual value is below the threshold value.
ThresholdResponse, ThresholdResponseArgs
- 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.
- Target
Axis 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.
- Target
Axis 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.
- target
Axis 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.
- target
Axis 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.
- target
Axis 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, ThresholdTargetAxisArgs
- Target
Axis Unspecified - TARGET_AXIS_UNSPECIFIEDThe target axis was not specified. Defaults to Y1.
- Y1
- Y1The y_axis (the right axis of chart).
- Y2
- Y2The y2_axis (the left axis of chart).
- Threshold
Target Axis Target Axis Unspecified - TARGET_AXIS_UNSPECIFIEDThe target axis was not specified. Defaults to Y1.
- Threshold
Target Axis Y1 - Y1The y_axis (the right axis of chart).
- Threshold
Target Axis Y2 - Y2The y2_axis (the left axis of chart).
- Target
Axis Unspecified - TARGET_AXIS_UNSPECIFIEDThe target axis was not specified. Defaults to Y1.
- Y1
- Y1The y_axis (the right axis of chart).
- Y2
- Y2The y2_axis (the left axis of chart).
- Target
Axis Unspecified - TARGET_AXIS_UNSPECIFIEDThe target axis was not specified. Defaults to Y1.
- Y1
- Y1The y_axis (the right axis of chart).
- Y2
- Y2The y2_axis (the left axis of chart).
- TARGET_AXIS_UNSPECIFIED
- TARGET_AXIS_UNSPECIFIEDThe target axis was not specified. Defaults to Y1.
- Y1
- Y1The y_axis (the right axis of chart).
- Y2
- Y2The y2_axis (the left axis of chart).
- "TARGET_AXIS_UNSPECIFIED"
- TARGET_AXIS_UNSPECIFIEDThe target axis was not specified. Defaults to Y1.
- "Y1"
- Y1The y_axis (the right axis of chart).
- "Y2"
- Y2The y2_axis (the left axis of chart).
Tile, TileArgs
- Height int
- The height of the tile, measured in grid blocks. Tiles must have a minimum height of 1.
- Widget
Pulumi.
Google Native. 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.
- x
Pos 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.
- y
Pos 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.
- x
Pos 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.
- y
Pos 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.
- x
Pos 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.
- y
Pos 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, TileResponseArgs
- Height int
- The height of the tile, measured in grid blocks. Tiles must have a minimum height of 1.
- Widget
Pulumi.
Google Native. Monitoring. V1. Inputs. Widget Response - 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
Response - 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
Response - 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.
- x
Pos 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.
- y
Pos 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
Response - 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.
- x
Pos 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.
- y
Pos 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
Response - 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.
- x
Pos 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.
- y
Pos 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, TimeSeriesFilterArgs
- 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.
Google Native. 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.
- Pick
Time Pulumi.Series Filter Google Native. Monitoring. V1. Inputs. Pick Time Series Filter - Ranking based time series filter.
- Secondary
Aggregation Pulumi.Google Native. Monitoring. V1. Inputs. Aggregation - Apply a second aggregation after aggregation is applied.
- Statistical
Time Pulumi.Series Filter Google Native. Monitoring. V1. Inputs. Statistical Time Series Filter - 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.
- Pick
Time PickSeries Filter Time Series Filter - Ranking based time series filter.
- Secondary
Aggregation Aggregation - Apply a second aggregation after aggregation is applied.
- Statistical
Time StatisticalSeries Filter Time Series Filter - 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.
- pick
Time PickSeries Filter Time Series Filter - Ranking based time series filter.
- secondary
Aggregation Aggregation - Apply a second aggregation after aggregation is applied.
- statistical
Time StatisticalSeries Filter Time Series Filter - 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.
- pick
Time PickSeries Filter Time Series Filter - Ranking based time series filter.
- secondary
Aggregation Aggregation - Apply a second aggregation after aggregation is applied.
- statistical
Time StatisticalSeries Filter Time Series Filter - 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_ Pickseries_ filter Time Series Filter - Ranking based time series filter.
- secondary_
aggregation Aggregation - Apply a second aggregation after aggregation is applied.
- statistical_
time_ Statisticalseries_ filter Time Series Filter - 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.
- pick
Time Property MapSeries Filter - Ranking based time series filter.
- secondary
Aggregation Property Map - Apply a second aggregation after aggregation is applied.
- statistical
Time Property MapSeries Filter - Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.
TimeSeriesFilterRatio, TimeSeriesFilterRatioArgs
- Denominator
Pulumi.
Google Native. Monitoring. V1. Inputs. Ratio Part - The denominator of the ratio.
- Numerator
Pulumi.
Google Native. Monitoring. V1. Inputs. Ratio Part - The numerator of the ratio.
- Pick
Time Pulumi.Series Filter Google Native. Monitoring. V1. Inputs. Pick Time Series Filter - Ranking based time series filter.
- Secondary
Aggregation Pulumi.Google Native. Monitoring. V1. Inputs. Aggregation - Apply a second aggregation after the ratio is computed.
- Statistical
Time Pulumi.Series Filter Google Native. Monitoring. V1. Inputs. Statistical Time Series Filter - Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.
- Denominator
Ratio
Part - The denominator of the ratio.
- Numerator
Ratio
Part - The numerator of the ratio.
- Pick
Time PickSeries Filter Time Series Filter - Ranking based time series filter.
- Secondary
Aggregation Aggregation - Apply a second aggregation after the ratio is computed.
- Statistical
Time StatisticalSeries Filter Time Series Filter - Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.
- denominator
Ratio
Part - The denominator of the ratio.
- numerator
Ratio
Part - The numerator of the ratio.
- pick
Time PickSeries Filter Time Series Filter - Ranking based time series filter.
- secondary
Aggregation Aggregation - Apply a second aggregation after the ratio is computed.
- statistical
Time StatisticalSeries Filter Time Series Filter - Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.
- denominator
Ratio
Part - The denominator of the ratio.
- numerator
Ratio
Part - The numerator of the ratio.
- pick
Time PickSeries Filter Time Series Filter - Ranking based time series filter.
- secondary
Aggregation Aggregation - Apply a second aggregation after the ratio is computed.
- statistical
Time StatisticalSeries Filter Time Series Filter - Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.
- denominator
Ratio
Part - The denominator of the ratio.
- numerator
Ratio
Part - The numerator of the ratio.
- pick_
time_ Pickseries_ filter Time Series Filter - Ranking based time series filter.
- secondary_
aggregation Aggregation - Apply a second aggregation after the ratio is computed.
- statistical_
time_ Statisticalseries_ filter Time Series Filter - 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.
- pick
Time Property MapSeries Filter - Ranking based time series filter.
- secondary
Aggregation Property Map - Apply a second aggregation after the ratio is computed.
- statistical
Time Property MapSeries Filter - Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.
TimeSeriesFilterRatioResponse, TimeSeriesFilterRatioResponseArgs
- Denominator
Pulumi.
Google Native. Monitoring. V1. Inputs. Ratio Part Response - The denominator of the ratio.
- Numerator
Pulumi.
Google Native. Monitoring. V1. Inputs. Ratio Part Response - The numerator of the ratio.
- Pick
Time Pulumi.Series Filter Google Native. Monitoring. V1. Inputs. Pick Time Series Filter Response - Ranking based time series filter.
- Secondary
Aggregation Pulumi.Google Native. Monitoring. V1. Inputs. Aggregation Response - Apply a second aggregation after the ratio is computed.
- Statistical
Time Pulumi.Series Filter Google Native. Monitoring. V1. Inputs. Statistical Time Series Filter Response - Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.
- Denominator
Ratio
Part Response - The denominator of the ratio.
- Numerator
Ratio
Part Response - The numerator of the ratio.
- Pick
Time PickSeries Filter Time Series Filter Response - Ranking based time series filter.
- Secondary
Aggregation AggregationResponse - Apply a second aggregation after the ratio is computed.
- Statistical
Time StatisticalSeries Filter Time Series Filter Response - Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.
- denominator
Ratio
Part Response - The denominator of the ratio.
- numerator
Ratio
Part Response - The numerator of the ratio.
- pick
Time PickSeries Filter Time Series Filter Response - Ranking based time series filter.
- secondary
Aggregation AggregationResponse - Apply a second aggregation after the ratio is computed.
- statistical
Time StatisticalSeries Filter Time Series Filter Response - Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.
- denominator
Ratio
Part Response - The denominator of the ratio.
- numerator
Ratio
Part Response - The numerator of the ratio.
- pick
Time PickSeries Filter Time Series Filter Response - Ranking based time series filter.
- secondary
Aggregation AggregationResponse - Apply a second aggregation after the ratio is computed.
- statistical
Time StatisticalSeries Filter Time Series Filter Response - Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.
- denominator
Ratio
Part Response - The denominator of the ratio.
- numerator
Ratio
Part Response - The numerator of the ratio.
- pick_
time_ Pickseries_ filter Time Series Filter Response - Ranking based time series filter.
- secondary_
aggregation AggregationResponse - Apply a second aggregation after the ratio is computed.
- statistical_
time_ Statisticalseries_ filter Time Series Filter Response - 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.
- pick
Time Property MapSeries Filter - Ranking based time series filter.
- secondary
Aggregation Property Map - Apply a second aggregation after the ratio is computed.
- statistical
Time Property MapSeries Filter - Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.
TimeSeriesFilterResponse, TimeSeriesFilterResponseArgs
- Aggregation
Pulumi.
Google Native. Monitoring. V1. Inputs. Aggregation Response - 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.
- Pick
Time Pulumi.Series Filter Google Native. Monitoring. V1. Inputs. Pick Time Series Filter Response - Ranking based time series filter.
- Secondary
Aggregation Pulumi.Google Native. Monitoring. V1. Inputs. Aggregation Response - Apply a second aggregation after aggregation is applied.
- Statistical
Time Pulumi.Series Filter Google Native. Monitoring. V1. Inputs. Statistical Time Series Filter Response - Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.
- Aggregation
Aggregation
Response - 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.
- Pick
Time PickSeries Filter Time Series Filter Response - Ranking based time series filter.
- Secondary
Aggregation AggregationResponse - Apply a second aggregation after aggregation is applied.
- Statistical
Time StatisticalSeries Filter Time Series Filter Response - Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.
- aggregation
Aggregation
Response - 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.
- pick
Time PickSeries Filter Time Series Filter Response - Ranking based time series filter.
- secondary
Aggregation AggregationResponse - Apply a second aggregation after aggregation is applied.
- statistical
Time StatisticalSeries Filter Time Series Filter Response - Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.
- aggregation
Aggregation
Response - 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.
- pick
Time PickSeries Filter Time Series Filter Response - Ranking based time series filter.
- secondary
Aggregation AggregationResponse - Apply a second aggregation after aggregation is applied.
- statistical
Time StatisticalSeries Filter Time Series Filter Response - Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.
- aggregation
Aggregation
Response - 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_ Pickseries_ filter Time Series Filter Response - Ranking based time series filter.
- secondary_
aggregation AggregationResponse - Apply a second aggregation after aggregation is applied.
- statistical_
time_ Statisticalseries_ filter Time Series Filter Response - 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.
- pick
Time Property MapSeries Filter - Ranking based time series filter.
- secondary
Aggregation Property Map - Apply a second aggregation after aggregation is applied.
- statistical
Time Property MapSeries Filter - Statistics based time series filter. Note: This field is deprecated and completely ignored by the API.
TimeSeriesQuery, TimeSeriesQueryArgs
- Ops
Analytics Pulumi.Query Google Native. Monitoring. V1. Inputs. Ops Analytics Query - Preview: A query used to fetch a time series, category series, or numeric series with SQL. This is a preview feature and may be subject to change before final release.
- Output
Full boolDuration - Optional. If set, Cloud Monitoring will treat the full query duration as the alignment period so that there will be only 1 output value.*Note: This could override the configured alignment period except for the cases where a series of data points are expected, like - XyChart - Scorecard's spark chart
- Prometheus
Query string - A query used to fetch time series with PromQL.
- Time
Series Pulumi.Filter Google Native. Monitoring. V1. Inputs. Time Series Filter - Filter parameters to fetch time series.
- Time
Series Pulumi.Filter Ratio Google Native. Monitoring. V1. Inputs. Time Series Filter Ratio - Parameters to fetch a ratio between two time series filters.
- Time
Series stringQuery Language - A query used to fetch time series with MQL.
- Unit
Override 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.
- Ops
Analytics OpsQuery Analytics Query - Preview: A query used to fetch a time series, category series, or numeric series with SQL. This is a preview feature and may be subject to change before final release.
- Output
Full boolDuration - Optional. If set, Cloud Monitoring will treat the full query duration as the alignment period so that there will be only 1 output value.*Note: This could override the configured alignment period except for the cases where a series of data points are expected, like - XyChart - Scorecard's spark chart
- Prometheus
Query string - A query used to fetch time series with PromQL.
- Time
Series TimeFilter Series Filter - Filter parameters to fetch time series.
- Time
Series TimeFilter Ratio Series Filter Ratio - Parameters to fetch a ratio between two time series filters.
- Time
Series stringQuery Language - A query used to fetch time series with MQL.
- Unit
Override 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.
- ops
Analytics OpsQuery Analytics Query - Preview: A query used to fetch a time series, category series, or numeric series with SQL. This is a preview feature and may be subject to change before final release.
- output
Full BooleanDuration - Optional. If set, Cloud Monitoring will treat the full query duration as the alignment period so that there will be only 1 output value.*Note: This could override the configured alignment period except for the cases where a series of data points are expected, like - XyChart - Scorecard's spark chart
- prometheus
Query String - A query used to fetch time series with PromQL.
- time
Series TimeFilter Series Filter - Filter parameters to fetch time series.
- time
Series TimeFilter Ratio Series Filter Ratio - Parameters to fetch a ratio between two time series filters.
- time
Series StringQuery Language - A query used to fetch time series with MQL.
- unit
Override 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.
- ops
Analytics OpsQuery Analytics Query - Preview: A query used to fetch a time series, category series, or numeric series with SQL. This is a preview feature and may be subject to change before final release.
- output
Full booleanDuration - Optional. If set, Cloud Monitoring will treat the full query duration as the alignment period so that there will be only 1 output value.*Note: This could override the configured alignment period except for the cases where a series of data points are expected, like - XyChart - Scorecard's spark chart
- prometheus
Query string - A query used to fetch time series with PromQL.
- time
Series TimeFilter Series Filter - Filter parameters to fetch time series.
- time
Series TimeFilter Ratio Series Filter Ratio - Parameters to fetch a ratio between two time series filters.
- time
Series stringQuery Language - A query used to fetch time series with MQL.
- unit
Override 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.
- ops_
analytics_ Opsquery Analytics Query - Preview: A query used to fetch a time series, category series, or numeric series with SQL. This is a preview feature and may be subject to change before final release.
- output_
full_ boolduration - Optional. If set, Cloud Monitoring will treat the full query duration as the alignment period so that there will be only 1 output value.*Note: This could override the configured alignment period except for the cases where a series of data points are expected, like - XyChart - Scorecard's spark chart
- prometheus_
query str - A query used to fetch time series with PromQL.
- time_
series_ Timefilter Series Filter - Filter parameters to fetch time series.
- time_
series_ Timefilter_ ratio Series Filter Ratio - Parameters to fetch a ratio between two time series filters.
- time_
series_ strquery_ language - 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.
- ops
Analytics Property MapQuery - Preview: A query used to fetch a time series, category series, or numeric series with SQL. This is a preview feature and may be subject to change before final release.
- output
Full BooleanDuration - Optional. If set, Cloud Monitoring will treat the full query duration as the alignment period so that there will be only 1 output value.*Note: This could override the configured alignment period except for the cases where a series of data points are expected, like - XyChart - Scorecard's spark chart
- prometheus
Query String - A query used to fetch time series with PromQL.
- time
Series Property MapFilter - Filter parameters to fetch time series.
- time
Series Property MapFilter Ratio - Parameters to fetch a ratio between two time series filters.
- time
Series StringQuery Language - A query used to fetch time series with MQL.
- unit
Override 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, TimeSeriesQueryResponseArgs
- Ops
Analytics Pulumi.Query Google Native. Monitoring. V1. Inputs. Ops Analytics Query Response - Preview: A query used to fetch a time series, category series, or numeric series with SQL. This is a preview feature and may be subject to change before final release.
- Output
Full boolDuration - Optional. If set, Cloud Monitoring will treat the full query duration as the alignment period so that there will be only 1 output value.*Note: This could override the configured alignment period except for the cases where a series of data points are expected, like - XyChart - Scorecard's spark chart
- Prometheus
Query string - A query used to fetch time series with PromQL.
- Time
Series Pulumi.Filter Google Native. Monitoring. V1. Inputs. Time Series Filter Response - Filter parameters to fetch time series.
- Time
Series Pulumi.Filter Ratio Google Native. Monitoring. V1. Inputs. Time Series Filter Ratio Response - Parameters to fetch a ratio between two time series filters.
- Time
Series stringQuery Language - A query used to fetch time series with MQL.
- Unit
Override 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.
- Ops
Analytics OpsQuery Analytics Query Response - Preview: A query used to fetch a time series, category series, or numeric series with SQL. This is a preview feature and may be subject to change before final release.
- Output
Full boolDuration - Optional. If set, Cloud Monitoring will treat the full query duration as the alignment period so that there will be only 1 output value.*Note: This could override the configured alignment period except for the cases where a series of data points are expected, like - XyChart - Scorecard's spark chart
- Prometheus
Query string - A query used to fetch time series with PromQL.
- Time
Series TimeFilter Series Filter Response - Filter parameters to fetch time series.
- Time
Series TimeFilter Ratio Series Filter Ratio Response - Parameters to fetch a ratio between two time series filters.
- Time
Series stringQuery Language - A query used to fetch time series with MQL.
- Unit
Override 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.
- ops
Analytics OpsQuery Analytics Query Response - Preview: A query used to fetch a time series, category series, or numeric series with SQL. This is a preview feature and may be subject to change before final release.
- output
Full BooleanDuration - Optional. If set, Cloud Monitoring will treat the full query duration as the alignment period so that there will be only 1 output value.*Note: This could override the configured alignment period except for the cases where a series of data points are expected, like - XyChart - Scorecard's spark chart
- prometheus
Query String - A query used to fetch time series with PromQL.
- time
Series TimeFilter Series Filter Response - Filter parameters to fetch time series.
- time
Series TimeFilter Ratio Series Filter Ratio Response - Parameters to fetch a ratio between two time series filters.
- time
Series StringQuery Language - A query used to fetch time series with MQL.
- unit
Override 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.
- ops
Analytics OpsQuery Analytics Query Response - Preview: A query used to fetch a time series, category series, or numeric series with SQL. This is a preview feature and may be subject to change before final release.
- output
Full booleanDuration - Optional. If set, Cloud Monitoring will treat the full query duration as the alignment period so that there will be only 1 output value.*Note: This could override the configured alignment period except for the cases where a series of data points are expected, like - XyChart - Scorecard's spark chart
- prometheus
Query string - A query used to fetch time series with PromQL.
- time
Series TimeFilter Series Filter Response - Filter parameters to fetch time series.
- time
Series TimeFilter Ratio Series Filter Ratio Response - Parameters to fetch a ratio between two time series filters.
- time
Series stringQuery Language - A query used to fetch time series with MQL.
- unit
Override 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.
- ops_
analytics_ Opsquery Analytics Query Response - Preview: A query used to fetch a time series, category series, or numeric series with SQL. This is a preview feature and may be subject to change before final release.
- output_
full_ boolduration - Optional. If set, Cloud Monitoring will treat the full query duration as the alignment period so that there will be only 1 output value.*Note: This could override the configured alignment period except for the cases where a series of data points are expected, like - XyChart - Scorecard's spark chart
- prometheus_
query str - A query used to fetch time series with PromQL.
- time_
series_ Timefilter Series Filter Response - Filter parameters to fetch time series.
- time_
series_ Timefilter_ ratio Series Filter Ratio Response - Parameters to fetch a ratio between two time series filters.
- time_
series_ strquery_ language - 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.
- ops
Analytics Property MapQuery - Preview: A query used to fetch a time series, category series, or numeric series with SQL. This is a preview feature and may be subject to change before final release.
- output
Full BooleanDuration - Optional. If set, Cloud Monitoring will treat the full query duration as the alignment period so that there will be only 1 output value.*Note: This could override the configured alignment period except for the cases where a series of data points are expected, like - XyChart - Scorecard's spark chart
- prometheus
Query String - A query used to fetch time series with PromQL.
- time
Series Property MapFilter - Filter parameters to fetch time series.
- time
Series Property MapFilter Ratio - Parameters to fetch a ratio between two time series filters.
- time
Series StringQuery Language - A query used to fetch time series with MQL.
- unit
Override 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, TimeSeriesTableArgs
- Data
Sets List<Pulumi.Google Native. Monitoring. V1. Inputs. Table Data Set> - The data displayed in this table.
- Column
Settings List<Pulumi.Google Native. Monitoring. V1. Inputs. Column Settings> - Optional. The list of the persistent column settings for the table.
- Metric
Visualization Pulumi.Google Native. Monitoring. V1. Time Series Table Metric Visualization - Optional. Store rendering strategy
- Data
Sets []TableData Set - The data displayed in this table.
- Column
Settings []ColumnSettings - Optional. The list of the persistent column settings for the table.
- Metric
Visualization TimeSeries Table Metric Visualization - Optional. Store rendering strategy
- data
Sets List<TableData Set> - The data displayed in this table.
- column
Settings List<ColumnSettings> - Optional. The list of the persistent column settings for the table.
- metric
Visualization TimeSeries Table Metric Visualization - Optional. Store rendering strategy
- data
Sets TableData Set[] - The data displayed in this table.
- column
Settings ColumnSettings[] - Optional. The list of the persistent column settings for the table.
- metric
Visualization TimeSeries Table Metric Visualization - Optional. Store rendering strategy
- data_
sets Sequence[TableData Set] - The data displayed in this table.
- column_
settings Sequence[ColumnSettings] - Optional. The list of the persistent column settings for the table.
- metric_
visualization TimeSeries Table Metric Visualization - Optional. Store rendering strategy
- data
Sets List<Property Map> - The data displayed in this table.
- column
Settings List<Property Map> - Optional. The list of the persistent column settings for the table.
- metric
Visualization "METRIC_VISUALIZATION_UNSPECIFIED" | "NUMBER" | "BAR" - Optional. Store rendering strategy
TimeSeriesTableMetricVisualization, TimeSeriesTableMetricVisualizationArgs
- Metric
Visualization Unspecified - METRIC_VISUALIZATION_UNSPECIFIEDUnspecified state
- Number
- NUMBERDefault text rendering
- Bar
- BARHorizontal bar rendering
- Time
Series Table Metric Visualization Metric Visualization Unspecified - METRIC_VISUALIZATION_UNSPECIFIEDUnspecified state
- Time
Series Table Metric Visualization Number - NUMBERDefault text rendering
- Time
Series Table Metric Visualization Bar - BARHorizontal bar rendering
- Metric
Visualization Unspecified - METRIC_VISUALIZATION_UNSPECIFIEDUnspecified state
- Number
- NUMBERDefault text rendering
- Bar
- BARHorizontal bar rendering
- Metric
Visualization Unspecified - METRIC_VISUALIZATION_UNSPECIFIEDUnspecified state
- Number
- NUMBERDefault text rendering
- Bar
- BARHorizontal bar rendering
- METRIC_VISUALIZATION_UNSPECIFIED
- METRIC_VISUALIZATION_UNSPECIFIEDUnspecified state
- NUMBER
- NUMBERDefault text rendering
- BAR
- BARHorizontal bar rendering
- "METRIC_VISUALIZATION_UNSPECIFIED"
- METRIC_VISUALIZATION_UNSPECIFIEDUnspecified state
- "NUMBER"
- NUMBERDefault text rendering
- "BAR"
- BARHorizontal bar rendering
TimeSeriesTableResponse, TimeSeriesTableResponseArgs
- Column
Settings List<Pulumi.Google Native. Monitoring. V1. Inputs. Column Settings Response> - Optional. The list of the persistent column settings for the table.
- Data
Sets List<Pulumi.Google Native. Monitoring. V1. Inputs. Table Data Set Response> - The data displayed in this table.
- Metric
Visualization string - Optional. Store rendering strategy
- Column
Settings []ColumnSettings Response - Optional. The list of the persistent column settings for the table.
- Data
Sets []TableData Set Response - The data displayed in this table.
- Metric
Visualization string - Optional. Store rendering strategy
- column
Settings List<ColumnSettings Response> - Optional. The list of the persistent column settings for the table.
- data
Sets List<TableData Set Response> - The data displayed in this table.
- metric
Visualization String - Optional. Store rendering strategy
- column
Settings ColumnSettings Response[] - Optional. The list of the persistent column settings for the table.
- data
Sets TableData Set Response[] - The data displayed in this table.
- metric
Visualization string - Optional. Store rendering strategy
- column_
settings Sequence[ColumnSettings Response] - Optional. The list of the persistent column settings for the table.
- data_
sets Sequence[TableData Set Response] - The data displayed in this table.
- metric_
visualization str - Optional. Store rendering strategy
- column
Settings List<Property Map> - Optional. The list of the persistent column settings for the table.
- data
Sets List<Property Map> - The data displayed in this table.
- metric
Visualization String - Optional. Store rendering strategy
Widget, WidgetArgs
- Alert
Chart Pulumi.Google Native. Monitoring. V1. Inputs. Alert Chart - A chart of alert policy data.
- Blank
Pulumi.
Google Native. Monitoring. V1. Inputs. Empty - A blank space.
- Collapsible
Group Pulumi.Google Native. Monitoring. V1. Inputs. Collapsible Group - A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
- Error
Reporting Pulumi.Panel Google Native. Monitoring. V1. Inputs. Error Reporting Panel - A widget that displays a list of error groups.
- Id string
- Optional. The widget id. Ids may be made up of alphanumerics, dashes and underscores. Widget ids are optional.
- Incident
List Pulumi.Google Native. Monitoring. V1. Inputs. Incident List - A widget that shows list of incidents.
- Logs
Panel Pulumi.Google Native. Monitoring. V1. Inputs. Logs Panel - A widget that shows a stream of logs.
- Pie
Chart Pulumi.Google Native. Monitoring. V1. Inputs. Pie Chart - A widget that displays timeseries data as a pie chart.
- Scorecard
Pulumi.
Google Native. Monitoring. V1. Inputs. Scorecard - A scorecard summarizing time series data.
- Text
Pulumi.
Google Native. Monitoring. V1. Inputs. Text - A raw string or markdown displaying textual content.
- Time
Series Pulumi.Table Google Native. Monitoring. V1. Inputs. Time Series Table - A widget that displays time series data in a tabular format.
- Title string
- Optional. The title of the widget.
- Xy
Chart Pulumi.Google Native. Monitoring. V1. Inputs. Xy Chart - 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.
- Error
Reporting ErrorPanel Reporting Panel - A widget that displays a list of error groups.
- Id string
- Optional. The widget id. Ids may be made up of alphanumerics, dashes and underscores. Widget ids are optional.
- Incident
List IncidentList - A widget that shows list of incidents.
- Logs
Panel LogsPanel - A widget that shows a stream of logs.
- Pie
Chart PieChart - A widget that displays timeseries data as a pie chart.
- Scorecard Scorecard
- A scorecard summarizing time series data.
- Text Text
- A raw string or markdown displaying textual content.
- Time
Series TimeTable Series Table - A widget that displays time series data in a tabular format.
- Title string
- Optional. The title of the widget.
- Xy
Chart 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.
- error
Reporting ErrorPanel Reporting Panel - A widget that displays a list of error groups.
- id String
- Optional. The widget id. Ids may be made up of alphanumerics, dashes and underscores. Widget ids are optional.
- incident
List IncidentList - A widget that shows list of incidents.
- logs
Panel LogsPanel - A widget that shows a stream of logs.
- pie
Chart PieChart - A widget that displays timeseries data as a pie chart.
- scorecard Scorecard
- A scorecard summarizing time series data.
- text Text
- A raw string or markdown displaying textual content.
- time
Series TimeTable Series Table - A widget that displays time series data in a tabular format.
- title String
- Optional. The title of the widget.
- xy
Chart 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.
- error
Reporting ErrorPanel Reporting Panel - A widget that displays a list of error groups.
- id string
- Optional. The widget id. Ids may be made up of alphanumerics, dashes and underscores. Widget ids are optional.
- incident
List IncidentList - A widget that shows list of incidents.
- logs
Panel LogsPanel - A widget that shows a stream of logs.
- pie
Chart PieChart - A widget that displays timeseries data as a pie chart.
- scorecard Scorecard
- A scorecard summarizing time series data.
- text Text
- A raw string or markdown displaying textual content.
- time
Series TimeTable Series Table - A widget that displays time series data in a tabular format.
- title string
- Optional. The title of the widget.
- xy
Chart 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.
- error_
reporting_ Errorpanel Reporting Panel - A widget that displays a list of error groups.
- id str
- Optional. The widget id. Ids may be made up of alphanumerics, dashes and underscores. Widget ids are optional.
- incident_
list IncidentList - A widget that shows list of incidents.
- logs_
panel LogsPanel - A widget that shows a stream of logs.
- pie_
chart PieChart - A widget that displays timeseries data as a pie chart.
- scorecard Scorecard
- A scorecard summarizing time series data.
- text Text
- A raw string or markdown displaying textual content.
- time_
series_ Timetable Series Table - 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.
- alert
Chart Property Map - A chart of alert policy data.
- blank Property Map
- A blank space.
- collapsible
Group 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.
- error
Reporting Property MapPanel - A widget that displays a list of error groups.
- id String
- Optional. The widget id. Ids may be made up of alphanumerics, dashes and underscores. Widget ids are optional.
- incident
List Property Map - A widget that shows list of incidents.
- logs
Panel Property Map - A widget that shows a stream of logs.
- pie
Chart Property Map - A widget that displays timeseries data as a pie chart.
- scorecard Property Map
- A scorecard summarizing time series data.
- text Property Map
- A raw string or markdown displaying textual content.
- time
Series Property MapTable - A widget that displays time series data in a tabular format.
- title String
- Optional. The title of the widget.
- xy
Chart Property Map - A chart of time series data.
WidgetResponse, WidgetResponseArgs
- Alert
Chart Pulumi.Google Native. Monitoring. V1. Inputs. Alert Chart Response - A chart of alert policy data.
- Blank
Pulumi.
Google Native. Monitoring. V1. Inputs. Empty Response - A blank space.
- Collapsible
Group Pulumi.Google Native. Monitoring. V1. Inputs. Collapsible Group Response - A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
- Error
Reporting Pulumi.Panel Google Native. Monitoring. V1. Inputs. Error Reporting Panel Response - A widget that displays a list of error groups.
- Incident
List Pulumi.Google Native. Monitoring. V1. Inputs. Incident List Response - A widget that shows list of incidents.
- Logs
Panel Pulumi.Google Native. Monitoring. V1. Inputs. Logs Panel Response - A widget that shows a stream of logs.
- Pie
Chart Pulumi.Google Native. Monitoring. V1. Inputs. Pie Chart Response - A widget that displays timeseries data as a pie chart.
- Scorecard
Pulumi.
Google Native. Monitoring. V1. Inputs. Scorecard Response - A scorecard summarizing time series data.
- Text
Pulumi.
Google Native. Monitoring. V1. Inputs. Text Response - A raw string or markdown displaying textual content.
- Time
Series Pulumi.Table Google Native. Monitoring. V1. Inputs. Time Series Table Response - A widget that displays time series data in a tabular format.
- Title string
- Optional. The title of the widget.
- Xy
Chart Pulumi.Google Native. Monitoring. V1. Inputs. Xy Chart Response - A chart of time series data.
- Alert
Chart AlertChart Response - A chart of alert policy data.
- Blank
Empty
Response - A blank space.
- Collapsible
Group CollapsibleGroup Response - A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
- Error
Reporting ErrorPanel Reporting Panel Response - A widget that displays a list of error groups.
- Incident
List IncidentList Response - A widget that shows list of incidents.
- Logs
Panel LogsPanel Response - A widget that shows a stream of logs.
- Pie
Chart PieChart Response - A widget that displays timeseries data as a pie chart.
- Scorecard
Scorecard
Response - A scorecard summarizing time series data.
- Text
Text
Response - A raw string or markdown displaying textual content.
- Time
Series TimeTable Series Table Response - A widget that displays time series data in a tabular format.
- Title string
- Optional. The title of the widget.
- Xy
Chart XyChart Response - A chart of time series data.
- alert
Chart AlertChart Response - A chart of alert policy data.
- blank
Empty
Response - A blank space.
- collapsible
Group CollapsibleGroup Response - A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
- error
Reporting ErrorPanel Reporting Panel Response - A widget that displays a list of error groups.
- incident
List IncidentList Response - A widget that shows list of incidents.
- logs
Panel LogsPanel Response - A widget that shows a stream of logs.
- pie
Chart PieChart Response - A widget that displays timeseries data as a pie chart.
- scorecard
Scorecard
Response - A scorecard summarizing time series data.
- text
Text
Response - A raw string or markdown displaying textual content.
- time
Series TimeTable Series Table Response - A widget that displays time series data in a tabular format.
- title String
- Optional. The title of the widget.
- xy
Chart XyChart Response - A chart of time series data.
- alert
Chart AlertChart Response - A chart of alert policy data.
- blank
Empty
Response - A blank space.
- collapsible
Group CollapsibleGroup Response - A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
- error
Reporting ErrorPanel Reporting Panel Response - A widget that displays a list of error groups.
- incident
List IncidentList Response - A widget that shows list of incidents.
- logs
Panel LogsPanel Response - A widget that shows a stream of logs.
- pie
Chart PieChart Response - A widget that displays timeseries data as a pie chart.
- scorecard
Scorecard
Response - A scorecard summarizing time series data.
- text
Text
Response - A raw string or markdown displaying textual content.
- time
Series TimeTable Series Table Response - A widget that displays time series data in a tabular format.
- title string
- Optional. The title of the widget.
- xy
Chart XyChart Response - A chart of time series data.
- alert_
chart AlertChart Response - A chart of alert policy data.
- blank
Empty
Response - A blank space.
- collapsible_
group CollapsibleGroup Response - A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
- error_
reporting_ Errorpanel Reporting Panel Response - A widget that displays a list of error groups.
- incident_
list IncidentList Response - A widget that shows list of incidents.
- logs_
panel LogsPanel Response - A widget that shows a stream of logs.
- pie_
chart PieChart Response - A widget that displays timeseries data as a pie chart.
- scorecard
Scorecard
Response - A scorecard summarizing time series data.
- text
Text
Response - A raw string or markdown displaying textual content.
- time_
series_ Timetable Series Table Response - A widget that displays time series data in a tabular format.
- title str
- Optional. The title of the widget.
- xy_
chart XyChart Response - A chart of time series data.
- alert
Chart Property Map - A chart of alert policy data.
- blank Property Map
- A blank space.
- collapsible
Group 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.
- error
Reporting Property MapPanel - A widget that displays a list of error groups.
- incident
List Property Map - A widget that shows list of incidents.
- logs
Panel Property Map - A widget that shows a stream of logs.
- pie
Chart Property Map - A widget that displays timeseries data as a pie chart.
- scorecard Property Map
- A scorecard summarizing time series data.
- text Property Map
- A raw string or markdown displaying textual content.
- time
Series Property MapTable - A widget that displays time series data in a tabular format.
- title String
- Optional. The title of the widget.
- xy
Chart Property Map - A chart of time series data.
XyChart, XyChartArgs
- Data
Sets List<Pulumi.Google Native. Monitoring. V1. Inputs. Data Set> - The data displayed in this chart.
- Chart
Options Pulumi.Google Native. Monitoring. V1. Inputs. Chart Options - Display options for the chart.
- Thresholds
List<Pulumi.
Google Native. Monitoring. V1. Inputs. Threshold> - Threshold lines drawn horizontally across the chart.
- Timeshift
Duration 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.
Google Native. Monitoring. V1. Inputs. Axis - The properties applied to the x-axis.
- Y2Axis
Pulumi.
Google Native. Monitoring. V1. Inputs. Axis - The properties applied to the y2-axis.
- YAxis
Pulumi.
Google Native. Monitoring. V1. Inputs. Axis - The properties applied to the y-axis.
- Data
Sets []DataSet - The data displayed in this chart.
- Chart
Options ChartOptions - Display options for the chart.
- Thresholds []Threshold
- Threshold lines drawn horizontally across the chart.
- Timeshift
Duration 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 List<DataSet> - The data displayed in this chart.
- chart
Options ChartOptions - Display options for the chart.
- thresholds List<Threshold>
- Threshold lines drawn horizontally across the chart.
- timeshift
Duration 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.
- x
Axis Axis - The properties applied to the x-axis.
- y2Axis Axis
- The properties applied to the y2-axis.
- y
Axis Axis - The properties applied to the y-axis.
- data
Sets DataSet[] - The data displayed in this chart.
- chart
Options ChartOptions - Display options for the chart.
- thresholds Threshold[]
- Threshold lines drawn horizontally across the chart.
- timeshift
Duration 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.
- x
Axis Axis - The properties applied to the x-axis.
- y2Axis Axis
- The properties applied to the y2-axis.
- y
Axis 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.
- data
Sets List<Property Map> - The data displayed in this chart.
- chart
Options Property Map - Display options for the chart.
- thresholds List<Property Map>
- Threshold lines drawn horizontally across the chart.
- timeshift
Duration 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.
- x
Axis Property Map - The properties applied to the x-axis.
- y2Axis Property Map
- The properties applied to the y2-axis.
- y
Axis Property Map - The properties applied to the y-axis.
XyChartResponse, XyChartResponseArgs
- Chart
Options Pulumi.Google Native. Monitoring. V1. Inputs. Chart Options Response - Display options for the chart.
- Data
Sets List<Pulumi.Google Native. Monitoring. V1. Inputs. Data Set Response> - The data displayed in this chart.
- Thresholds
List<Pulumi.
Google Native. Monitoring. V1. Inputs. Threshold Response> - Threshold lines drawn horizontally across the chart.
- Timeshift
Duration 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.
Google Native. Monitoring. V1. Inputs. Axis Response - The properties applied to the x-axis.
- Y2Axis
Pulumi.
Google Native. Monitoring. V1. Inputs. Axis Response - The properties applied to the y2-axis.
- YAxis
Pulumi.
Google Native. Monitoring. V1. Inputs. Axis Response - The properties applied to the y-axis.
- Chart
Options ChartOptions Response - Display options for the chart.
- Data
Sets []DataSet Response - The data displayed in this chart.
- Thresholds
[]Threshold
Response - Threshold lines drawn horizontally across the chart.
- Timeshift
Duration 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
Response - The properties applied to the x-axis.
- Y2Axis
Axis
Response - The properties applied to the y2-axis.
- YAxis
Axis
Response - The properties applied to the y-axis.
- chart
Options ChartOptions Response - Display options for the chart.
- data
Sets List<DataSet Response> - The data displayed in this chart.
- thresholds
List<Threshold
Response> - Threshold lines drawn horizontally across the chart.
- timeshift
Duration 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.
- x
Axis AxisResponse - The properties applied to the x-axis.
- y2Axis
Axis
Response - The properties applied to the y2-axis.
- y
Axis AxisResponse - The properties applied to the y-axis.
- chart
Options ChartOptions Response - Display options for the chart.
- data
Sets DataSet Response[] - The data displayed in this chart.
- thresholds
Threshold
Response[] - Threshold lines drawn horizontally across the chart.
- timeshift
Duration 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.
- x
Axis AxisResponse - The properties applied to the x-axis.
- y2Axis
Axis
Response - The properties applied to the y2-axis.
- y
Axis AxisResponse - The properties applied to the y-axis.
- chart_
options ChartOptions Response - Display options for the chart.
- data_
sets Sequence[DataSet Response] - The data displayed in this chart.
- thresholds
Sequence[Threshold
Response] - 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.
- chart
Options Property Map - Display options for the chart.
- data
Sets List<Property Map> - The data displayed in this chart.
- thresholds List<Property Map>
- Threshold lines drawn horizontally across the chart.
- timeshift
Duration 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.
- x
Axis Property Map - The properties applied to the x-axis.
- y2Axis Property Map
- The properties applied to the y2-axis.
- y
Axis Property Map - The properties applied to the y-axis.
Package Details
- Repository
- Google Cloud Native pulumi/pulumi-google-native
- License
- Apache-2.0
Google Cloud Native is in preview. Google Cloud Classic is fully supported.
Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi