Property  Value 

Google Cloud Service Name  Cloud Monitoring 
Google Cloud Service Documentation  /monitoring/dashboards/ 
Google Cloud REST Resource Name  v3.projects.dashboards 
Google Cloud REST Resource Documentation  monitoring/api/ref_v3/rest/v1/projects.dashboards/ 
Config Connector Resource Short Names  gcpmonitoringdashboard gcpmonitoringdashboards monitoringdashboard 
Config Connector Service Name  monitoring.googleapis.com 
Config Connector Resource Fully Qualified Name  monitoringdashboards.monitoring.cnrm.cloud.google.com 
Can Be Referenced by IAMPolicy/IAMPolicyMember  No 
Custom Resource Definition Properties
Annotations
Fields  

cnrm.cloud.google.com/stateintospec 
Spec
Schema
columnLayout:
columns:
 weight: integer
widgets:
 blank: {}
logsPanel:
filter: string
resourceNames:
 external: string
name: string
namespace: string
scorecard:
gaugeView:
lowerBound: float
upperBound: float
sparkChartView:
minAlignmentPeriod: string
sparkChartType: string
thresholds:
 color: string
direction: string
label: string
value: float
timeSeriesQuery:
timeSeriesFilter:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
pickTimeSeriesFilter:
direction: string
numTimeSeries: integer
rankingMethod: string
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
timeSeriesFilterRatio:
denominator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
numerator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
pickTimeSeriesFilter:
direction: string
numTimeSeries: integer
rankingMethod: string
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
timeSeriesQueryLanguage: string
unitOverride: string
text:
content: string
format: string
title: string
xyChart:
chartOptions:
mode: string
dataSets:
 legendTemplate: string
minAlignmentPeriod: string
plotType: string
timeSeriesQuery:
timeSeriesFilter:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
pickTimeSeriesFilter:
direction: string
numTimeSeries: integer
rankingMethod: string
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
timeSeriesFilterRatio:
denominator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
numerator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
pickTimeSeriesFilter:
direction: string
numTimeSeries: integer
rankingMethod: string
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
timeSeriesQueryLanguage: string
unitOverride: string
thresholds:
 color: string
direction: string
label: string
value: float
timeshiftDuration: string
xAxis:
label: string
scale: string
yAxis:
label: string
scale: string
displayName: string
gridLayout:
columns: integer
widgets:
 blank: {}
logsPanel:
filter: string
resourceNames:
 external: string
name: string
namespace: string
scorecard:
gaugeView:
lowerBound: float
upperBound: float
sparkChartView:
minAlignmentPeriod: string
sparkChartType: string
thresholds:
 color: string
direction: string
label: string
value: float
timeSeriesQuery:
timeSeriesFilter:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
pickTimeSeriesFilter:
direction: string
numTimeSeries: integer
rankingMethod: string
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
timeSeriesFilterRatio:
denominator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
numerator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
pickTimeSeriesFilter:
direction: string
numTimeSeries: integer
rankingMethod: string
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
timeSeriesQueryLanguage: string
unitOverride: string
text:
content: string
format: string
title: string
xyChart:
chartOptions:
mode: string
dataSets:
 legendTemplate: string
minAlignmentPeriod: string
plotType: string
timeSeriesQuery:
timeSeriesFilter:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
pickTimeSeriesFilter:
direction: string
numTimeSeries: integer
rankingMethod: string
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
timeSeriesFilterRatio:
denominator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
numerator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
pickTimeSeriesFilter:
direction: string
numTimeSeries: integer
rankingMethod: string
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
timeSeriesQueryLanguage: string
unitOverride: string
thresholds:
 color: string
direction: string
label: string
value: float
timeshiftDuration: string
xAxis:
label: string
scale: string
yAxis:
label: string
scale: string
mosaicLayout:
columns: integer
tiles:
 height: integer
widget:
blank: {}
logsPanel:
filter: string
resourceNames:
 external: string
name: string
namespace: string
scorecard:
gaugeView:
lowerBound: float
upperBound: float
sparkChartView:
minAlignmentPeriod: string
sparkChartType: string
thresholds:
 color: string
direction: string
label: string
value: float
timeSeriesQuery:
timeSeriesFilter:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
pickTimeSeriesFilter:
direction: string
numTimeSeries: integer
rankingMethod: string
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
timeSeriesFilterRatio:
denominator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
numerator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
pickTimeSeriesFilter:
direction: string
numTimeSeries: integer
rankingMethod: string
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
timeSeriesQueryLanguage: string
unitOverride: string
text:
content: string
format: string
title: string
xyChart:
chartOptions:
mode: string
dataSets:
 legendTemplate: string
minAlignmentPeriod: string
plotType: string
timeSeriesQuery:
timeSeriesFilter:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
pickTimeSeriesFilter:
direction: string
numTimeSeries: integer
rankingMethod: string
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
timeSeriesFilterRatio:
denominator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
numerator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
pickTimeSeriesFilter:
direction: string
numTimeSeries: integer
rankingMethod: string
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
timeSeriesQueryLanguage: string
unitOverride: string
thresholds:
 color: string
direction: string
label: string
value: float
timeshiftDuration: string
xAxis:
label: string
scale: string
yAxis:
label: string
scale: string
width: integer
xPos: integer
yPos: integer
projectRef:
external: string
name: string
namespace: string
resourceID: string
rowLayout:
rows:
 weight: integer
widgets:
 blank: {}
logsPanel:
filter: string
resourceNames:
 external: string
name: string
namespace: string
scorecard:
gaugeView:
lowerBound: float
upperBound: float
sparkChartView:
minAlignmentPeriod: string
sparkChartType: string
thresholds:
 color: string
direction: string
label: string
value: float
timeSeriesQuery:
timeSeriesFilter:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
pickTimeSeriesFilter:
direction: string
numTimeSeries: integer
rankingMethod: string
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
timeSeriesFilterRatio:
denominator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
numerator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
pickTimeSeriesFilter:
direction: string
numTimeSeries: integer
rankingMethod: string
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
timeSeriesQueryLanguage: string
unitOverride: string
text:
content: string
format: string
title: string
xyChart:
chartOptions:
mode: string
dataSets:
 legendTemplate: string
minAlignmentPeriod: string
plotType: string
timeSeriesQuery:
timeSeriesFilter:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
pickTimeSeriesFilter:
direction: string
numTimeSeries: integer
rankingMethod: string
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
timeSeriesFilterRatio:
denominator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
numerator:
aggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
filter: string
pickTimeSeriesFilter:
direction: string
numTimeSeries: integer
rankingMethod: string
secondaryAggregation:
alignmentPeriod: string
crossSeriesReducer: string
groupByFields:
 string
perSeriesAligner: string
timeSeriesQueryLanguage: string
unitOverride: string
thresholds:
 color: string
direction: string
label: string
value: float
timeshiftDuration: string
xAxis:
label: string
scale: string
yAxis:
label: string
scale: string
Fields  

Optional 
The content is divided into equally spaced columns and the widgets are arranged vertically. 
Optional 
The columns of content to display. 
Optional 

Optional 
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. 
Optional 
The display widgets arranged vertically in this column. 
Optional 

Optional 
A blank space. 
Optional 

Optional 
A filter that chooses which log entries to return. See [Advanced Logs Queries](https://cloud.google.com/logging/docs/view/advancedqueries). Only log entries that match the filter are returned. An empty filter matches all log entries. 
Optional 

Optional 

Optional 
Allowed value: The Google Cloud resource name of a `Project` resource (format: `projects/{{name}}`). 
Optional 
Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/workingwithobjects/names/#names 
Optional 
Namespace of the referent. More info: https://kubernetes.io/docs/concepts/overview/workingwithobjects/namespaces/ 
Optional 
A scorecard summarizing time series data. 
Optional 
Will cause the scorecard to show a gauge chart. 
Optional 
The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this. 
Optional 
The upper bound for this gauge chart. The value of the chart should always be less than or equal to this. 
Optional 
Will cause the scorecard to show a spark chart. 
Optional 
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. 
Required* 
Required. The type of sparkchart to show in this chartView. Possible values: SPARK_CHART_TYPE_UNSPECIFIED, SPARK_LINE, SPARK_BAR 
Optional 
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. 
Optional 

Optional 
The state color for this threshold. Color is not allowed in a XyChart. Possible values: COLOR_UNSPECIFIED, GREY, BLUE, GREEN, YELLOW, ORANGE, RED 
Optional 
The direction for the current threshold. Direction is not allowed in a XyChart. Possible values: DIRECTION_UNSPECIFIED, ABOVE, BELOW 
Optional 
A label for the threshold. 
Optional 
The value of the threshold. The value should be defined in the native scale of the metric. 
Required* 
Required. Fields for querying time series data from the Stackdriver metrics API. 
Optional 
Filter parameters to fetch time series. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
Ranking based time series filter. 
Optional 
How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM 
Optional 
How many time series to allow to pass through the filter. 
Optional 
`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. Possible values: METHOD_UNSPECIFIED, METHOD_MEAN, METHOD_MAX, METHOD_MIN, METHOD_SUM, METHOD_LATEST 
Optional 
Apply a second aggregation after `aggregation` is applied. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Optional 
Parameters to fetch a ratio between two time series filters. 
Optional 
The denominator of the ratio. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
The numerator of the ratio. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
Ranking based time series filter. 
Optional 
How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM 
Optional 
How many time series to allow to pass through the filter. 
Optional 
`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. Possible values: METHOD_UNSPECIFIED, METHOD_MEAN, METHOD_MAX, METHOD_MIN, METHOD_SUM, METHOD_LATEST 
Optional 
Apply a second aggregation after the ratio is computed. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Optional 
A query used to fetch time series. 
Optional 
The unit of data contained in fetched time series. If nonempty, 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`. 
Optional 
A raw string or markdown displaying textual content. 
Optional 
The text content to be displayed. 
Optional 
How the text content is formatted. Possible values: FORMAT_UNSPECIFIED, MARKDOWN, RAW 
Optional 
Optional. The title of the widget. 
Optional 
A chart of time series data. 
Optional 
Display options for the chart. 
Optional 
The chart mode. Possible values: MODE_UNSPECIFIED, COLOR, X_RAY, STATS 
Required* 
Required. The data displayed in this chart. 
Required* 

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. 
Optional 
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. 
Optional 
How this data should be plotted on the chart. Possible values: PLOT_TYPE_UNSPECIFIED, LINE, STACKED_AREA, STACKED_BAR, HEATMAP 
Required* 
Required. Fields for querying time series data from the Stackdriver metrics API. 
Optional 
Filter parameters to fetch time series. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
Ranking based time series filter. 
Optional 
How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM 
Optional 
How many time series to allow to pass through the filter. 
Optional 
`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. Possible values: METHOD_UNSPECIFIED, METHOD_MEAN, METHOD_MAX, METHOD_MIN, METHOD_SUM, METHOD_LATEST 
Optional 
Apply a second aggregation after `aggregation` is applied. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Optional 
Parameters to fetch a ratio between two time series filters. 
Optional 
The denominator of the ratio. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
The numerator of the ratio. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
Ranking based time series filter. 
Optional 
How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM 
Optional 
How many time series to allow to pass through the filter. 
Optional 
`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. Possible values: METHOD_UNSPECIFIED, METHOD_MEAN, METHOD_MAX, METHOD_MIN, METHOD_SUM, METHOD_LATEST 
Optional 
Apply a second aggregation after the ratio is computed. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Optional 
A query used to fetch time series. 
Optional 
The unit of data contained in fetched time series. If nonempty, 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`. 
Optional 
Threshold lines drawn horizontally across the chart. 
Optional 

Optional 
The state color for this threshold. Color is not allowed in a XyChart. Possible values: COLOR_UNSPECIFIED, GREY, BLUE, GREEN, YELLOW, ORANGE, RED 
Optional 
The direction for the current threshold. Direction is not allowed in a XyChart. Possible values: DIRECTION_UNSPECIFIED, ABOVE, BELOW 
Optional 
A label for the threshold. 
Optional 
The value of the threshold. The value should be defined in the native scale of the metric. 
Optional 
The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similarlength time periods (e.g., weekoverweek metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type. 
Optional 
The properties applied to the X axis. 
Optional 
The label of the axis. 
Optional 
The axis scale. By default, a linear scale is used. Possible values: SCALE_UNSPECIFIED, LINEAR, LOG10 
Optional 
The properties applied to the Y axis. 
Optional 
The label of the axis. 
Optional 
The axis scale. By default, a linear scale is used. Possible values: SCALE_UNSPECIFIED, LINEAR, LOG10 
Required 
Required. The mutable, humanreadable name. 
Optional 
Content is arranged with a basic layout that reflows a simple list of informational elements like widgets or tiles. 
Optional 
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. 
Optional 
The informational elements that are arranged into the columns rowfirst. 
Optional 

Optional 
A blank space. 
Optional 

Optional 
A filter that chooses which log entries to return. See [Advanced Logs Queries](https://cloud.google.com/logging/docs/view/advancedqueries). Only log entries that match the filter are returned. An empty filter matches all log entries. 
Optional 

Optional 

Optional 
Allowed value: The Google Cloud resource name of a `Project` resource (format: `projects/{{name}}`). 
Optional 
Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/workingwithobjects/names/#names 
Optional 
Namespace of the referent. More info: https://kubernetes.io/docs/concepts/overview/workingwithobjects/namespaces/ 
Optional 
A scorecard summarizing time series data. 
Optional 
Will cause the scorecard to show a gauge chart. 
Optional 
The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this. 
Optional 
The upper bound for this gauge chart. The value of the chart should always be less than or equal to this. 
Optional 
Will cause the scorecard to show a spark chart. 
Optional 
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. 
Required* 
Required. The type of sparkchart to show in this chartView. Possible values: SPARK_CHART_TYPE_UNSPECIFIED, SPARK_LINE, SPARK_BAR 
Optional 
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. 
Optional 

Optional 
The state color for this threshold. Color is not allowed in a XyChart. Possible values: COLOR_UNSPECIFIED, GREY, BLUE, GREEN, YELLOW, ORANGE, RED 
Optional 
The direction for the current threshold. Direction is not allowed in a XyChart. Possible values: DIRECTION_UNSPECIFIED, ABOVE, BELOW 
Optional 
A label for the threshold. 
Optional 
The value of the threshold. The value should be defined in the native scale of the metric. 
Required* 
Required. Fields for querying time series data from the Stackdriver metrics API. 
Optional 
Filter parameters to fetch time series. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
Ranking based time series filter. 
Optional 
How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM 
Optional 
How many time series to allow to pass through the filter. 
Optional 
`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. Possible values: METHOD_UNSPECIFIED, METHOD_MEAN, METHOD_MAX, METHOD_MIN, METHOD_SUM, METHOD_LATEST 
Optional 
Apply a second aggregation after `aggregation` is applied. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Optional 
Parameters to fetch a ratio between two time series filters. 
Optional 
The denominator of the ratio. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
The numerator of the ratio. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
Ranking based time series filter. 
Optional 
How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM 
Optional 
How many time series to allow to pass through the filter. 
Optional 
`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. Possible values: METHOD_UNSPECIFIED, METHOD_MEAN, METHOD_MAX, METHOD_MIN, METHOD_SUM, METHOD_LATEST 
Optional 
Apply a second aggregation after the ratio is computed. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Optional 
A query used to fetch time series. 
Optional 
The unit of data contained in fetched time series. If nonempty, 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`. 
Optional 
A raw string or markdown displaying textual content. 
Optional 
The text content to be displayed. 
Optional 
How the text content is formatted. Possible values: FORMAT_UNSPECIFIED, MARKDOWN, RAW 
Optional 
Optional. The title of the widget. 
Optional 
A chart of time series data. 
Optional 
Display options for the chart. 
Optional 
The chart mode. Possible values: MODE_UNSPECIFIED, COLOR, X_RAY, STATS 
Required* 
Required. The data displayed in this chart. 
Required* 

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. 
Optional 
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. 
Optional 
How this data should be plotted on the chart. Possible values: PLOT_TYPE_UNSPECIFIED, LINE, STACKED_AREA, STACKED_BAR, HEATMAP 
Required* 
Required. Fields for querying time series data from the Stackdriver metrics API. 
Optional 
Filter parameters to fetch time series. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
Ranking based time series filter. 
Optional 
How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM 
Optional 
How many time series to allow to pass through the filter. 
Optional 
`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. Possible values: METHOD_UNSPECIFIED, METHOD_MEAN, METHOD_MAX, METHOD_MIN, METHOD_SUM, METHOD_LATEST 
Optional 
Apply a second aggregation after `aggregation` is applied. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Optional 
Parameters to fetch a ratio between two time series filters. 
Optional 
The denominator of the ratio. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
The numerator of the ratio. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
Ranking based time series filter. 
Optional 
How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM 
Optional 
How many time series to allow to pass through the filter. 
Optional 
`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. Possible values: METHOD_UNSPECIFIED, METHOD_MEAN, METHOD_MAX, METHOD_MIN, METHOD_SUM, METHOD_LATEST 
Optional 
Apply a second aggregation after the ratio is computed. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Optional 
A query used to fetch time series. 
Optional 
The unit of data contained in fetched time series. If nonempty, 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`. 
Optional 
Threshold lines drawn horizontally across the chart. 
Optional 

Optional 
The state color for this threshold. Color is not allowed in a XyChart. Possible values: COLOR_UNSPECIFIED, GREY, BLUE, GREEN, YELLOW, ORANGE, RED 
Optional 
The direction for the current threshold. Direction is not allowed in a XyChart. Possible values: DIRECTION_UNSPECIFIED, ABOVE, BELOW 
Optional 
A label for the threshold. 
Optional 
The value of the threshold. The value should be defined in the native scale of the metric. 
Optional 
The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similarlength time periods (e.g., weekoverweek metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type. 
Optional 
The properties applied to the X axis. 
Optional 
The label of the axis. 
Optional 
The axis scale. By default, a linear scale is used. Possible values: SCALE_UNSPECIFIED, LINEAR, LOG10 
Optional 
The properties applied to the Y axis. 
Optional 
The label of the axis. 
Optional 
The axis scale. By default, a linear scale is used. Possible values: SCALE_UNSPECIFIED, LINEAR, LOG10 
Optional 
The content is arranged as a grid of tiles, with each content widget occupying one or more tiles. 
Optional 
The number of columns in the mosaic grid. 
Optional 
The tiles to display. 
Optional 

Optional 
The height of the tile, measured in grid squares. 
Optional 
The informational widget contained in the tile. 
Optional 
A blank space. 
Optional 

Optional 
A filter that chooses which log entries to return. See [Advanced Logs Queries](https://cloud.google.com/logging/docs/view/advancedqueries). Only log entries that match the filter are returned. An empty filter matches all log entries. 
Optional 

Optional 

Optional 
Allowed value: The Google Cloud resource name of a `Project` resource (format: `projects/{{name}}`). 
Optional 
Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/workingwithobjects/names/#names 
Optional 
Namespace of the referent. More info: https://kubernetes.io/docs/concepts/overview/workingwithobjects/namespaces/ 
Optional 
A scorecard summarizing time series data. 
Optional 
Will cause the scorecard to show a gauge chart. 
Optional 
The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this. 
Optional 
The upper bound for this gauge chart. The value of the chart should always be less than or equal to this. 
Optional 
Will cause the scorecard to show a spark chart. 
Optional 
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. 
Required* 
Required. The type of sparkchart to show in this chartView. Possible values: SPARK_CHART_TYPE_UNSPECIFIED, SPARK_LINE, SPARK_BAR 
Optional 
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. 
Optional 

Optional 
The state color for this threshold. Color is not allowed in a XyChart. Possible values: COLOR_UNSPECIFIED, GREY, BLUE, GREEN, YELLOW, ORANGE, RED 
Optional 
The direction for the current threshold. Direction is not allowed in a XyChart. Possible values: DIRECTION_UNSPECIFIED, ABOVE, BELOW 
Optional 
A label for the threshold. 
Optional 
The value of the threshold. The value should be defined in the native scale of the metric. 
Required* 
Required. Fields for querying time series data from the Stackdriver metrics API. 
Optional 
Filter parameters to fetch time series. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
Ranking based time series filter. 
Optional 
How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM 
Optional 
How many time series to allow to pass through the filter. 
Optional 
`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. Possible values: METHOD_UNSPECIFIED, METHOD_MEAN, METHOD_MAX, METHOD_MIN, METHOD_SUM, METHOD_LATEST 
Optional 
Apply a second aggregation after `aggregation` is applied. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Optional 
Parameters to fetch a ratio between two time series filters. 
Optional 
The denominator of the ratio. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
The numerator of the ratio. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
Ranking based time series filter. 
Optional 
How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM 
Optional 
How many time series to allow to pass through the filter. 
Optional 
`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. Possible values: METHOD_UNSPECIFIED, METHOD_MEAN, METHOD_MAX, METHOD_MIN, METHOD_SUM, METHOD_LATEST 
Optional 
Apply a second aggregation after the ratio is computed. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Optional 
A query used to fetch time series. 
Optional 
The unit of data contained in fetched time series. If nonempty, 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`. 
Optional 
A raw string or markdown displaying textual content. 
Optional 
The text content to be displayed. 
Optional 
How the text content is formatted. Possible values: FORMAT_UNSPECIFIED, MARKDOWN, RAW 
Optional 
Optional. The title of the widget. 
Optional 
A chart of time series data. 
Optional 
Display options for the chart. 
Optional 
The chart mode. Possible values: MODE_UNSPECIFIED, COLOR, X_RAY, STATS 
Required* 
Required. The data displayed in this chart. 
Required* 

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. 
Optional 
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. 
Optional 
How this data should be plotted on the chart. Possible values: PLOT_TYPE_UNSPECIFIED, LINE, STACKED_AREA, STACKED_BAR, HEATMAP 
Required* 
Required. Fields for querying time series data from the Stackdriver metrics API. 
Optional 
Filter parameters to fetch time series. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
Ranking based time series filter. 
Optional 
How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM 
Optional 
How many time series to allow to pass through the filter. 
Optional 
`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. Possible values: METHOD_UNSPECIFIED, METHOD_MEAN, METHOD_MAX, METHOD_MIN, METHOD_SUM, METHOD_LATEST 
Optional 
Apply a second aggregation after `aggregation` is applied. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Optional 
Parameters to fetch a ratio between two time series filters. 
Optional 
The denominator of the ratio. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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. 
Optional 

Optional 
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 crosstime 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. 
Required* 
Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query. 
Optional 
The numerator of the ratio. 
Optional 
By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. 
Optional 
The `alignment_period` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the perseries aligner can be applied to the data. The value must be at least 60 seconds. If a perseries aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no perseries aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored. 
Optional 
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 crosstime 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. Possible values: 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, REDUCE_FRACTION_LESS_THAN, REDUCE_MAKE_DISTRIBUTION 
Optional 
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 `g 