k8s.io/kube-openapi@v0.0.0-20240228011516-70dd3763d340/pkg/schemaconv/testdata/crds/openapiv3/monitoring_v1beta1_monitoringdashboard.json

k8s.io/kube-openapi@v0.0.0-20240228011516-70dd3763d340/pkg/schemaconv/testdata/crds/openapiv3/monitoring_v1beta1_monitoringdashboard.json (about)

1 {"openapi":"3.0.0","info":{"title":"Kubernetes CRD Swagger","version":"v0.1.0"},"components":{"schemas":{"com.google.cloud.cnrm.monitoring.v1beta1.MonitoringDashboard":{"type":"object","required":["spec"],"properties":{"apiVersion":{"description":"APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources","type":"string"},"kind":{"description":"Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds","type":"string"},"metadata":{"description":"Standard object's metadata. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata","allOf":[{"$ref":"#/components/schemas/io.k8s.apimachinery.pkg.apis.meta.v1.ObjectMeta"}]},"spec":{"type":"object","required":["displayName","projectRef"],"properties":{"columnLayout":{"description":"The content is divided into equally spaced columns and the widgets are arranged vertically.","type":"object","properties":{"columns":{"description":"The columns of content to display.","type":"array","items":{"type":"object","properties":{"weight":{"description":"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.","type":"integer","format":"int64"},"widgets":{"description":"The display widgets arranged vertically in this column.","type":"array","items":{"type":"object","properties":{"blank":{"description":"A blank space.","type":"object","x-kubernetes-preserve-unknown-fields":true},"logsPanel":{"type":"object","properties":{"filter":{"description":"A filter that chooses which log entries to return. See [Advanced Logs Queries](https://cloud.google.com/logging/docs/view/advanced-queries). Only log entries that match the filter are returned. An empty filter matches all log entries.","type":"string"},"resourceNames":{"type":"array","items":{"type":"object","oneOf":[{"required":["name"],"not":{"required":["external"]}},{"required":["external"],"not":{"anyOf":[{"required":["name"]},{"required":["namespace"]}]}}],"properties":{"external":{"description":"Allowed value: The Google Cloud resource name of a `Project` resource (format: `projects/{{name}}`).","type":"string"},"name":{"description":"Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names","type":"string"},"namespace":{"description":"Namespace of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/namespaces/","type":"string"}}}}}},"scorecard":{"description":"A scorecard summarizing time series data.","type":"object","required":["timeSeriesQuery"],"properties":{"gaugeView":{"description":"Will cause the scorecard to show a gauge chart.","type":"object","properties":{"lowerBound":{"description":"The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.","type":"number","format":"double"},"upperBound":{"description":"The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.","type":"number","format":"double"}}},"sparkChartView":{"description":"Will cause the scorecard to show a spark chart.","type":"object","required":["sparkChartType"],"properties":{"minAlignmentPeriod":{"description":"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.","type":"string"},"sparkChartType":{"description":"Required. The type of sparkchart to show in this chartView. Possible values: SPARK_CHART_TYPE_UNSPECIFIED, SPARK_LINE, SPARK_BAR","type":"string"}}},"thresholds":{"description":"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.","type":"array","items":{"type":"object","properties":{"color":{"description":"The state color for this threshold. Color is not allowed in a XyChart. Possible values: COLOR_UNSPECIFIED, GREY, BLUE, GREEN, YELLOW, ORANGE, RED","type":"string"},"direction":{"description":"The direction for the current threshold. Direction is not allowed in a XyChart. Possible values: DIRECTION_UNSPECIFIED, ABOVE, BELOW","type":"string"},"label":{"description":"A label for the threshold.","type":"string"},"value":{"description":"The value of the threshold. The value should be defined in the native scale of the metric.","type":"number","format":"double"}}}},"timeSeriesQuery":{"description":"Required. Fields for querying time series data from the Stackdriver metrics API.","type":"object","properties":{"timeSeriesFilter":{"description":"Filter parameters to fetch time series.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"},"pickTimeSeriesFilter":{"description":"Ranking based time series filter.","type":"object","properties":{"direction":{"description":"How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM","type":"string"},"numTimeSeries":{"description":"How many time series to allow to pass through the filter.","type":"integer","format":"int64"},"rankingMethod":{"description":"`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","type":"string"}}},"secondaryAggregation":{"description":"Apply a second aggregation after `aggregation` is applied.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}}}},"timeSeriesFilterRatio":{"description":"Parameters to fetch a ratio between two time series filters.","type":"object","properties":{"denominator":{"description":"The denominator of the ratio.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"}}},"numerator":{"description":"The numerator of the ratio.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"}}},"pickTimeSeriesFilter":{"description":"Ranking based time series filter.","type":"object","properties":{"direction":{"description":"How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM","type":"string"},"numTimeSeries":{"description":"How many time series to allow to pass through the filter.","type":"integer","format":"int64"},"rankingMethod":{"description":"`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","type":"string"}}},"secondaryAggregation":{"description":"Apply a second aggregation after the ratio is computed.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}}}},"timeSeriesQueryLanguage":{"description":"A query used to fetch time series.","type":"string"},"unitOverride":{"description":"The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the [`unit`](https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in `MetricDescriptor`.","type":"string"}}}}},"text":{"description":"A raw string or markdown displaying textual content.","type":"object","properties":{"content":{"description":"The text content to be displayed.","type":"string"},"format":{"description":"How the text content is formatted. Possible values: FORMAT_UNSPECIFIED, MARKDOWN, RAW","type":"string"}}},"title":{"description":"Optional. The title of the widget.","type":"string"},"xyChart":{"description":"A chart of time series data.","type":"object","required":["dataSets"],"properties":{"chartOptions":{"description":"Display options for the chart.","type":"object","properties":{"mode":{"description":"The chart mode. Possible values: MODE_UNSPECIFIED, COLOR, X_RAY, STATS","type":"string"}}},"dataSets":{"description":"Required. The data displayed in this chart.","type":"array","items":{"type":"object","required":["timeSeriesQuery"],"properties":{"legendTemplate":{"description":"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. ","type":"string"},"minAlignmentPeriod":{"description":"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.","type":"string"},"plotType":{"description":"How this data should be plotted on the chart. Possible values: PLOT_TYPE_UNSPECIFIED, LINE, STACKED_AREA, STACKED_BAR, HEATMAP","type":"string"},"timeSeriesQuery":{"description":"Required. Fields for querying time series data from the Stackdriver metrics API.","type":"object","properties":{"timeSeriesFilter":{"description":"Filter parameters to fetch time series.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"},"pickTimeSeriesFilter":{"description":"Ranking based time series filter.","type":"object","properties":{"direction":{"description":"How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM","type":"string"},"numTimeSeries":{"description":"How many time series to allow to pass through the filter.","type":"integer","format":"int64"},"rankingMethod":{"description":"`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","type":"string"}}},"secondaryAggregation":{"description":"Apply a second aggregation after `aggregation` is applied.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}}}},"timeSeriesFilterRatio":{"description":"Parameters to fetch a ratio between two time series filters.","type":"object","properties":{"denominator":{"description":"The denominator of the ratio.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"}}},"numerator":{"description":"The numerator of the ratio.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"}}},"pickTimeSeriesFilter":{"description":"Ranking based time series filter.","type":"object","properties":{"direction":{"description":"How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM","type":"string"},"numTimeSeries":{"description":"How many time series to allow to pass through the filter.","type":"integer","format":"int64"},"rankingMethod":{"description":"`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","type":"string"}}},"secondaryAggregation":{"description":"Apply a second aggregation after the ratio is computed.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}}}},"timeSeriesQueryLanguage":{"description":"A query used to fetch time series.","type":"string"},"unitOverride":{"description":"The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the [`unit`](https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in `MetricDescriptor`.","type":"string"}}}}}},"thresholds":{"description":"Threshold lines drawn horizontally across the chart.","type":"array","items":{"type":"object","properties":{"color":{"description":"The state color for this threshold. Color is not allowed in a XyChart. Possible values: COLOR_UNSPECIFIED, GREY, BLUE, GREEN, YELLOW, ORANGE, RED","type":"string"},"direction":{"description":"The direction for the current threshold. Direction is not allowed in a XyChart. Possible values: DIRECTION_UNSPECIFIED, ABOVE, BELOW","type":"string"},"label":{"description":"A label for the threshold.","type":"string"},"value":{"description":"The value of the threshold. The value should be defined in the native scale of the metric.","type":"number","format":"double"}}}},"timeshiftDuration":{"description":"The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similar-length time periods (e.g., week-over-week metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type.","type":"string"},"xAxis":{"description":"The properties applied to the X axis.","type":"object","properties":{"label":{"description":"The label of the axis.","type":"string"},"scale":{"description":"The axis scale. By default, a linear scale is used. Possible values: SCALE_UNSPECIFIED, LINEAR, LOG10","type":"string"}}},"yAxis":{"description":"The properties applied to the Y axis.","type":"object","properties":{"label":{"description":"The label of the axis.","type":"string"},"scale":{"description":"The axis scale. By default, a linear scale is used. Possible values: SCALE_UNSPECIFIED, LINEAR, LOG10","type":"string"}}}}}}}}}}}}},"displayName":{"description":"Required. The mutable, human-readable name.","type":"string"},"gridLayout":{"description":"Content is arranged with a basic layout that re-flows a simple list of informational elements like widgets or tiles.","type":"object","properties":{"columns":{"description":"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.","type":"integer","format":"int64"},"widgets":{"description":"The informational elements that are arranged into the columns row-first.","type":"array","items":{"type":"object","properties":{"blank":{"description":"A blank space.","type":"object","x-kubernetes-preserve-unknown-fields":true},"logsPanel":{"type":"object","properties":{"filter":{"description":"A filter that chooses which log entries to return. See [Advanced Logs Queries](https://cloud.google.com/logging/docs/view/advanced-queries). Only log entries that match the filter are returned. An empty filter matches all log entries.","type":"string"},"resourceNames":{"type":"array","items":{"type":"object","oneOf":[{"required":["name"],"not":{"required":["external"]}},{"required":["external"],"not":{"anyOf":[{"required":["name"]},{"required":["namespace"]}]}}],"properties":{"external":{"description":"Allowed value: The Google Cloud resource name of a `Project` resource (format: `projects/{{name}}`).","type":"string"},"name":{"description":"Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names","type":"string"},"namespace":{"description":"Namespace of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/namespaces/","type":"string"}}}}}},"scorecard":{"description":"A scorecard summarizing time series data.","type":"object","required":["timeSeriesQuery"],"properties":{"gaugeView":{"description":"Will cause the scorecard to show a gauge chart.","type":"object","properties":{"lowerBound":{"description":"The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.","type":"number","format":"double"},"upperBound":{"description":"The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.","type":"number","format":"double"}}},"sparkChartView":{"description":"Will cause the scorecard to show a spark chart.","type":"object","required":["sparkChartType"],"properties":{"minAlignmentPeriod":{"description":"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.","type":"string"},"sparkChartType":{"description":"Required. The type of sparkchart to show in this chartView. Possible values: SPARK_CHART_TYPE_UNSPECIFIED, SPARK_LINE, SPARK_BAR","type":"string"}}},"thresholds":{"description":"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.","type":"array","items":{"type":"object","properties":{"color":{"description":"The state color for this threshold. Color is not allowed in a XyChart. Possible values: COLOR_UNSPECIFIED, GREY, BLUE, GREEN, YELLOW, ORANGE, RED","type":"string"},"direction":{"description":"The direction for the current threshold. Direction is not allowed in a XyChart. Possible values: DIRECTION_UNSPECIFIED, ABOVE, BELOW","type":"string"},"label":{"description":"A label for the threshold.","type":"string"},"value":{"description":"The value of the threshold. The value should be defined in the native scale of the metric.","type":"number","format":"double"}}}},"timeSeriesQuery":{"description":"Required. Fields for querying time series data from the Stackdriver metrics API.","type":"object","properties":{"timeSeriesFilter":{"description":"Filter parameters to fetch time series.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"},"pickTimeSeriesFilter":{"description":"Ranking based time series filter.","type":"object","properties":{"direction":{"description":"How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM","type":"string"},"numTimeSeries":{"description":"How many time series to allow to pass through the filter.","type":"integer","format":"int64"},"rankingMethod":{"description":"`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","type":"string"}}},"secondaryAggregation":{"description":"Apply a second aggregation after `aggregation` is applied.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}}}},"timeSeriesFilterRatio":{"description":"Parameters to fetch a ratio between two time series filters.","type":"object","properties":{"denominator":{"description":"The denominator of the ratio.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"}}},"numerator":{"description":"The numerator of the ratio.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"}}},"pickTimeSeriesFilter":{"description":"Ranking based time series filter.","type":"object","properties":{"direction":{"description":"How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM","type":"string"},"numTimeSeries":{"description":"How many time series to allow to pass through the filter.","type":"integer","format":"int64"},"rankingMethod":{"description":"`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","type":"string"}}},"secondaryAggregation":{"description":"Apply a second aggregation after the ratio is computed.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}}}},"timeSeriesQueryLanguage":{"description":"A query used to fetch time series.","type":"string"},"unitOverride":{"description":"The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the [`unit`](https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in `MetricDescriptor`.","type":"string"}}}}},"text":{"description":"A raw string or markdown displaying textual content.","type":"object","properties":{"content":{"description":"The text content to be displayed.","type":"string"},"format":{"description":"How the text content is formatted. Possible values: FORMAT_UNSPECIFIED, MARKDOWN, RAW","type":"string"}}},"title":{"description":"Optional. The title of the widget.","type":"string"},"xyChart":{"description":"A chart of time series data.","type":"object","required":["dataSets"],"properties":{"chartOptions":{"description":"Display options for the chart.","type":"object","properties":{"mode":{"description":"The chart mode. Possible values: MODE_UNSPECIFIED, COLOR, X_RAY, STATS","type":"string"}}},"dataSets":{"description":"Required. The data displayed in this chart.","type":"array","items":{"type":"object","required":["timeSeriesQuery"],"properties":{"legendTemplate":{"description":"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. ","type":"string"},"minAlignmentPeriod":{"description":"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.","type":"string"},"plotType":{"description":"How this data should be plotted on the chart. Possible values: PLOT_TYPE_UNSPECIFIED, LINE, STACKED_AREA, STACKED_BAR, HEATMAP","type":"string"},"timeSeriesQuery":{"description":"Required. Fields for querying time series data from the Stackdriver metrics API.","type":"object","properties":{"timeSeriesFilter":{"description":"Filter parameters to fetch time series.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"},"pickTimeSeriesFilter":{"description":"Ranking based time series filter.","type":"object","properties":{"direction":{"description":"How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM","type":"string"},"numTimeSeries":{"description":"How many time series to allow to pass through the filter.","type":"integer","format":"int64"},"rankingMethod":{"description":"`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","type":"string"}}},"secondaryAggregation":{"description":"Apply a second aggregation after `aggregation` is applied.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}}}},"timeSeriesFilterRatio":{"description":"Parameters to fetch a ratio between two time series filters.","type":"object","properties":{"denominator":{"description":"The denominator of the ratio.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"}}},"numerator":{"description":"The numerator of the ratio.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"}}},"pickTimeSeriesFilter":{"description":"Ranking based time series filter.","type":"object","properties":{"direction":{"description":"How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM","type":"string"},"numTimeSeries":{"description":"How many time series to allow to pass through the filter.","type":"integer","format":"int64"},"rankingMethod":{"description":"`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","type":"string"}}},"secondaryAggregation":{"description":"Apply a second aggregation after the ratio is computed.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}}}},"timeSeriesQueryLanguage":{"description":"A query used to fetch time series.","type":"string"},"unitOverride":{"description":"The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the [`unit`](https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in `MetricDescriptor`.","type":"string"}}}}}},"thresholds":{"description":"Threshold lines drawn horizontally across the chart.","type":"array","items":{"type":"object","properties":{"color":{"description":"The state color for this threshold. Color is not allowed in a XyChart. Possible values: COLOR_UNSPECIFIED, GREY, BLUE, GREEN, YELLOW, ORANGE, RED","type":"string"},"direction":{"description":"The direction for the current threshold. Direction is not allowed in a XyChart. Possible values: DIRECTION_UNSPECIFIED, ABOVE, BELOW","type":"string"},"label":{"description":"A label for the threshold.","type":"string"},"value":{"description":"The value of the threshold. The value should be defined in the native scale of the metric.","type":"number","format":"double"}}}},"timeshiftDuration":{"description":"The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similar-length time periods (e.g., week-over-week metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type.","type":"string"},"xAxis":{"description":"The properties applied to the X axis.","type":"object","properties":{"label":{"description":"The label of the axis.","type":"string"},"scale":{"description":"The axis scale. By default, a linear scale is used. Possible values: SCALE_UNSPECIFIED, LINEAR, LOG10","type":"string"}}},"yAxis":{"description":"The properties applied to the Y axis.","type":"object","properties":{"label":{"description":"The label of the axis.","type":"string"},"scale":{"description":"The axis scale. By default, a linear scale is used. Possible values: SCALE_UNSPECIFIED, LINEAR, LOG10","type":"string"}}}}}}}}}},"mosaicLayout":{"description":"The content is arranged as a grid of tiles, with each content widget occupying one or more tiles.","type":"object","properties":{"columns":{"description":"The number of columns in the mosaic grid.","type":"integer","format":"int64"},"tiles":{"description":"The tiles to display.","type":"array","items":{"type":"object","properties":{"height":{"description":"The height of the tile, measured in grid squares.","type":"integer","format":"int64"},"widget":{"description":"The informational widget contained in the tile.","type":"object","properties":{"blank":{"description":"A blank space.","type":"object","x-kubernetes-preserve-unknown-fields":true},"logsPanel":{"type":"object","properties":{"filter":{"description":"A filter that chooses which log entries to return. See [Advanced Logs Queries](https://cloud.google.com/logging/docs/view/advanced-queries). Only log entries that match the filter are returned. An empty filter matches all log entries.","type":"string"},"resourceNames":{"type":"array","items":{"type":"object","oneOf":[{"required":["name"],"not":{"required":["external"]}},{"required":["external"],"not":{"anyOf":[{"required":["name"]},{"required":["namespace"]}]}}],"properties":{"external":{"description":"Allowed value: The Google Cloud resource name of a `Project` resource (format: `projects/{{name}}`).","type":"string"},"name":{"description":"Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names","type":"string"},"namespace":{"description":"Namespace of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/namespaces/","type":"string"}}}}}},"scorecard":{"description":"A scorecard summarizing time series data.","type":"object","required":["timeSeriesQuery"],"properties":{"gaugeView":{"description":"Will cause the scorecard to show a gauge chart.","type":"object","properties":{"lowerBound":{"description":"The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.","type":"number","format":"double"},"upperBound":{"description":"The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.","type":"number","format":"double"}}},"sparkChartView":{"description":"Will cause the scorecard to show a spark chart.","type":"object","required":["sparkChartType"],"properties":{"minAlignmentPeriod":{"description":"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.","type":"string"},"sparkChartType":{"description":"Required. The type of sparkchart to show in this chartView. Possible values: SPARK_CHART_TYPE_UNSPECIFIED, SPARK_LINE, SPARK_BAR","type":"string"}}},"thresholds":{"description":"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.","type":"array","items":{"type":"object","properties":{"color":{"description":"The state color for this threshold. Color is not allowed in a XyChart. Possible values: COLOR_UNSPECIFIED, GREY, BLUE, GREEN, YELLOW, ORANGE, RED","type":"string"},"direction":{"description":"The direction for the current threshold. Direction is not allowed in a XyChart. Possible values: DIRECTION_UNSPECIFIED, ABOVE, BELOW","type":"string"},"label":{"description":"A label for the threshold.","type":"string"},"value":{"description":"The value of the threshold. The value should be defined in the native scale of the metric.","type":"number","format":"double"}}}},"timeSeriesQuery":{"description":"Required. Fields for querying time series data from the Stackdriver metrics API.","type":"object","properties":{"timeSeriesFilter":{"description":"Filter parameters to fetch time series.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"},"pickTimeSeriesFilter":{"description":"Ranking based time series filter.","type":"object","properties":{"direction":{"description":"How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM","type":"string"},"numTimeSeries":{"description":"How many time series to allow to pass through the filter.","type":"integer","format":"int64"},"rankingMethod":{"description":"`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","type":"string"}}},"secondaryAggregation":{"description":"Apply a second aggregation after `aggregation` is applied.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}}}},"timeSeriesFilterRatio":{"description":"Parameters to fetch a ratio between two time series filters.","type":"object","properties":{"denominator":{"description":"The denominator of the ratio.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"}}},"numerator":{"description":"The numerator of the ratio.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"}}},"pickTimeSeriesFilter":{"description":"Ranking based time series filter.","type":"object","properties":{"direction":{"description":"How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM","type":"string"},"numTimeSeries":{"description":"How many time series to allow to pass through the filter.","type":"integer","format":"int64"},"rankingMethod":{"description":"`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","type":"string"}}},"secondaryAggregation":{"description":"Apply a second aggregation after the ratio is computed.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}}}},"timeSeriesQueryLanguage":{"description":"A query used to fetch time series.","type":"string"},"unitOverride":{"description":"The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the [`unit`](https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in `MetricDescriptor`.","type":"string"}}}}},"text":{"description":"A raw string or markdown displaying textual content.","type":"object","properties":{"content":{"description":"The text content to be displayed.","type":"string"},"format":{"description":"How the text content is formatted. Possible values: FORMAT_UNSPECIFIED, MARKDOWN, RAW","type":"string"}}},"title":{"description":"Optional. The title of the widget.","type":"string"},"xyChart":{"description":"A chart of time series data.","type":"object","required":["dataSets"],"properties":{"chartOptions":{"description":"Display options for the chart.","type":"object","properties":{"mode":{"description":"The chart mode. Possible values: MODE_UNSPECIFIED, COLOR, X_RAY, STATS","type":"string"}}},"dataSets":{"description":"Required. The data displayed in this chart.","type":"array","items":{"type":"object","required":["timeSeriesQuery"],"properties":{"legendTemplate":{"description":"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. ","type":"string"},"minAlignmentPeriod":{"description":"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.","type":"string"},"plotType":{"description":"How this data should be plotted on the chart. Possible values: PLOT_TYPE_UNSPECIFIED, LINE, STACKED_AREA, STACKED_BAR, HEATMAP","type":"string"},"timeSeriesQuery":{"description":"Required. Fields for querying time series data from the Stackdriver metrics API.","type":"object","properties":{"timeSeriesFilter":{"description":"Filter parameters to fetch time series.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"},"pickTimeSeriesFilter":{"description":"Ranking based time series filter.","type":"object","properties":{"direction":{"description":"How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM","type":"string"},"numTimeSeries":{"description":"How many time series to allow to pass through the filter.","type":"integer","format":"int64"},"rankingMethod":{"description":"`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","type":"string"}}},"secondaryAggregation":{"description":"Apply a second aggregation after `aggregation` is applied.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}}}},"timeSeriesFilterRatio":{"description":"Parameters to fetch a ratio between two time series filters.","type":"object","properties":{"denominator":{"description":"The denominator of the ratio.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"}}},"numerator":{"description":"The numerator of the ratio.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"}}},"pickTimeSeriesFilter":{"description":"Ranking based time series filter.","type":"object","properties":{"direction":{"description":"How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM","type":"string"},"numTimeSeries":{"description":"How many time series to allow to pass through the filter.","type":"integer","format":"int64"},"rankingMethod":{"description":"`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","type":"string"}}},"secondaryAggregation":{"description":"Apply a second aggregation after the ratio is computed.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}}}},"timeSeriesQueryLanguage":{"description":"A query used to fetch time series.","type":"string"},"unitOverride":{"description":"The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the [`unit`](https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in `MetricDescriptor`.","type":"string"}}}}}},"thresholds":{"description":"Threshold lines drawn horizontally across the chart.","type":"array","items":{"type":"object","properties":{"color":{"description":"The state color for this threshold. Color is not allowed in a XyChart. Possible values: COLOR_UNSPECIFIED, GREY, BLUE, GREEN, YELLOW, ORANGE, RED","type":"string"},"direction":{"description":"The direction for the current threshold. Direction is not allowed in a XyChart. Possible values: DIRECTION_UNSPECIFIED, ABOVE, BELOW","type":"string"},"label":{"description":"A label for the threshold.","type":"string"},"value":{"description":"The value of the threshold. The value should be defined in the native scale of the metric.","type":"number","format":"double"}}}},"timeshiftDuration":{"description":"The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similar-length time periods (e.g., week-over-week metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type.","type":"string"},"xAxis":{"description":"The properties applied to the X axis.","type":"object","properties":{"label":{"description":"The label of the axis.","type":"string"},"scale":{"description":"The axis scale. By default, a linear scale is used. Possible values: SCALE_UNSPECIFIED, LINEAR, LOG10","type":"string"}}},"yAxis":{"description":"The properties applied to the Y axis.","type":"object","properties":{"label":{"description":"The label of the axis.","type":"string"},"scale":{"description":"The axis scale. By default, a linear scale is used. Possible values: SCALE_UNSPECIFIED, LINEAR, LOG10","type":"string"}}}}}}},"width":{"description":"The width of the tile, measured in grid squares.","type":"integer","format":"int64"},"xPos":{"description":"The zero-indexed position of the tile in grid squares relative to the left edge of the grid.","type":"integer","format":"int64"},"yPos":{"description":"The zero-indexed position of the tile in grid squares relative to the top edge of the grid.","type":"integer","format":"int64"}}}}}},"projectRef":{"description":"Immutable. The Project that this resource belongs to.","type":"object","oneOf":[{"required":["name"],"not":{"required":["external"]}},{"required":["external"],"not":{"anyOf":[{"required":["name"]},{"required":["namespace"]}]}}],"properties":{"external":{"description":"The project id of the resource.\n\nAllowed value: The Google Cloud resource name of a `Project` resource (format: `projects/{{name}}`).","type":"string"},"name":{"description":"Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names","type":"string"},"namespace":{"description":"Namespace of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/namespaces/","type":"string"}}},"resourceID":{"description":"Immutable. Optional. The name of the resource. Used for creation and acquisition. When unset, the value of `metadata.name` is used as the default.","type":"string"},"rowLayout":{"description":"The content is divided into equally spaced rows and the widgets are arranged horizontally.","type":"object","properties":{"rows":{"description":"The rows of content to display.","type":"array","items":{"type":"object","properties":{"weight":{"description":"The relative weight of this row. The row weight is used to adjust the height of rows on the screen (relative to peers). Greater the weight, greater the height of the row on the screen. If omitted, a value of 1 is used while rendering.","type":"integer","format":"int64"},"widgets":{"description":"The display widgets arranged horizontally in this row.","type":"array","items":{"type":"object","properties":{"blank":{"description":"A blank space.","type":"object","x-kubernetes-preserve-unknown-fields":true},"logsPanel":{"type":"object","properties":{"filter":{"description":"A filter that chooses which log entries to return. See [Advanced Logs Queries](https://cloud.google.com/logging/docs/view/advanced-queries). Only log entries that match the filter are returned. An empty filter matches all log entries.","type":"string"},"resourceNames":{"type":"array","items":{"type":"object","oneOf":[{"required":["name"],"not":{"required":["external"]}},{"required":["external"],"not":{"anyOf":[{"required":["name"]},{"required":["namespace"]}]}}],"properties":{"external":{"description":"Allowed value: The Google Cloud resource name of a `Project` resource (format: `projects/{{name}}`).","type":"string"},"name":{"description":"Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names","type":"string"},"namespace":{"description":"Namespace of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/namespaces/","type":"string"}}}}}},"scorecard":{"description":"A scorecard summarizing time series data.","type":"object","required":["timeSeriesQuery"],"properties":{"gaugeView":{"description":"Will cause the scorecard to show a gauge chart.","type":"object","properties":{"lowerBound":{"description":"The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.","type":"number","format":"double"},"upperBound":{"description":"The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.","type":"number","format":"double"}}},"sparkChartView":{"description":"Will cause the scorecard to show a spark chart.","type":"object","required":["sparkChartType"],"properties":{"minAlignmentPeriod":{"description":"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.","type":"string"},"sparkChartType":{"description":"Required. The type of sparkchart to show in this chartView. Possible values: SPARK_CHART_TYPE_UNSPECIFIED, SPARK_LINE, SPARK_BAR","type":"string"}}},"thresholds":{"description":"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.","type":"array","items":{"type":"object","properties":{"color":{"description":"The state color for this threshold. Color is not allowed in a XyChart. Possible values: COLOR_UNSPECIFIED, GREY, BLUE, GREEN, YELLOW, ORANGE, RED","type":"string"},"direction":{"description":"The direction for the current threshold. Direction is not allowed in a XyChart. Possible values: DIRECTION_UNSPECIFIED, ABOVE, BELOW","type":"string"},"label":{"description":"A label for the threshold.","type":"string"},"value":{"description":"The value of the threshold. The value should be defined in the native scale of the metric.","type":"number","format":"double"}}}},"timeSeriesQuery":{"description":"Required. Fields for querying time series data from the Stackdriver metrics API.","type":"object","properties":{"timeSeriesFilter":{"description":"Filter parameters to fetch time series.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"},"pickTimeSeriesFilter":{"description":"Ranking based time series filter.","type":"object","properties":{"direction":{"description":"How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM","type":"string"},"numTimeSeries":{"description":"How many time series to allow to pass through the filter.","type":"integer","format":"int64"},"rankingMethod":{"description":"`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","type":"string"}}},"secondaryAggregation":{"description":"Apply a second aggregation after `aggregation` is applied.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}}}},"timeSeriesFilterRatio":{"description":"Parameters to fetch a ratio between two time series filters.","type":"object","properties":{"denominator":{"description":"The denominator of the ratio.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"}}},"numerator":{"description":"The numerator of the ratio.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"}}},"pickTimeSeriesFilter":{"description":"Ranking based time series filter.","type":"object","properties":{"direction":{"description":"How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM","type":"string"},"numTimeSeries":{"description":"How many time series to allow to pass through the filter.","type":"integer","format":"int64"},"rankingMethod":{"description":"`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","type":"string"}}},"secondaryAggregation":{"description":"Apply a second aggregation after the ratio is computed.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}}}},"timeSeriesQueryLanguage":{"description":"A query used to fetch time series.","type":"string"},"unitOverride":{"description":"The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the [`unit`](https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in `MetricDescriptor`.","type":"string"}}}}},"text":{"description":"A raw string or markdown displaying textual content.","type":"object","properties":{"content":{"description":"The text content to be displayed.","type":"string"},"format":{"description":"How the text content is formatted. Possible values: FORMAT_UNSPECIFIED, MARKDOWN, RAW","type":"string"}}},"title":{"description":"Optional. The title of the widget.","type":"string"},"xyChart":{"description":"A chart of time series data.","type":"object","required":["dataSets"],"properties":{"chartOptions":{"description":"Display options for the chart.","type":"object","properties":{"mode":{"description":"The chart mode. Possible values: MODE_UNSPECIFIED, COLOR, X_RAY, STATS","type":"string"}}},"dataSets":{"description":"Required. The data displayed in this chart.","type":"array","items":{"type":"object","required":["timeSeriesQuery"],"properties":{"legendTemplate":{"description":"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. ","type":"string"},"minAlignmentPeriod":{"description":"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.","type":"string"},"plotType":{"description":"How this data should be plotted on the chart. Possible values: PLOT_TYPE_UNSPECIFIED, LINE, STACKED_AREA, STACKED_BAR, HEATMAP","type":"string"},"timeSeriesQuery":{"description":"Required. Fields for querying time series data from the Stackdriver metrics API.","type":"object","properties":{"timeSeriesFilter":{"description":"Filter parameters to fetch time series.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"},"pickTimeSeriesFilter":{"description":"Ranking based time series filter.","type":"object","properties":{"direction":{"description":"How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM","type":"string"},"numTimeSeries":{"description":"How many time series to allow to pass through the filter.","type":"integer","format":"int64"},"rankingMethod":{"description":"`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","type":"string"}}},"secondaryAggregation":{"description":"Apply a second aggregation after `aggregation` is applied.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}}}},"timeSeriesFilterRatio":{"description":"Parameters to fetch a ratio between two time series filters.","type":"object","properties":{"denominator":{"description":"The denominator of the ratio.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"}}},"numerator":{"description":"The numerator of the ratio.","type":"object","required":["filter"],"properties":{"aggregation":{"description":"By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}},"filter":{"description":"Required. The [monitoring filter](https://cloud.google.com/monitoring/api/v3/filters) that identifies the metric types, resources, and projects to query.","type":"string"}}},"pickTimeSeriesFilter":{"description":"Ranking based time series filter.","type":"object","properties":{"direction":{"description":"How to use the ranking to select time series that pass through the filter. Possible values: DIRECTION_UNSPECIFIED, TOP, BOTTOM","type":"string"},"numTimeSeries":{"description":"How many time series to allow to pass through the filter.","type":"integer","format":"int64"},"rankingMethod":{"description":"`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","type":"string"}}},"secondaryAggregation":{"description":"Apply a second aggregation after the ratio is computed.","type":"object","properties":{"alignmentPeriod":{"description":"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 per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than `ALIGN_NONE` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner `ALIGN_NONE` is specified, then this field is ignored.","type":"string"},"crossSeriesReducer":{"description":"The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the `metric_kind` and the `value_type` of the original time series. Reduction can yield a time series with a different `metric_kind` or `value_type` than the input time series. Time series data must first be aligned (see `per_series_aligner`) in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified, and must not be `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an error is returned. 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","type":"string"},"groupByFields":{"description":"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.","type":"array","items":{"type":"string"}},"perSeriesAligner":{"description":"An `Aligner` describes how to bring the data points in a single time series into temporal alignment. Except for `ALIGN_NONE`, all alignments cause all the data points in an `alignment_period` to be mathematically grouped together, resulting in a single data point for each `alignment_period` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the `metric_kind` and `value_type` of the original time series. Alignment can change the `metric_kind` or the `value_type` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If `cross_series_reducer` is specified, then `per_series_aligner` must be specified and not equal to `ALIGN_NONE` and `alignment_period` must be specified; otherwise, an error is returned.","type":"string"}}}}},"timeSeriesQueryLanguage":{"description":"A query used to fetch time series.","type":"string"},"unitOverride":{"description":"The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the [`unit`](https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors) field in `MetricDescriptor`.","type":"string"}}}}}},"thresholds":{"description":"Threshold lines drawn horizontally across the chart.","type":"array","items":{"type":"object","properties":{"color":{"description":"The state color for this threshold. Color is not allowed in a XyChart. Possible values: COLOR_UNSPECIFIED, GREY, BLUE, GREEN, YELLOW, ORANGE, RED","type":"string"},"direction":{"description":"The direction for the current threshold. Direction is not allowed in a XyChart. Possible values: DIRECTION_UNSPECIFIED, ABOVE, BELOW","type":"string"},"label":{"description":"A label for the threshold.","type":"string"},"value":{"description":"The value of the threshold. The value should be defined in the native scale of the metric.","type":"number","format":"double"}}}},"timeshiftDuration":{"description":"The duration used to display a comparison chart. A comparison chart simultaneously shows values from two similar-length time periods (e.g., week-over-week metrics). The duration must be positive, and it can only be applied to charts with data sets of LINE plot type.","type":"string"},"xAxis":{"description":"The properties applied to the X axis.","type":"object","properties":{"label":{"description":"The label of the axis.","type":"string"},"scale":{"description":"The axis scale. By default, a linear scale is used. Possible values: SCALE_UNSPECIFIED, LINEAR, LOG10","type":"string"}}},"yAxis":{"description":"The properties applied to the Y axis.","type":"object","properties":{"label":{"description":"The label of the axis.","type":"string"},"scale":{"description":"The axis scale. By default, a linear scale is used. Possible values: SCALE_UNSPECIFIED, LINEAR, LOG10","type":"string"}}}}}}}}}}}}}}},"status":{"type":"object","properties":{"conditions":{"description":"Conditions represent the latest available observation of the resource's current state.","type":"array","items":{"type":"object","properties":{"lastTransitionTime":{"description":"Last time the condition transitioned from one status to another.","type":"string"},"message":{"description":"Human-readable message indicating details about last transition.","type":"string"},"reason":{"description":"Unique, one-word, CamelCase reason for the condition's last transition.","type":"string"},"status":{"description":"Status is the status of the condition. Can be True, False, Unknown.","type":"string"},"type":{"description":"Type is the type of the condition.","type":"string"}}}},"etag":{"description":"\\`etag\\` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. An \\`etag\\` is returned in the response to \\`GetDashboard\\`, and users are expected to put that etag in the request to \\`UpdateDashboard\\` to ensure that their change will be applied to the same version of the Dashboard configuration. The field should not be passed during dashboard creation.","type":"string"},"observedGeneration":{"description":"ObservedGeneration is the generation of the resource that was most recently observed by the Config Connector controller. If this is equal to metadata.generation, then that means that the current reported status reflects the most recent desired state of the resource.","type":"integer"}}}},"x-kubernetes-group-version-kind":[{"group":"monitoring.cnrm.cloud.google.com","kind":"MonitoringDashboard","version":"v1beta1"}]},"com.google.cloud.cnrm.monitoring.v1beta1.MonitoringDashboardList":{"description":"MonitoringDashboardList is a list of MonitoringDashboard","type":"object","required":["items"],"properties":{"apiVersion":{"description":"APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources","type":"string"},"items":{"description":"List of monitoringdashboards. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md","type":"array","items":{"$ref":"#/components/schemas/com.google.cloud.cnrm.monitoring.v1beta1.MonitoringDashboard"}},"kind":{"description":"Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds","type":"string"},"metadata":{"description":"Standard list metadata. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds","allOf":[{"$ref":"#/components/schemas/io.k8s.apimachinery.pkg.apis.meta.v1.ListMeta"}]}},"x-kubernetes-group-version-kind":[{"group":"monitoring.cnrm.cloud.google.com","kind":"MonitoringDashboardList","version":"v1beta1"}]},"io.k8s.apimachinery.pkg.apis.meta.v1.FieldsV1":{"description":"FieldsV1 stores a set of fields in a data structure like a Trie, in JSON format.\n\nEach key is either a '.' representing the field itself, and will always map to an empty set, or a string representing a sub-field or item. The string will follow one of these four formats: 'f:\u003cname\u003e', where \u003cname\u003e is the name of a field in a struct, or key in a map 'v:\u003cvalue\u003e', where \u003cvalue\u003e is the exact json formatted value of a list item 'i:\u003cindex\u003e', where \u003cindex\u003e is position of a item in a list 'k:\u003ckeys\u003e', where \u003ckeys\u003e is a map of a list item's key fields to their unique values If a key maps to an empty Fields value, the field that key represents is part of the set.\n\nThe exact format is defined in sigs.k8s.io/structured-merge-diff","type":"object"},"io.k8s.apimachinery.pkg.apis.meta.v1.ListMeta":{"description":"ListMeta describes metadata that synthetic resources must have, including lists and various status objects. A resource may have only one of {ObjectMeta, ListMeta}.","type":"object","properties":{"continue":{"description":"continue may be set if the user set a limit on the number of items returned, and indicates that the server has more data available. The value is opaque and may be used to issue another request to the endpoint that served this list to retrieve the next set of available objects. Continuing a consistent list may not be possible if the server configuration has changed or more than a few minutes have passed. The resourceVersion field returned when using this continue value will be identical to the value in the first response, unless you have received this token from an error message.","type":"string"},"remainingItemCount":{"description":"remainingItemCount is the number of subsequent items in the list which are not included in this list response. If the list request contained label or field selectors, then the number of remaining items is unknown and the field will be left unset and omitted during serialization. If the list is complete (either because it is not chunking or because this is the last chunk), then there are no more remaining items and this field will be left unset and omitted during serialization. Servers older than v1.15 do not set this field. The intended use of the remainingItemCount is *estimating* the size of a collection. Clients should not rely on the remainingItemCount to be set or to be exact.","type":"integer","format":"int64"},"resourceVersion":{"description":"String that identifies the server's internal version of this object that can be used by clients to determine when objects have changed. Value must be treated as opaque by clients and passed unmodified back to the server. Populated by the system. Read-only. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#concurrency-control-and-consistency","type":"string"},"selfLink":{"description":"Deprecated: selfLink is a legacy read-only field that is no longer populated by the system.","type":"string"}}},"io.k8s.apimachinery.pkg.apis.meta.v1.ManagedFieldsEntry":{"description":"ManagedFieldsEntry is a workflow-id, a FieldSet and the group version of the resource that the fieldset applies to.","type":"object","properties":{"apiVersion":{"description":"APIVersion defines the version of this resource that this field set applies to. The format is \"group/version\" just like the top-level APIVersion field. It is necessary to track the version of a field set because it cannot be automatically converted.","type":"string"},"fieldsType":{"description":"FieldsType is the discriminator for the different fields format and version. There is currently only one possible value: \"FieldsV1\"","type":"string"},"fieldsV1":{"description":"FieldsV1 holds the first JSON version format as described in the \"FieldsV1\" type.","allOf":[{"$ref":"#/components/schemas/io.k8s.apimachinery.pkg.apis.meta.v1.FieldsV1"}]},"manager":{"description":"Manager is an identifier of the workflow managing these fields.","type":"string"},"operation":{"description":"Operation is the type of operation which lead to this ManagedFieldsEntry being created. The only valid values for this field are 'Apply' and 'Update'.","type":"string"},"subresource":{"description":"Subresource is the name of the subresource used to update that object, or empty string if the object was updated through the main resource. The value of this field is used to distinguish between managers, even if they share the same name. For example, a status update will be distinct from a regular update using the same manager name. Note that the APIVersion field is not related to the Subresource field and it always corresponds to the version of the main resource.","type":"string"},"time":{"description":"Time is the timestamp of when the ManagedFields entry was added. The timestamp will also be updated if a field is added, the manager changes any of the owned fields value or removes a field. The timestamp does not update when a field is removed from the entry because another manager took it over.","allOf":[{"$ref":"#/components/schemas/io.k8s.apimachinery.pkg.apis.meta.v1.Time"}]}}},"io.k8s.apimachinery.pkg.apis.meta.v1.ObjectMeta":{"description":"ObjectMeta is metadata that all persisted resources must have, which includes all objects users must create.","type":"object","properties":{"annotations":{"description":"Annotations is an unstructured key value map stored with a resource that may be set by external tools to store and retrieve arbitrary metadata. They are not queryable and should be preserved when modifying objects. More info: http://kubernetes.io/docs/user-guide/annotations","type":"object","additionalProperties":{"type":"string","default":""}},"creationTimestamp":{"description":"CreationTimestamp is a timestamp representing the server time when this object was created. It is not guaranteed to be set in happens-before order across separate operations. Clients may not set this value. It is represented in RFC3339 form and is in UTC.\n\nPopulated by the system. Read-only. Null for lists. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata","default":{},"allOf":[{"$ref":"#/components/schemas/io.k8s.apimachinery.pkg.apis.meta.v1.Time"}]},"deletionGracePeriodSeconds":{"description":"Number of seconds allowed for this object to gracefully terminate before it will be removed from the system. Only set when deletionTimestamp is also set. May only be shortened. Read-only.","type":"integer","format":"int64"},"deletionTimestamp":{"description":"DeletionTimestamp is RFC 3339 date and time at which this resource will be deleted. This field is set by the server when a graceful deletion is requested by the user, and is not directly settable by a client. The resource is expected to be deleted (no longer visible from resource lists, and not reachable by name) after the time in this field, once the finalizers list is empty. As long as the finalizers list contains items, deletion is blocked. Once the deletionTimestamp is set, this value may not be unset or be set further into the future, although it may be shortened or the resource may be deleted prior to this time. For example, a user may request that a pod is deleted in 30 seconds. The Kubelet will react by sending a graceful termination signal to the containers in the pod. After that 30 seconds, the Kubelet will send a hard termination signal (SIGKILL) to the container and after cleanup, remove the pod from the API. In the presence of network partitions, this object may still exist after this timestamp, until an administrator or automated process can determine the resource is fully terminated. If not set, graceful deletion of the object has not been requested.\n\nPopulated by the system when a graceful deletion is requested. Read-only. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata","allOf":[{"$ref":"#/components/schemas/io.k8s.apimachinery.pkg.apis.meta.v1.Time"}]},"finalizers":{"description":"Must be empty before the object is deleted from the registry. Each entry is an identifier for the responsible component that will remove the entry from the list. If the deletionTimestamp of the object is non-nil, entries in this list can only be removed. Finalizers may be processed and removed in any order. Order is NOT enforced because it introduces significant risk of stuck finalizers. finalizers is a shared field, any actor with permission can reorder it. If the finalizer list is processed in order, then this can lead to a situation in which the component responsible for the first finalizer in the list is waiting for a signal (field value, external system, or other) produced by a component responsible for a finalizer later in the list, resulting in a deadlock. Without enforced ordering finalizers are free to order amongst themselves and are not vulnerable to ordering changes in the list.","type":"array","items":{"type":"string","default":""},"x-kubernetes-patch-strategy":"merge"},"generateName":{"description":"GenerateName is an optional prefix, used by the server, to generate a unique name ONLY IF the Name field has not been provided. If this field is used, the name returned to the client will be different than the name passed. This value will also be combined with a unique suffix. The provided value has the same validation rules as the Name field, and may be truncated by the length of the suffix required to make the value unique on the server.\n\nIf this field is specified and the generated name exists, the server will return a 409.\n\nApplied only if Name is not specified. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#idempotency","type":"string"},"generation":{"description":"A sequence number representing a specific generation of the desired state. Populated by the system. Read-only.","type":"integer","format":"int64"},"labels":{"description":"Map of string keys and values that can be used to organize and categorize (scope and select) objects. May match selectors of replication controllers and services. More info: http://kubernetes.io/docs/user-guide/labels","type":"object","additionalProperties":{"type":"string","default":""}},"managedFields":{"description":"ManagedFields maps workflow-id and version to the set of fields that are managed by that workflow. This is mostly for internal housekeeping, and users typically shouldn't need to set or understand this field. A workflow can be the user's name, a controller's name, or the name of a specific apply path like \"ci-cd\". The set of fields is always in the version that the workflow used when modifying the object.","type":"array","items":{"default":{},"allOf":[{"$ref":"#/components/schemas/io.k8s.apimachinery.pkg.apis.meta.v1.ManagedFieldsEntry"}]}},"name":{"description":"Name must be unique within a namespace. Is required when creating resources, although some resources may allow a client to request the generation of an appropriate name automatically. Name is primarily intended for creation idempotence and configuration definition. Cannot be updated. More info: http://kubernetes.io/docs/user-guide/identifiers#names","type":"string"},"namespace":{"description":"Namespace defines the space within which each name must be unique. An empty namespace is equivalent to the \"default\" namespace, but \"default\" is the canonical representation. Not all objects are required to be scoped to a namespace - the value of this field for those objects will be empty.\n\nMust be a DNS_LABEL. Cannot be updated. More info: http://kubernetes.io/docs/user-guide/namespaces","type":"string"},"ownerReferences":{"description":"List of objects depended by this object. If ALL objects in the list have been deleted, this object will be garbage collected. If this object is managed by a controller, then an entry in this list will point to this controller, with the controller field set to true. There cannot be more than one managing controller.","type":"array","items":{"default":{},"allOf":[{"$ref":"#/components/schemas/io.k8s.apimachinery.pkg.apis.meta.v1.OwnerReference"}]},"x-kubernetes-patch-merge-key":"uid","x-kubernetes-patch-strategy":"merge"},"resourceVersion":{"description":"An opaque value that represents the internal version of this object that can be used by clients to determine when objects have changed. May be used for optimistic concurrency, change detection, and the watch operation on a resource or set of resources. Clients must treat these values as opaque and passed unmodified back to the server. They may only be valid for a particular resource or set of resources.\n\nPopulated by the system. Read-only. Value must be treated as opaque by clients and . More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#concurrency-control-and-consistency","type":"string"},"selfLink":{"description":"Deprecated: selfLink is a legacy read-only field that is no longer populated by the system.","type":"string"},"uid":{"description":"UID is the unique in time and space value for this object. It is typically generated by the server on successful creation of a resource and is not allowed to change on PUT operations.\n\nPopulated by the system. Read-only. More info: http://kubernetes.io/docs/user-guide/identifiers#uids","type":"string"}}},"io.k8s.apimachinery.pkg.apis.meta.v1.OwnerReference":{"description":"OwnerReference contains enough information to let you identify an owning object. An owning object must be in the same namespace as the dependent, or be cluster-scoped, so there is no namespace field.","type":"object","required":["apiVersion","kind","name","uid"],"properties":{"apiVersion":{"description":"API version of the referent.","type":"string","default":""},"blockOwnerDeletion":{"description":"If true, AND if the owner has the \"foregroundDeletion\" finalizer, then the owner cannot be deleted from the key-value store until this reference is removed. See https://kubernetes.io/docs/concepts/architecture/garbage-collection/#foreground-deletion for how the garbage collector interacts with this field and enforces the foreground deletion. Defaults to false. To set this field, a user needs \"delete\" permission of the owner, otherwise 422 (Unprocessable Entity) will be returned.","type":"boolean"},"controller":{"description":"If true, this reference points to the managing controller.","type":"boolean"},"kind":{"description":"Kind of the referent. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds","type":"string","default":""},"name":{"description":"Name of the referent. More info: http://kubernetes.io/docs/user-guide/identifiers#names","type":"string","default":""},"uid":{"description":"UID of the referent. More info: http://kubernetes.io/docs/user-guide/identifiers#uids","type":"string","default":""}},"x-kubernetes-map-type":"atomic"},"io.k8s.apimachinery.pkg.apis.meta.v1.Time":{"description":"Time is a wrapper around time.Time which supports correct marshaling to YAML and JSON. Wrappers are provided for many of the factory methods that the time package offers.","type":"string","format":"date-time"}}}}