Package google.monitoring.dashboard.v1

Index

DashboardsService

Manages Stackdriver dashboards. A dashboard is an arrangement of data display widgets in a specific layout.

CreateDashboard

rpc CreateDashboard(CreateDashboardRequest) returns (Dashboard)

Creates a new custom dashboard.

This method requires the monitoring.dashboards.create permission on the specified project. For more information, see Google Cloud IAM.

Authorization Scopes

Requires one of the following OAuth scopes:

  • https://www.googleapis.com/auth/cloud-platform
  • https://www.googleapis.com/auth/monitoring
  • https://www.googleapis.com/auth/monitoring.write

For more information, see the Authentication Overview.

DeleteDashboard

rpc DeleteDashboard(DeleteDashboardRequest) returns (Empty)

Deletes an existing custom dashboard.

This method requires the monitoring.dashboards.delete permission on the specified dashboard. For more information, see Google Cloud IAM.

Authorization Scopes

Requires one of the following OAuth scopes:

  • https://www.googleapis.com/auth/cloud-platform
  • https://www.googleapis.com/auth/monitoring
  • https://www.googleapis.com/auth/monitoring.write

For more information, see the Authentication Overview.

GetDashboard

rpc GetDashboard(GetDashboardRequest) returns (Dashboard)

Fetches a specific dashboard.

This method requires the monitoring.dashboards.get permission on the specified dashboard. For more information, see Google Cloud IAM.

Authorization Scopes

Requires one of the following OAuth scopes:

  • https://www.googleapis.com/auth/cloud-platform
  • https://www.googleapis.com/auth/monitoring
  • https://www.googleapis.com/auth/monitoring.read

For more information, see the Authentication Overview.

ListDashboards

rpc ListDashboards(ListDashboardsRequest) returns (ListDashboardsResponse)

Lists the existing dashboards.

This method requires the monitoring.dashboards.list permission on the specified project. For more information, see Google Cloud IAM.

Authorization Scopes

Requires one of the following OAuth scopes:

  • https://www.googleapis.com/auth/cloud-platform
  • https://www.googleapis.com/auth/monitoring
  • https://www.googleapis.com/auth/monitoring.read

For more information, see the Authentication Overview.

UpdateDashboard

rpc UpdateDashboard(UpdateDashboardRequest) returns (Dashboard)

Replaces an existing custom dashboard with a new definition.

This method requires the monitoring.dashboards.update permission on the specified dashboard. For more information, see Google Cloud IAM.

Authorization Scopes

Requires one of the following OAuth scopes:

  • https://www.googleapis.com/auth/cloud-platform
  • https://www.googleapis.com/auth/monitoring
  • https://www.googleapis.com/auth/monitoring.write

For more information, see the Authentication Overview.

Aggregation

Describes how to combine multiple time series to provide a different view of the data. Aggregation of time series is done in two steps. First, each time series in the set is aligned to the same time interval boundaries, then the set of time series is optionally reduced in number.

Alignment consists of applying the per_series_aligner operation to each time series after its data has been divided into regular alignment_period time intervals. This process takes all of the data points in an alignment period, applies a mathematical transformation such as averaging, minimum, maximum, delta, etc., and converts them into a single data point per period.

Reduction is when the aligned and transformed time series can optionally be combined, reducing the number of time series through similar mathematical transformations. Reduction involves applying a cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, and applying the reducer to each subset.

The raw time series data can contain a huge amount of information from multiple sources. Alignment and reduction transforms this mass of data into a more manageable and representative collection of data, for example "the 95% latency across the average of all tasks in a cluster". This representative data can be more easily graphed and comprehended, and the individual time series data is still available for later drilldown. For more details, see Aggregating Time Series.

Fields
alignment_period

Duration

The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.

The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.

per_series_aligner

Aligner

An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.

Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.

Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned.

cross_series_reducer

Reducer

The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.

Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.

Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned.

group_by_fields[]

string

The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The cross_series_reducer is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains resource.type. Fields not specified in group_by_fields are aggregated away. If group_by_fields is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If cross_series_reducer is not defined, this field is ignored.

Aligner

The Aligner specifies the operation that will be applied to the data points in each alignment period in a time series. Except for ALIGN_NONE, which specifies that no operation be applied, each alignment operation replaces the set of data values in each alignment period with a single value: the result of applying the operation to the data values. An aligned time series has a single data value at the end of each alignment_period.

An alignment operation can change the data type of the values, too. For example, if you apply a counting operation to boolean values, the data value_type in the original time series is BOOLEAN, but the value_type in the aligned result is INT64.

Enums
ALIGN_NONE No alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
ALIGN_DELTA

Align and convert to DELTA. The output is delta = y1 - y0.

This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.

ALIGN_RATE

Align and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.

This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.

If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.

ALIGN_INTERPOLATE Align by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
ALIGN_NEXT_OLDER Align by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
ALIGN_MIN Align the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
ALIGN_MAX Align the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
ALIGN_MEAN Align the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
ALIGN_COUNT Align the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
ALIGN_SUM Align the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
ALIGN_STDDEV Align the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
ALIGN_COUNT_TRUE Align the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
ALIGN_COUNT_FALSE Align the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
ALIGN_FRACTION_TRUE Align the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range [0.0, 1.0] and has value_type DOUBLE.
ALIGN_PERCENTILE_99 Align the time series by using percentile aggregation. The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
ALIGN_PERCENTILE_95 Align the time series by using percentile aggregation. The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
ALIGN_PERCENTILE_50 Align the time series by using percentile aggregation. The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
ALIGN_PERCENTILE_05 Align the time series by using percentile aggregation. The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
ALIGN_PERCENT_CHANGE

Align and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.

If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.

A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.

Reducer

A Reducer operation describes how to aggregate data points from multiple 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.

Enums
REDUCE_NONE No cross-time series reduction. The output of the Aligner is returned.
REDUCE_MEAN Reduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
REDUCE_MIN Reduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
REDUCE_MAX Reduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
REDUCE_SUM Reduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
REDUCE_STDDEV Reduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
REDUCE_COUNT Reduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
REDUCE_COUNT_TRUE Reduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
REDUCE_COUNT_FALSE Reduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
REDUCE_FRACTION_TRUE Reduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range [0.0, 1.0] and has value_type DOUBLE.
REDUCE_PERCENTILE_99 Reduce by computing the 99th percentile of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
REDUCE_PERCENTILE_95 Reduce by computing the 95th percentile of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
REDUCE_PERCENTILE_50 Reduce by computing the 50th percentile of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
REDUCE_PERCENTILE_05 Reduce by computing the 5th percentile of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.

ChartOptions

Options to control visual rendering of a chart.

Fields
mode

Mode

The chart mode.

Mode

Chart mode options.

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

ColumnLayout

A simplified layout that divides the available space into vertical columns and arranges a set of widgets vertically in each column.

Fields
columns[]

Column

The columns of content to display.

Column

Defines the layout properties and content for a column.

Fields
weight

int64

The relative weight of this column. The column weight is used to adjust the width of columns on the screen (relative to peers). Greater the weight, greater the width of the column on the screen. If omitted, a value of 1 is used while rendering.

widgets[]

Widget

The display widgets arranged vertically in this column.

CreateDashboardRequest

The CreateDashboard request.

Fields
parent

string

Required. The project on which to execute the request. The format is "projects/{project_id_or_number}". The {project_id_or_number} must match the dashboard resource name.

Authorization requires the following Google IAM permission on the specified resource parent:

  • monitoring.dashboards.create
dashboard

Dashboard

Required. The initial dashboard specification.

Dashboard

A Google Stackdriver dashboard. Dashboards define the content and layout of pages in the Stackdriver web application.

Fields
name

string

The resource name of the dashboard.

display_name

string

The mutable, human-readable name.

etag

string

etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. An etag is returned in the response to GetDashboard, and users are expected to put that etag in the request to UpdateDashboard to ensure that their change will be applied to the same version of the Dashboard configuration. The field should not be passed during dashboard creation.

Union field layout. A dashboard's root container element that defines the layout style. layout can be only one of the following:
grid_layout

GridLayout

Content is arranged with a basic layout that re-flows a simple list of informational elements like widgets or tiles.

row_layout

RowLayout

The content is divided into equally spaced rows and the widgets are arranged horizontally.

column_layout

ColumnLayout

The content is divided into equally spaced columns and the widgets are arranged vertically.

DeleteDashboardRequest

The DeleteDashboard request.

Fields
name

string

Required. The resource name of the Dashboard. The format is "projects/{project_id_or_number}/dashboards/{dashboard_id}".

Authorization requires the following Google IAM permission on the specified resource name:

  • monitoring.dashboards.delete

GetDashboardRequest

The GetDashboard request.

Fields
name

string

Required. The resource name of the Dashboard. The format is one of "dashboards/{dashboard_id}" (for system dashboards) or "projects/{project_id_or_number}/dashboards/{dashboard_id}" (for custom dashboards).

Authorization requires the following Google IAM permission on the specified resource name:

  • monitoring.dashboards.get

GridLayout

A basic layout divides the available space into vertical columns of equal width and arranges a list of widgets using a row-first strategy.

Fields
columns

int64

The number of columns into which the view's width is divided. If omitted or set to zero, a system default will be used while rendering.

widgets[]

Widget

The informational elements that are arranged into the columns row-first.

ListDashboardsRequest

The ListDashboards request.

Fields
parent

string

Required. The scope of the dashboards to list. A project scope must be specified in the form of "projects/{project_id_or_number}".

Authorization requires the following Google IAM permission on the specified resource parent:

  • monitoring.dashboards.list
page_size

int32

A positive number that is the maximum number of results to return. If unspecified, a default of 1000 is used.

page_token

string

If this field is not empty then it must contain the nextPageToken value returned by a previous call to this method. Using this field causes the method to return additional results from the previous method call.

ListDashboardsResponse

The ListDashboards request.

Fields
dashboards[]

Dashboard

The list of requested dashboards.

next_page_token

string

If there are more results than have been returned, then this field is set to a non-empty value. To see the additional results, use that value as pageToken in the next call to this method.

PickTimeSeriesFilter

Describes a ranking-based time series filter. Each input time series is ranked with an aligner. The filter will allow up to num_time_series time series to pass through it, selecting them based on the relative ranking.

For example, if ranking_method is METHOD_MEAN,direction is BOTTOM, and num_time_series is 3, then the 3 times series with the lowest mean values will pass through the filter.

Fields
ranking_method

Method

ranking_method is applied to each time series independently to produce the value which will be used to compare the time series to other time series.

num_time_series

int32

How many time series to allow to pass through the filter.

direction

Direction

How to use the ranking to select time series that pass through the filter.

Direction

Describes the ranking directions.

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

Method

The value reducers that can be applied to a PickTimeSeriesFilter.

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

RowLayout

A simplified layout that divides the available space into rows and arranges a set of widgets horizontally in each row.

Fields
rows[]

Row

The rows of content to display.

Row

Defines the layout properties and content for a row.

Fields
weight

int64

The relative weight of this row. The row weight is used to adjust the height of rows on the screen (relative to peers). Greater the weight, greater the height of the row on the screen. If omitted, a value of 1 is used while rendering.

widgets[]

Widget

The display widgets arranged horizontally in this row.

Scorecard

A widget showing the latest value of a metric, and how this value relates to one or more thresholds.

Fields
time_series_query

TimeSeriesQuery

Fields for querying time series data from the Stackdriver metrics API.

thresholds[]

Threshold

The thresholds used to determine the state of the scorecard given the time series' current value. For an actual value x, the scorecard is in a danger state if x is less than or equal to a danger threshold that triggers below, or greater than or equal to a danger threshold that triggers above. Similarly, if x is above/below a warning threshold that triggers above/below, then the scorecard is in a warning state - unless x also puts it in a danger state. (Danger trumps warning.)

As an example, consider a scorecard with the following four thresholds: { value: 90, category: 'DANGER', trigger: 'ABOVE', }, { value: 70, category: 'WARNING', trigger: 'ABOVE', }, { value: 10, category: 'DANGER', trigger: 'BELOW', }, { value: 20, category: 'WARNING', trigger: 'BELOW', }

Then: values less than or equal to 10 would put the scorecard in a DANGER state, values greater than 10 but less than or equal to 20 a WARNING state, values strictly between 20 and 70 an OK state, values greater than or equal to 70 but less than 90 a WARNING state, and values greater than or equal to 90 a DANGER state.

Union field data_view. Defines the optional additional chart shown on the scorecard. If neither is included - then a default scorecard is shown. data_view can be only one of the following:
gauge_view

GaugeView

Will cause the scorecard to show a gauge chart.

spark_chart_view

SparkChartView

Will cause the scorecard to show a spark chart.

GaugeView

A gauge chart shows where the current value sits within a pre-defined range. The upper and lower bounds should define the possible range of values for the scorecard's query (inclusive).

Fields
lower_bound

double

The lower bound for this gauge chart. The value of the chart should always be greater than or equal to this.

upper_bound

double

The upper bound for this gauge chart. The value of the chart should always be less than or equal to this.

SparkChartView

A sparkChart is a small chart suitable for inclusion in a table-cell or inline in text. This message contains the configuration for a sparkChart to show up on a Scorecard, showing recent trends of the scorecard's timeseries.

Fields
spark_chart_type

SparkChartType

The type of sparkchart to show in this chartView.

min_alignment_period

Duration

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

SparkChartType

Defines the possible types of spark chart supported by the Scorecard.

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

Text

A widget that displays textual content.

Fields
content

string

The text content to be displayed.

format

Format

How the text content is formatted.

Format

The format type of the text content.

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

Threshold

Defines a threshold for categorizing time series values.

Fields
label

string

A label for the threshold.

value

double

The value of the threshold. The value should be defined in the native scale of the metric.

color

Color

The state color for this threshold. Color is not allowed in a XyChart.

direction

Direction

The direction for the current threshold. Direction is not allowed in a XyChart.

Color

The color suggests an interpretation to the viewer when actual values cross the threshold. Comments on each color provide UX guidance on how users can be expected to interpret a given state color.

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

Direction

Whether the threshold is considered crossed by an actual value above or below its threshold value.

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

TimeSeriesFilter

A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries method.

Fields
filter

string

Required. The monitoring filter that identifies the metric types, resources, and projects to query.

aggregation

Aggregation

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

pick_time_series_filter

PickTimeSeriesFilter

Ranking based time series filter.

TimeSeriesFilterRatio

A pair of time series filters that define a ratio computation. The output time series is the pair-wise division of each aligned element from the numerator and denominator time series.

Fields
numerator

RatioPart

The numerator of the ratio.

denominator

RatioPart

The denominator of the ratio.

secondary_aggregation

Aggregation

Apply a second aggregation after the ratio is computed.

pick_time_series_filter

PickTimeSeriesFilter

Ranking based time series filter.

RatioPart

Describes a query to build the numerator or denominator of a TimeSeriesFilterRatio.

Fields
filter

string

Required. The monitoring filter that identifies the metric types, resources, and projects to query.

aggregation

Aggregation

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

TimeSeriesQuery

TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API.

Fields
unit_override

string

The unit of data contained in fetched time series. If non-empty, this unit will override any unit that accompanies fetched data. The format is the same as the unit field in MetricDescriptor.

Union field source. Parameters needed to obtain data for the chart. source can be only one of the following:
time_series_filter

TimeSeriesFilter

Filter parameters to fetch time series.

time_series_filter_ratio

TimeSeriesFilterRatio

Parameters to fetch a ratio between two time series filters.

UpdateDashboardRequest

The UpdateDashboard request.

Fields
dashboard

Dashboard

Required. The dashboard that will replace the existing dashboard.

Authorization requires the following Google IAM permission on the specified resource dashboard:

  • monitoring.dashboards.update

Widget

Widget contains a single dashboard component and configuration of how to present the component in the dashboard.

Fields
title

string

Optional. The title of the widget.

Union field content. Content defines the component used to populate the widget. content can be only one of the following:
xy_chart

XyChart

A chart of time series data.

scorecard

Scorecard

A scorecard summarizing time series data.

text

Text

A raw string or markdown displaying textual content.

blank

Empty

A blank space.

XyChart

A chart that displays data on a 2D (X and Y axes) plane.

Fields
data_sets[]

DataSet

The data displayed in this chart.

timeshift_duration

Duration

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.

thresholds[]

Threshold

Threshold lines drawn horizontally across the chart.

x_axis

Axis

The properties applied to the X axis.

y_axis

Axis

The properties applied to the Y axis.

chart_options

ChartOptions

Display options for the chart.

Axis

A chart axis.

Fields
label

string

The label of the axis.

scale

Scale

The axis scale. By default, a linear scale is used.

Scale

Types of scales used in axes.

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

DataSet

Groups a time series query definition with charting options.

Fields
time_series_query

TimeSeriesQuery

Fields for querying time series data from the Stackdriver metrics API.

plot_type

PlotType

How this data should be plotted on the chart.

legend_template

string

A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value.

min_alignment_period

Duration

Optional. The lower bound on data point frequency for this data set, implemented by specifying the minimum alignment period to use in a time series query For example, if the data is published once every 10 minutes, the min_alignment_period should be at least 10 minutes. It would not make sense to fetch and align data at one minute intervals.

PlotType

The types of plotting strategies for data sets.

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