Summary of entries of Classes for monitoring-dashboards.
Classes
DashboardsServiceAsyncClient
Manages Stackdriver dashboards. A dashboard is an arrangement of data display widgets in a specific layout.
DashboardsServiceClient
Manages Stackdriver dashboards. A dashboard is an arrangement of data display widgets in a specific layout.
ListDashboardsAsyncPager
A pager for iterating through list_dashboards
requests.
This class thinly wraps an initial
ListDashboardsResponse object, and
provides an __aiter__
method to iterate through its
dashboards
field.
If there are more pages, the __aiter__
method will make additional
ListDashboards
requests and continue to iterate
through the dashboards
field on the
corresponding responses.
All the usual ListDashboardsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.
ListDashboardsPager
A pager for iterating through list_dashboards
requests.
This class thinly wraps an initial
ListDashboardsResponse object, and
provides an __iter__
method to iterate through its
dashboards
field.
If there are more pages, the __iter__
method will make additional
ListDashboards
requests and continue to iterate
through the dashboards
field on the
corresponding responses.
All the usual ListDashboardsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.
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 Filtering and
aggregation <https://cloud.google.com/monitoring/api/v3/aggregation>
__.
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
.
This alignment is valid for
`CUMULATIVE][google.api.MetricDescriptor.MetricKind.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 (2):
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 (3):
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 (4):
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 (10):
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 (11):
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 (12):
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 (13):
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 (14):
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 (15):
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 (16):
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 (24):
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 (17):
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 (18):
Align the time series by using `percentile
aggregation <https://en.wikipedia.org/wiki/Percentile>`__.
The resulting data point in each alignment period is the
99th percentile of all data points in the period. This
aligner is valid for `GAUGE` and `DELTA` metrics with
distribution values. The output is a `GAUGE` metric with
`value_type` `DOUBLE`.
ALIGN_PERCENTILE_95 (19):
Align the time series by using `percentile
aggregation <https://en.wikipedia.org/wiki/Percentile>`__.
The resulting data point in each alignment period is the
95th percentile of all data points in the period. This
aligner is valid for `GAUGE` and `DELTA` metrics with
distribution values. The output is a `GAUGE` metric with
`value_type` `DOUBLE`.
ALIGN_PERCENTILE_50 (20):
Align the time series by using `percentile
aggregation <https://en.wikipedia.org/wiki/Percentile>`__.
The resulting data point in each alignment period is the
50th percentile of all data points in the period. This
aligner is valid for `GAUGE` and `DELTA` metrics with
distribution values. The output is a `GAUGE` metric with
`value_type` `DOUBLE`.
ALIGN_PERCENTILE_05 (21):
Align the time series by using `percentile
aggregation <https://en.wikipedia.org/wiki/Percentile>`__.
The resulting data point in each alignment period is the 5th
percentile of all data points in the period. This aligner is
valid for `GAUGE` and `DELTA` metrics with distribution
values. The output is a `GAUGE` metric with `value_type`
`DOUBLE`.
ALIGN_PERCENT_CHANGE (23):
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.
AlertChart
A chart that displays alert policy data.
ChartOptions
Options to control visual rendering of a chart.
Mode
Chart mode options.
CollapsibleGroup
A widget that groups the other widgets. All widgets that are within the area spanned by the grouping widget are considered member widgets.
ColumnLayout
A simplified layout that divides the available space into vertical columns and arranges a set of widgets vertically in each column.
Column
Defines the layout properties and content for a column.
CreateDashboardRequest
The CreateDashboard
request.
Dashboard
A Google Stackdriver dashboard. Dashboards define the content and layout of pages in the Stackdriver web application.
This message has oneof
_ fields (mutually exclusive fields).
For each oneof, at most one member field can be set at the same time.
Setting any member of the oneof automatically clears all other
members.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
LabelsEntry
The abstract base class for a message.
DashboardFilter
A filter to reduce the amount of data charted in relevant widgets.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
FilterType
The type for the dashboard filter
DeleteDashboardRequest
The DeleteDashboard
request.
ErrorReportingPanel
A widget that displays a list of error groups.
GetDashboardRequest
The GetDashboard
request.
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.
IncidentList
A widget that displays a list of incidents
ListDashboardsRequest
The ListDashboards
request.
ListDashboardsResponse
The ListDashboards
request.
LogsPanel
A widget that displays a stream of log.
MosaicLayout
A mosaic layout divides the available space into a grid of blocks,
and overlays the grid with tiles. Unlike GridLayout
, tiles may
span multiple grid blocks and can be placed at arbitrary locations
in the grid.
Tile
A single tile in the mosaic. The placement and size of the tile are configurable.
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.
Direction
Describes the ranking directions.
Method
The value reducers that can be applied to a
PickTimeSeriesFilter
.
PieChart
A widget that displays timeseries data as a pie or a donut.
PieChartDataSet
Groups a time series query definition.
PieChartType
Types for the pie chart.
RowLayout
A simplified layout that divides the available space into rows and arranges a set of widgets horizontally in each row.
Row
Defines the layout properties and content for a row.
Scorecard
A widget showing the latest value of a metric, and how this value relates to one or more thresholds.
This message has oneof
_ fields (mutually exclusive fields).
For each oneof, at most one member field can be set at the same time.
Setting any member of the oneof automatically clears all other
members.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
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).
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.
SectionHeader
A widget that defines a new section header. Sections populate a table of contents and allow easier navigation of long-form content.
SingleViewGroup
A widget that groups the other widgets by using a dropdown menu. All widgets that are within the area spanned by the grouping widget are considered member widgets.
SparkChartType
Defines the possible types of spark chart supported by the
Scorecard
.
StatisticalTimeSeriesFilter
A filter that ranks streams based on their statistical relation to other streams in a request. Note: This field is deprecated and completely ignored by the API.
Method
The filter methods that can be applied to a stream.
TableDisplayOptions
Table display options that can be reused.
Text
A widget that displays textual content.
Format
The format type of the text content.
TextStyle
Properties that determine how the title and content are styled
FontSize
Specifies a font size for the title and content of a text widget
HorizontalAlignment
The horizontal alignment of both the title and content on a text widget
PaddingSize
Specifies padding size around a text widget
PointerLocation
Specifies where a visual pointer is placed on a text widget (also sometimes called a "tail")
VerticalAlignment
The vertical alignment of both the title and content on a text widget
Threshold
Defines a threshold for categorizing time series values.
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.
Direction
Whether the threshold is considered crossed by an actual value above or below its threshold value.
TargetAxis
An axis identifier.
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
https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list`__
method.
This message has oneof
_ fields (mutually exclusive fields).
For each oneof, at most one member field can be set at the same time.
Setting any member of the oneof automatically clears all other
members.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
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.
This message has oneof
_ fields (mutually exclusive fields).
For each oneof, at most one member field can be set at the same time.
Setting any member of the oneof automatically clears all other
members.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
RatioPart
Describes a query to build the numerator or denominator of a TimeSeriesFilterRatio.
TimeSeriesQuery
TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API.
This message has oneof
_ fields (mutually exclusive fields).
For each oneof, at most one member field can be set at the same time.
Setting any member of the oneof automatically clears all other
members.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
TimeSeriesTable
A table that displays time series data.
ColumnSettings
The persistent settings for a table's columns.
MetricVisualization
Enum for metric metric_visualization
TableDataSet
Groups a time series query definition with table options.
UpdateDashboardRequest
The UpdateDashboard
request.
Widget
Widget contains a single dashboard component and configuration of how to present the component in the dashboard.
This message has oneof
_ fields (mutually exclusive fields).
For each oneof, at most one member field can be set at the same time.
Setting any member of the oneof automatically clears all other
members.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
XyChart
A chart that displays data on a 2D (X and Y axes) plane.
Axis
A chart axis.
Scale
Types of scales used in axes.
DataSet
Groups a time series query definition with charting options.
PlotType
The types of plotting strategies for data sets.
TargetAxis
An axis identifier.
Modules
pagers
API documentation for monitoring_dashboard_v1.services.dashboards_service.pagers
module.