Package Classes (2.15.2)

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.

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.

.. _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

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.

.. _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.

.. _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.

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

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Modules

pagers

API documentation for monitoring_dashboard_v1.services.dashboards_service.pagers module.