Class: Google::Apis::MonitoringV1::RatioPart

Inherits:

Object

• Object
• Google::Apis::MonitoringV1::RatioPart

Includes:

Core::Hashable, Core::JsonObjectSupport

Defined in:

generated/google/apis/monitoring_v1/classes.rb,
generated/google/apis/monitoring_v1/representations.rb,
generated/google/apis/monitoring_v1/representations.rb

Overview

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

Instance Attribute Summary

• #aggregation ⇒ Google::Apis::MonitoringV1::Aggregation

  Describes how to combine multiple time series to provide a different view of the data.

• #filter ⇒ String

  Required.

Instance Method Summary

• #initialize(**args) ⇒ RatioPart constructor

  A new instance of RatioPart.

• #update!(**args) ⇒ Object

  Update properties of this object.

Constructor Details

#initialize(**args) ⇒ RatioPart

Returns a new instance of RatioPart.

# File 'generated/google/apis/monitoring_v1/classes.rb', line 660

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#aggregation ⇒ Google::Apis::MonitoringV1::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). Corresponds to the JSON property aggregation

Returns:

• (Google::Apis::MonitoringV1::Aggregation)

# File 'generated/google/apis/monitoring_v1/classes.rb', line 652

def aggregation
  @aggregation
end

#filter ⇒ String

Required. The monitoring filter (https://cloud.google.com/monitoring/api/v3/ filters) that identifies the metric types, resources, and projects to query. Corresponds to the JSON property filter

Returns:

• (String)

# File 'generated/google/apis/monitoring_v1/classes.rb', line 658

def filter
  @filter
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object

# File 'generated/google/apis/monitoring_v1/classes.rb', line 665

def update!(**args)
  @aggregation = args[:aggregation] if args.key?(:aggregation)
  @filter = args[:filter] if args.key?(:filter)
end