Aggregation class
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).
Constructors
 Aggregation()

Aggregation.fromJson(Map _json
)
Properties
 alignmentPeriod ↔ String

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 perseries aligner can be applied to the
data.The value must be at least 60 seconds. If a perseries aligner other
than ALIGN_NONE is specified, this field is required or an error is
returned. If no perseries aligner is specified, or the aligner ALIGN_NONE
is specified, then this field is ignored.
read / write
 crossSeriesReducer ↔ String

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 crosstime 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 string values are: [...]
read / write

groupByFields
↔ List<
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.
read / write
 hashCode → int

The hash code for this object. [...]
readonly, inherited
 perSeriesAligner ↔ String

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 crosstime 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.
Possible string values are: [...]
read / write
 runtimeType → Type

A representation of the runtime type of the object.
readonly, inherited
Methods

noSuchMethod(
Invocation invocation ) → dynamic 
Invoked when a nonexistent method or property is accessed. [...]
inherited

toJson(
) → Map< String, Object> 
toString(
) → String 
Returns a string representation of this object.
inherited
Operators

operator ==(
dynamic other ) → bool 
The equality operator. [...]
inherited