Describes how to combine multiple time series to provide different views
of the data. Aggregation consists of an alignment step on individual
time series (alignment_period and per_series_aligner) followed
by an optional reduction step of the data across the aligned time series
(cross_series_reducer and group_by_fields). For more details,
see Aggregation </monitoring/api/learn_more#aggregation>__.
The approach to be used to align individual time series. Not
all alignment functions may be applied to all time series,
depending on the metric type and value type of the original
time series. Alignment may change the metric type or the value
type of the time series. Time series data must be aligned in
order to perform cross-time series reduction. If
crossSeriesReducer is specified, then perSeriesAligner
must be specified and not equal ALIGN_NONE and
alignmentPeriod must be specified; otherwise, an error is
returned.
The set of fields to preserve when crossSeriesReducer is
specified. The groupByFields determine how the time series
are partitioned into subsets prior to applying the aggregation
function. 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
crossSeriesReducer 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 groupByFields are aggregated away.
If groupByFields 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
crossSeriesReducer is not defined, this field is ignored.