Struct google_monitoring3::Aggregation

pub struct Aggregation {
    pub group_by_fields: Option<Vec<String>>,
    pub alignment_period: Option<String>,
    pub per_series_aligner: Option<String>,
    pub cross_series_reducer: Option<String>,
}

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.

This type is not used in any activity, and only used as part of another schema.

Fields

group_by_fields: Option<Vec<String>>

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.

alignment_period: Option<String>

The alignment period for per-time series alignment. If present, alignmentPeriod must be at least 60 seconds. After per-time series alignment, each time series will contain data points only on the period boundaries. If perSeriesAligner is not specified or equals ALIGN_NONE, then this field is ignored. If perSeriesAligner is specified and does not equal ALIGN_NONE, then this field must be defined; otherwise an error is returned.

per_series_aligner: Option<String>

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.

cross_series_reducer: Option<String>

The approach to be used to combine time series. Not all reducer functions may be applied to all time series, depending on the metric type and the value type of the original time series. Reduction may change the metric type of 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.

Trait Implementations

impl Part for Aggregation

impl Default for Aggregation

fn default() -> Aggregation

Returns the "default value" for a type.

impl Clone for Aggregation

fn clone(&self) -> Aggregation

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Aggregation

fn fmt(&self, f: &mut Formatter) -> Result

Formats the value using the given formatter.

impl Serialize for Aggregation

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl<'de> Deserialize<'de> for Aggregation

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.