Struct Aggregation 

#[non_exhaustive]
pub struct Aggregation {
    pub alignment_period: Option<Duration>,
    pub per_series_aligner: Aligner,
    pub cross_series_reducer: Reducer,
    pub group_by_fields: Vec<String>,
    /* private fields */
}

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.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

alignment_period: Option<Duration>

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 per-series aligner can be applied to the data.

The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.

The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.

per_series_aligner: Aligner

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 cross-time 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.

cross_series_reducer: Reducer

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 cross-time 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.

group_by_fields: Vec<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.

Implementations

impl Aggregation

pub fn new() -> Self

pub fn set_alignment_period<T>(self, v: T) -> Self

where T: Into<Duration>,

Sets the value of alignment_period.

Example

use wkt::Duration;
let x = Aggregation::new().set_alignment_period(Duration::default()/* use setters */);

pub fn set_or_clear_alignment_period<T>(self, v: Option<T>) -> Self

where T: Into<Duration>,

Sets or clears the value of alignment_period.

Example

use wkt::Duration;
let x = Aggregation::new().set_or_clear_alignment_period(Some(Duration::default()/* use setters */));
let x = Aggregation::new().set_or_clear_alignment_period(None::<Duration>);

pub fn set_per_series_aligner<T: Into<Aligner>>(self, v: T) -> Self

Sets the value of per_series_aligner.

Example

use google_cloud_monitoring_v3::model::aggregation::Aligner;
let x0 = Aggregation::new().set_per_series_aligner(Aligner::AlignDelta);
let x1 = Aggregation::new().set_per_series_aligner(Aligner::AlignRate);
let x2 = Aggregation::new().set_per_series_aligner(Aligner::AlignInterpolate);

pub fn set_cross_series_reducer<T: Into<Reducer>>(self, v: T) -> Self

Sets the value of cross_series_reducer.

Example

use google_cloud_monitoring_v3::model::aggregation::Reducer;
let x0 = Aggregation::new().set_cross_series_reducer(Reducer::ReduceMean);
let x1 = Aggregation::new().set_cross_series_reducer(Reducer::ReduceMin);
let x2 = Aggregation::new().set_cross_series_reducer(Reducer::ReduceMax);

pub fn set_group_by_fields<T, V>(self, v: T) -> Self

where T: IntoIterator<Item = V>, V: Into<String>,

Sets the value of group_by_fields.

Example

let x = Aggregation::new().set_group_by_fields(["a", "b", "c"]);

Trait Implementations

impl Clone for Aggregation

fn clone(&self) -> Aggregation

Returns a duplicate 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 Default for Aggregation

fn default() -> Aggregation

Returns the “default value” for a type.

impl Message for Aggregation

fn typename() -> &'static str

The typename of this message.

impl PartialEq for Aggregation

fn eq(&self, other: &Aggregation) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for Aggregation