Enum Reducer 

#[non_exhaustive]
pub enum Reducer {
    ReduceNone,
    ReduceMean,
    ReduceMin,
    ReduceMax,
    ReduceSum,
    ReduceStddev,
    ReduceCount,
    ReduceCountTrue,
    ReduceCountFalse,
    ReduceFractionTrue,
    ReducePercentile99,
    ReducePercentile95,
    ReducePercentile50,
    ReducePercentile05,
    UnknownValue(UnknownValue),
}

A Reducer operation describes how to aggregate data points from multiple 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.

Working with unknown values

This enum is defined as #[non_exhaustive] because Google Cloud may add additional enum variants at any time. Adding new variants is not considered a breaking change. Applications should write their code in anticipation of:

• New values appearing in future releases of the client library, and
• New values received dynamically, without application changes.

Please consult the Working with enums section in the user guide for some guidelines.

Variants (Non-exhaustive)

Non-exhaustive enums could have additional variants added in future. Therefore, when matching against variants of non-exhaustive enums, an extra wildcard arm must be added to account for any future variants.

ReduceNone

No cross-time series reduction. The output of the Aligner is returned.

ReduceMean

Reduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.

ReduceMin

Reduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.

ReduceMax

Reduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.

ReduceSum

Reduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.

ReduceStddev

Reduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.

ReduceCount

Reduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.

ReduceCountTrue

Reduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.

ReduceCountFalse

Reduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.

ReduceFractionTrue

Reduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range [0.0, 1.0] and has value_type DOUBLE.

ReducePercentile99

Reduce by computing the 99th percentile of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.

ReducePercentile95

Reduce by computing the 95th percentile of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.

ReducePercentile50

Reduce by computing the 50th percentile of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.

ReducePercentile05

Reduce by computing the 5th percentile of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.

UnknownValue(UnknownValue)

If set, the enum was initialized with an unknown value.

Applications can examine the value using Reducer::value or Reducer::name.

Implementations

impl Reducer

pub fn value(&self) -> Option<i32>

Gets the enum value.

Returns None if the enum contains an unknown value deserialized from the string representation of enums.

pub fn name(&self) -> Option<&str>

Gets the enum value as a string.

Returns None if the enum contains an unknown value deserialized from the integer representation of enums.

Trait Implementations

impl Clone for Reducer

fn clone(&self) -> Reducer

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Reducer

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

Formats the value using the given formatter.

impl Default for Reducer

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for Reducer

fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>

where D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Display for Reducer

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl From<&str> for Reducer

fn from(value: &str) -> Self

Converts to this type from the input type.

impl From<i32> for Reducer

fn from(value: i32) -> Self

Converts to this type from the input type.

impl PartialEq for Reducer

fn eq(&self, other: &Reducer) -> 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 Serialize for Reducer

fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>

where S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for Reducer