Enum Aligner 

#[non_exhaustive]
pub enum Aligner {
    AlignNone,
    AlignDelta,
    AlignRate,
    AlignInterpolate,
    AlignNextOlder,
    AlignMin,
    AlignMax,
    AlignMean,
    AlignCount,
    AlignSum,
    AlignStddev,
    AlignCountTrue,
    AlignCountFalse,
    AlignFractionTrue,
    AlignPercentile99,
    AlignPercentile95,
    AlignPercentile50,
    AlignPercentile05,
    AlignPercentChange,
    UnknownValue(UnknownValue),
}

The Aligner specifies the operation that will be applied to the data points in each alignment period in a time series. Except for ALIGN_NONE, which specifies that no operation be applied, each alignment operation replaces the set of data values in each alignment period with a single value: the result of applying the operation to the data values. An aligned time series has a single data value at the end of each alignment_period.

An alignment operation can change the data type of the values, too. For example, if you apply a counting operation to boolean values, the data value_type in the original time series is BOOLEAN, but the value_type in the aligned result is INT64.

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.

AlignNone

No alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.

AlignDelta

Align and convert to DELTA. The output is delta = y1 - y0.

This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.

AlignRate

Align and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or “delta over time”. Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.

This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.

If, by “rate”, you mean “percentage change”, see the ALIGN_PERCENT_CHANGE aligner instead.

AlignInterpolate

Align by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.

AlignNextOlder

Align by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.

AlignMin

Align the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.

AlignMax

Align the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.

AlignMean

Align the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.

AlignCount

Align the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.

AlignSum

Align the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.

AlignStddev

Align the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.

AlignCountTrue

Align the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.

AlignCountFalse

Align the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.

AlignFractionTrue

Align the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range [0.0, 1.0] and has value_type DOUBLE.

AlignPercentile99

Align the time series by using percentile aggregation. The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.

AlignPercentile95

Align the time series by using percentile aggregation. The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.

AlignPercentile50

Align the time series by using percentile aggregation. The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.

AlignPercentile05

Align the time series by using percentile aggregation. The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.

AlignPercentChange

Align and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.

If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.

A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.

UnknownValue(UnknownValue)

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

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

Implementations

impl Aligner

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 Aligner

fn clone(&self) -> Aligner

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Aligner

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

Formats the value using the given formatter.

impl Default for Aligner

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for Aligner

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

where D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Display for Aligner

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

Formats the value using the given formatter.

impl From<&str> for Aligner

fn from(value: &str) -> Self

Converts to this type from the input type.

impl From<i32> for Aligner

fn from(value: i32) -> Self

Converts to this type from the input type.

impl PartialEq for Aligner

fn eq(&self, other: &Aligner) -> 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 Aligner

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 Aligner