Struct aws_sdk_cloudwatch::client::fluent_builders::PutAnomalyDetector

pub struct PutAnomalyDetector<C = DynConnector, M = DefaultMiddleware, R = Standard> { /* fields omitted */ }

Fluent builder constructing a request to PutAnomalyDetector.

Creates an anomaly detection model for a CloudWatch metric. You can use the model to display a band of expected normal values when the metric is graphed.

For more information, see CloudWatch Anomaly Detection.

Implementations

impl<C, M, R> PutAnomalyDetector<C, M, R> where
    C: SmithyConnector,
    M: SmithyMiddleware<C>,
    R: NewRequestPolicy, 

pub async fn send(
    self
) -> Result<PutAnomalyDetectorOutput, SdkError<PutAnomalyDetectorError>> where
    R::Policy: SmithyRetryPolicy<PutAnomalyDetectorInputOperationOutputAlias, PutAnomalyDetectorOutput, PutAnomalyDetectorError, PutAnomalyDetectorInputOperationRetryAlias>, 

Sends the request and returns the response.

If an error occurs, an SdkError will be returned with additional details that can be matched against.

By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.

pub fn namespace(self, input: impl Into<String>) -> Self

The namespace of the metric to create the anomaly detection model for.

pub fn set_namespace(self, input: Option<String>) -> Self

The namespace of the metric to create the anomaly detection model for.

pub fn metric_name(self, input: impl Into<String>) -> Self

The name of the metric to create the anomaly detection model for.

pub fn set_metric_name(self, input: Option<String>) -> Self

The name of the metric to create the anomaly detection model for.

pub fn dimensions(self, input: Dimension) -> Self

Appends an item to Dimensions.

To override the contents of this collection use set_dimensions.

The metric dimensions to create the anomaly detection model for.

pub fn set_dimensions(self, input: Option<Vec<Dimension>>) -> Self

The metric dimensions to create the anomaly detection model for.

pub fn stat(self, input: impl Into<String>) -> Self

The statistic to use for the metric and the anomaly detection model.

pub fn set_stat(self, input: Option<String>) -> Self

The statistic to use for the metric and the anomaly detection model.

pub fn configuration(self, input: AnomalyDetectorConfiguration) -> Self

The configuration specifies details about how the anomaly detection model is to be trained, including time ranges to exclude when training and updating the model. You can specify as many as 10 time ranges.

The configuration can also include the time zone to use for the metric.

pub fn set_configuration(
    self,
    input: Option<AnomalyDetectorConfiguration>
) -> Self

The configuration specifies details about how the anomaly detection model is to be trained, including time ranges to exclude when training and updating the model. You can specify as many as 10 time ranges.

The configuration can also include the time zone to use for the metric.

pub fn single_metric_anomaly_detector(
    self,
    input: SingleMetricAnomalyDetector
) -> Self

A single metric anomaly detector to be created.

When using SingleMetricAnomalyDetector, you cannot include the following parameters in the same operation:

• Dimensions

• MetricName

• Namespace

• Stat

• the MetricMatchAnomalyDetector parameters of PutAnomalyDetectorInput

Instead, specify the single metric anomaly detector attributes as part of the property SingleMetricAnomalyDetector.

pub fn set_single_metric_anomaly_detector(
    self,
    input: Option<SingleMetricAnomalyDetector>
) -> Self

A single metric anomaly detector to be created.

When using SingleMetricAnomalyDetector, you cannot include the following parameters in the same operation:

• Dimensions

• MetricName

• Namespace

• Stat

• the MetricMatchAnomalyDetector parameters of PutAnomalyDetectorInput

Instead, specify the single metric anomaly detector attributes as part of the property SingleMetricAnomalyDetector.

pub fn metric_math_anomaly_detector(
    self,
    input: MetricMathAnomalyDetector
) -> Self

The metric math anomaly detector to be created.

When using MetricMathAnomalyDetector, you cannot include the following parameters in the same operation:

• Dimensions

• MetricName

• Namespace

• Stat

• the SingleMetricAnomalyDetector parameters of PutAnomalyDetectorInput

Instead, specify the metric math anomaly detector attributes as part of the property MetricMathAnomalyDetector.

pub fn set_metric_math_anomaly_detector(
    self,
    input: Option<MetricMathAnomalyDetector>
) -> Self

The metric math anomaly detector to be created.

When using MetricMathAnomalyDetector, you cannot include the following parameters in the same operation:

• Dimensions

• MetricName

• Namespace

• Stat

• the SingleMetricAnomalyDetector parameters of PutAnomalyDetectorInput

Instead, specify the metric math anomaly detector attributes as part of the property MetricMathAnomalyDetector.

Trait Implementations

impl<C: Clone, M: Clone, R: Clone> Clone for PutAnomalyDetector<C, M, R>

fn clone(&self) -> PutAnomalyDetector<C, M, R>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<C: Debug, M: Debug, R: Debug> Debug for PutAnomalyDetector<C, M, R>

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

Formats the value using the given formatter.