pub struct PutAnomalyDetector<C = DynConnector, M = DefaultMiddleware, R = Standard> { /* private fields */ }
Expand description
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,
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>,
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.
The namespace of the metric to create the anomaly detection model for.
The namespace of the metric to create the anomaly detection model for.
The name of the metric to create the anomaly detection model for.
The name of the metric to create the anomaly detection model for.
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.
The metric dimensions to create the anomaly detection model for.
The statistic to use for the metric and the anomaly detection model.
The statistic to use for the metric and the anomaly detection model.
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.
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.
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 ofPutAnomalyDetectorInput
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
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 ofPutAnomalyDetectorInput
Instead, specify the single metric anomaly detector attributes as part of the property SingleMetricAnomalyDetector
.
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 ofPutAnomalyDetectorInput
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
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 ofPutAnomalyDetectorInput
Instead, specify the metric math anomaly detector attributes as part of the property MetricMathAnomalyDetector
.
Trait Implementations
Auto Trait Implementations
impl<C = DynConnector, M = DefaultMiddleware, R = Standard> !RefUnwindSafe for PutAnomalyDetector<C, M, R>
impl<C, M, R> Send for PutAnomalyDetector<C, M, R> where
C: Send + Sync,
M: Send + Sync,
R: Send + Sync,
impl<C, M, R> Sync for PutAnomalyDetector<C, M, R> where
C: Send + Sync,
M: Send + Sync,
R: Send + Sync,
impl<C, M, R> Unpin for PutAnomalyDetector<C, M, R>
impl<C = DynConnector, M = DefaultMiddleware, R = Standard> !UnwindSafe for PutAnomalyDetector<C, M, R>
Blanket Implementations
Mutably borrows from an owned value. Read more
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
Attaches the current default Subscriber
to this type, returning a
WithDispatch
wrapper. Read more