Lists the anomaly detection models that you have created in your account¶
Description¶
Lists the anomaly detection models that you have created in your account. For single metric anomaly detectors, you can list all of the models in your account or filter the results to only the models that are related to a certain namespace, metric name, or metric dimension. For metric math anomaly detectors, you can list them by adding METRIC_MATH to the AnomalyDetectorTypes array. This will return all metric math anomaly detectors in your account.
Usage¶
cloudwatch_describe_anomaly_detectors(AnomalyDetectorIds, NextToken,
MaxResults, Namespace, MetricName, Dimensions, AnomalyDetectorTypes)
Arguments¶
-
AnomalyDetectorIdsSpecifies the unique identifiers of the anomaly detectors to describe. You can specify up to 50 identifiers. If you specify this parameter, you cannot also specify the
Namespace,MetricName,Dimensions, orAnomalyDetectorTypesmetric filters. -
NextTokenUse the token returned by the previous operation to request the next page of results.
-
MaxResultsThe maximum number of results to return in one operation. The maximum value that you can specify is 100.
To retrieve the remaining results, make another call with the returned
NextTokenvalue. -
NamespaceLimits the results to only the anomaly detection models that are associated with the specified namespace.
-
MetricNameLimits the results to only the anomaly detection models that are associated with the specified metric name. If there are multiple metrics with this name in different namespaces that have anomaly detection models, they're all returned.
-
DimensionsLimits the results to only the anomaly detection models that are associated with the specified metric dimensions. If there are multiple metrics that have these dimensions and have anomaly detection models associated, they're all returned.
-
AnomalyDetectorTypesThe anomaly detector types to request when using
DescribeAnomalyDetectorsInput. If empty, defaults toSINGLE_METRIC.
Value¶
A list with the following syntax:
list(
AnomalyDetectors = list(
list(
AnomalyDetectorId = "string",
Namespace = "string",
MetricName = "string",
Dimensions = list(
list(
Name = "string",
Value = "string"
)
),
Stat = "string",
Configuration = list(
ExcludedTimeRanges = list(
list(
StartTime = as.POSIXct(
"2015-01-01"
),
EndTime = as.POSIXct(
"2015-01-01"
)
)
),
MetricTimezone = "string"
),
StateValue = "PENDING_TRAINING"|"TRAINED_INSUFFICIENT_DATA"|"TRAINED",
MetricCharacteristics = list(
PeriodicSpikes = TRUE|FALSE
),
SingleMetricAnomalyDetector = list(
AccountId = "string",
Namespace = "string",
MetricName = "string",
Dimensions = list(
list(
Name = "string",
Value = "string"
)
),
Stat = "string"
),
MetricMathAnomalyDetector = list(
MetricDataQueries = list(
list(
Id = "string",
MetricStat = list(
Metric = list(
Namespace = "string",
MetricName = "string",
Dimensions = list(
list(
Name = "string",
Value = "string"
)
)
),
Period = 123,
Stat = "string",
Unit = "Seconds"|"Microseconds"|"Milliseconds"|"Bytes"|"Kilobytes"|"Megabytes"|"Gigabytes"|"Terabytes"|"Bits"|"Kilobits"|"Megabits"|"Gigabits"|"Terabits"|"Percent"|"Count"|"Bytes/Second"|"Kilobytes/Second"|"Megabytes/Second"|"Gigabytes/Second"|"Terabytes/Second"|"Bits/Second"|"Kilobits/Second"|"Megabits/Second"|"Gigabits/Second"|"Terabits/Second"|"Count/Second"|"None"
),
Expression = "string",
Label = "string",
ReturnData = TRUE|FALSE,
Period = 123,
AccountId = "string"
)
)
)
)
),
NextToken = "string"
)