Describe Anomaly Detectors

cloudwatch_describe_anomaly_detectors

R Documentation

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(NextToken, MaxResults, Namespace,
  MetricName, Dimensions, AnomalyDetectorTypes)

Arguments

NextToken

Use the token returned by the previous operation to request the next page of results.

MaxResults

The 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 NextToken value.

Namespace

Limits the results to only the anomaly detection models that are associated with the specified namespace.

MetricName

Limits 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.

Dimensions

Limits 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.

AnomalyDetectorTypes

The anomaly detector types to request when using DescribeAnomalyDetectorsInput. If empty, defaults to SINGLE_METRIC.

Value

A list with the following syntax:

list(
  AnomalyDetectors = list(
    list(
      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"
)

Request syntax

svc$describe_anomaly_detectors(
  NextToken = "string",
  MaxResults = 123,
  Namespace = "string",
  MetricName = "string",
  Dimensions = list(
    list(
      Name = "string",
      Value = "string"
    )
  ),
  AnomalyDetectorTypes = list(
    "SINGLE_METRIC"|"METRIC_MATH"
  )
)