PrometheusService / Client / create_anomaly_detector

create_anomaly_detector

PrometheusService.Client.create_anomaly_detector(**kwargs)

Creates an anomaly detector within a workspace using the Random Cut Forest algorithm for time-series analysis. The anomaly detector analyzes Amazon Managed Service for Prometheus metrics to identify unusual patterns and behaviors.

See also: AWS API Documentation

Request Syntax

response = client.create_anomaly_detector(
    workspaceId='string',
    alias='string',
    evaluationIntervalInSeconds=123,
    missingDataAction={
        'markAsAnomaly': True|False,
        'skip': True|False
    },
    configuration={
        'randomCutForest': {
            'query': 'string',
            'shingleSize': 123,
            'sampleSize': 123,
            'ignoreNearExpectedFromAbove': {
                'amount': 123.0,
                'ratio': 123.0
            },
            'ignoreNearExpectedFromBelow': {
                'amount': 123.0,
                'ratio': 123.0
            }
        }
    },
    labels={
        'string': 'string'
    },
    clientToken='string',
    tags={
        'string': 'string'
    }
)

Parameters:

• workspaceId (string) –

  [REQUIRED]

  The identifier of the workspace where the anomaly detector will be created.

• alias (string) –

  [REQUIRED]

  A user-friendly name for the anomaly detector.

• evaluationIntervalInSeconds (integer) – The frequency, in seconds, at which the anomaly detector evaluates metrics. The default value is 60 seconds.

• missingDataAction (dict) –

  Specifies the action to take when data is missing during evaluation.

  Note

  This is a Tagged Union structure. Only one of the following top level keys can be set: markAsAnomaly, skip.

  • markAsAnomaly (boolean) –

    Marks missing data points as anomalies.

  • skip (boolean) –

    Skips evaluation when data is missing.

• configuration (dict) –

  [REQUIRED]

  The algorithm configuration for the anomaly detector.

  Note

  This is a Tagged Union structure. Only one of the following top level keys can be set: randomCutForest.

  • randomCutForest (dict) –

    The Random Cut Forest algorithm configuration for anomaly detection.

    • query (string) – [REQUIRED]

      The Prometheus query used to retrieve the time-series data for anomaly detection.

      Warning

      Random Cut Forest queries must be wrapped by a supported PromQL aggregation operator. For more information, see Aggregation operators on the Prometheus docs website.

      Supported PromQL aggregation operators: avg, count, group, max, min, quantile, stddev, stdvar, and sum.

    • shingleSize (integer) –

      The number of consecutive data points used to create a shingle for the Random Cut Forest algorithm. The default number is 8 consecutive data points.

    • sampleSize (integer) –

      The number of data points sampled from the input stream for the Random Cut Forest algorithm. The default number is 256 consecutive data points.

    • ignoreNearExpectedFromAbove (dict) –

      Configuration for ignoring values that are near expected values from above during anomaly detection.

      Note

      This is a Tagged Union structure. Only one of the following top level keys can be set: amount, ratio.

      • amount (float) –

        The absolute amount by which values can differ from expected values before being considered anomalous.

      • ratio (float) –

        The ratio by which values can differ from expected values before being considered anomalous.

    • ignoreNearExpectedFromBelow (dict) –

      Configuration for ignoring values that are near expected values from below during anomaly detection.

      Note

      This is a Tagged Union structure. Only one of the following top level keys can be set: amount, ratio.

      • amount (float) –

        The absolute amount by which values can differ from expected values before being considered anomalous.

      • ratio (float) –

        The ratio by which values can differ from expected values before being considered anomalous.

• labels (dict) –

  The Amazon Managed Service for Prometheus metric labels to associate with the anomaly detector.

  • (string) –

    • (string) –

• clientToken (string) –

  A unique, case-sensitive identifier that you provide to ensure the idempotency of the request.

  This field is autopopulated if not provided.

• tags (dict) –

  The metadata to apply to the anomaly detector to assist with categorization and organization.

  • (string) –

    The key of the tag. Must not begin with aws:.

    • (string) –

      The value of the tag.

Return type:

dict

Returns:

Response Syntax

{
    'anomalyDetectorId': 'string',
    'arn': 'string',
    'status': {
        'statusCode': 'CREATING'|'ACTIVE'|'UPDATING'|'DELETING'|'CREATION_FAILED'|'UPDATE_FAILED'|'DELETION_FAILED',
        'statusReason': 'string'
    },
    'tags': {
        'string': 'string'
    }
}

Response Structure

• (dict) –

  • anomalyDetectorId (string) –

    The unique identifier of the created anomaly detector.

  • arn (string) –

    The Amazon Resource Name (ARN) of the created anomaly detector.

  • status (dict) –

    The status information of the created anomaly detector.

    • statusCode (string) –

      The status code of the anomaly detector.

    • statusReason (string) –

      A description of the current status of the anomaly detector.

  • tags (dict) –

    The tags applied to the created anomaly detector.

    • (string) –

      The key of the tag. Must not begin with aws:.

      • (string) –

        The value of the tag.

Exceptions

• PrometheusService.Client.exceptions.ThrottlingException

• PrometheusService.Client.exceptions.ConflictException

• PrometheusService.Client.exceptions.ValidationException

• PrometheusService.Client.exceptions.AccessDeniedException

• PrometheusService.Client.exceptions.InternalServerException

• PrometheusService.Client.exceptions.ServiceQuotaExceededException