AnomalyDetector ¶

class AnomalyDetector(TypedDict, closed=True):
    anomaly_detector_arn: NotRequired[
        "capo_cloudwatch_logs.types.anomaly_detector_arn.AnomalyDetectorArn"
    ]
    """<p>The ARN of the anomaly detector.</p>"""
    detector_name: NotRequired["capo_cloudwatch_logs.types.detector_name.DetectorName"]
    """<p>The name of the anomaly detector.</p>"""
    log_group_arn_list: NotRequired[
        "capo_cloudwatch_logs.types.log_group_arn_list.LogGroupArnList"
    ]
    """<p>A list of the ARNs of the log groups that this anomaly detector watches.</p>"""
    evaluation_frequency: NotRequired[
        "capo_cloudwatch_logs.types.evaluation_frequency.EvaluationFrequency"
    ]
    """<p>Specifies how often the anomaly detector runs and look for anomalies.</p>"""
    filter_pattern: NotRequired[
        "capo_cloudwatch_logs.types.filter_pattern.FilterPattern"
    ]
    anomaly_detector_status: NotRequired[
        "capo_cloudwatch_logs.types.anomaly_detector_status.AnomalyDetectorStatus"
    ]
    """<p>Specifies the current status of the anomaly detector. To pause an anomaly detector, use the <code>enabled</code> parameter in the <a href="https://docs.aws.amazon.com/AmazonCloudWatchLogs/latest/APIReference/API_UpdateLogAnomalyDetector.html">UpdateLogAnomalyDetector</a> operation.</p>"""
    kms_key_id: NotRequired["capo_cloudwatch_logs.types.kms_key_id.KmsKeyId"]
    """<p>The ARN of the KMS key assigned to this anomaly detector, if any.</p>"""
    creation_time_stamp: "capo_cloudwatch_logs.types.epoch_millis.EpochMillis"
    """<p>The date and time when this anomaly detector was created.</p>"""
    last_modified_time_stamp: "capo_cloudwatch_logs.types.epoch_millis.EpochMillis"
    """<p>The date and time when this anomaly detector was most recently modified.</p>"""
    anomaly_visibility_time: NotRequired[
        "capo_cloudwatch_logs.types.anomaly_visibility_time.AnomalyVisibilityTime"
    ]
    """<p>The number of days used as the life cycle of anomalies. After this time, anomalies are automatically baselined and the anomaly detector model will treat new occurrences of similar event as normal. </p>"""