put_anomaly_detector ¶

def put_anomaly_detector(
    self,
    workspace_id: "capo_amp.types.workspace_id.WorkspaceId",
    anomaly_detector_id: "capo_amp.types.anomaly_detector_id.AnomalyDetectorId",
    configuration: "capo_amp.types.anomaly_detector_configuration.AnomalyDetectorConfiguration",
    *,
    config_overrides: Optional[ampClientConfig] = None,
    evaluation_interval_in_seconds: Optional[
        "capo_amp.types.anomaly_detector_evaluation_interval.AnomalyDetectorEvaluationInterval"
    ] = None,
    missing_data_action: Optional[
        "capo_amp.types.anomaly_detector_missing_data_action.AnomalyDetectorMissingDataAction"
    ] = None,
    labels: Optional[
        "capo_amp.types.prometheus_metric_label_map.PrometheusMetricLabelMap"
    ] = None,
    client_token: Optional[
        "capo_amp.types.idempotency_token.IdempotencyToken"
    ] = None,
) -> "capo_amp.types.put_anomaly_detector_response.PutAnomalyDetectorResponse":
    """<p>When you call <code>PutAnomalyDetector</code>, the operation creates a new anomaly detector if one doesn't exist, or updates an existing one. Each call to this operation triggers a complete retraining of the detector, which includes querying the minimum required samples and backfilling the detector with historical data. This process occurs regardless of whether you're making a minor change like updating the evaluation interval or making more substantial modifications. The operation serves as the single method for creating, updating, and retraining anomaly detectors.</p>

    Args:
        workspace_id: <p>The identifier of the workspace containing the anomaly detector to update.</p>
        anomaly_detector_id: <p>The identifier of the anomaly detector to update.</p>
        evaluation_interval_in_seconds: <p>The frequency, in seconds, at which the anomaly detector evaluates metrics.</p>
        missing_data_action: <p>Specifies the action to take when data is missing during evaluation.</p>
        configuration: <p>The algorithm configuration for the anomaly detector.</p>
        labels: <p>The Amazon Managed Service for Prometheus metric labels to associate with the anomaly detector.</p>
        client_token: <p>A unique, case-sensitive identifier that you provide to ensure the idempotency of the request.</p>

    Raises:
        capo_amp.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
        capo_amp.errors.conflict_exception.ConflictException: <p>The request would cause an inconsistent state.</p>
        capo_amp.errors.internal_server_exception.InternalServerException: <p>An unexpected error occurred during the processing of the request.</p>
        capo_amp.errors.service_quota_exceeded_exception.ServiceQuotaExceededException: <p>Completing the request would cause a service quota to be exceeded.</p>
        capo_amp.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling.</p>
        capo_amp.errors.validation_exception.ValidationException: <p>The input fails to satisfy the constraints specified by an Amazon Web Services service.</p>
        capo_amp.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_amp.types.put_anomaly_detector_request.PutAnomalyDetectorRequest]",
    ) -> OperationResponse[
        "capo_amp.types.put_anomaly_detector_response.PutAnomalyDetectorResponse"
    ]:
        import capo_amp._operations.amazon_prometheus_service.put_anomaly_detector

        output, http_response = (
            capo_amp._operations.amazon_prometheus_service.put_anomaly_detector.put_anomaly_detector(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("PutAnomalyDetector")
    )
    input_: capo_amp.types.put_anomaly_detector_request.PutAnomalyDetectorRequest = {
        "workspace_id": workspace_id,
        "anomaly_detector_id": anomaly_detector_id,
        "configuration": configuration,
    }
    if evaluation_interval_in_seconds is not None:
        input_["evaluation_interval_in_seconds"] = evaluation_interval_in_seconds
    if missing_data_action is not None:
        input_["missing_data_action"] = missing_data_action
    if labels is not None:
        input_["labels"] = labels
    if client_token is None:
        client_token = str(uuid.uuid4())
    input_["client_token"] = client_token

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output