execute_scheduled_query ¶

def execute_scheduled_query(
    self,
    scheduled_query_arn: "capo_timestream_query.types.amazon_resource_name.AmazonResourceName",
    invocation_time: "capo_timestream_query.types.time.Time",
    *,
    config_overrides: Optional[TimestreamQueryClientConfig] = None,
    client_token: Optional[
        "capo_timestream_query.types.client_token.ClientToken"
    ] = None,
    query_insights: Optional[
        "capo_timestream_query.types.scheduled_query_insights.ScheduledQueryInsights"
    ] = None,
) -> None:
    """<p> You can use this API to run a scheduled query manually. </p> <p>If you enabled <code>QueryInsights</code>, this API also returns insights and metrics related to the query that you executed as part of an Amazon SNS notification. <code>QueryInsights</code> helps with performance tuning of your query. For more information about <code>QueryInsights</code>, see <a href="https://docs.aws.amazon.com/timestream/latest/developerguide/using-query-insights.html">Using query insights to optimize queries in Amazon Timestream</a>.</p>

    Args:
        scheduled_query_arn: <p>ARN of the scheduled query.</p>
        invocation_time: <p>The timestamp in UTC. Query will be run as if it was invoked at this timestamp. </p>
        client_token: <p>Not used. </p>
        query_insights: <p>Encapsulates settings for enabling <code>QueryInsights</code>.</p> <p>Enabling <code>QueryInsights</code> returns insights and metrics as a part of the Amazon SNS notification for the query that you executed. You can use <code>QueryInsights</code> to tune your query performance and cost.</p>

    Raises:
        capo_timestream_query.errors.access_denied_exception.AccessDeniedException: <p>You do not have the necessary permissions to access the account settings.</p>
        capo_timestream_query.errors.internal_server_exception.InternalServerException: <p>An internal server error occurred while processing the request.</p>
        capo_timestream_query.errors.invalid_endpoint_exception.InvalidEndpointException: <p>The requested endpoint is invalid.</p>
        capo_timestream_query.errors.resource_not_found_exception.ResourceNotFoundException: <p>The requested resource could not be found.</p>
        capo_timestream_query.errors.throttling_exception.ThrottlingException: <p>The request was throttled due to excessive requests.</p>
        capo_timestream_query.errors.validation_exception.ValidationException: <p> Invalid or malformed request. </p>
        capo_timestream_query.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_timestream_query.types.execute_scheduled_query_request.ExecuteScheduledQueryRequest]",
    ) -> OperationResponse[None]:
        import capo_timestream_query._operations.timestream_20181101.execute_scheduled_query

        output, http_response = (
            capo_timestream_query._operations.timestream_20181101.execute_scheduled_query.execute_scheduled_query(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_timestream_query.types.execute_scheduled_query_request.ExecuteScheduledQueryRequest",
    ) -> dict[str, Any]:
        import capo_timestream_query.types.execute_scheduled_query_request

        return capo_timestream_query.types.execute_scheduled_query_request.redact_sensitive(
            input_
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("ExecuteScheduledQuery", redact_input=_redact_input),
    )
    input_: capo_timestream_query.types.execute_scheduled_query_request.ExecuteScheduledQueryRequest = {
        "scheduled_query_arn": scheduled_query_arn,
        "invocation_time": invocation_time,
    }
    if client_token is None:
        client_token = str(uuid.uuid4())
    input_["client_token"] = client_token
    if query_insights is not None:
        input_["query_insights"] = query_insights

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