list_prefetch_schedules ¶

def list_prefetch_schedules(
    self,
    playback_configuration_name: "capo_mediatailor.types.__string.__string",
    *,
    config_overrides: Optional[MediaTailorClientConfig] = None,
    max_results: Optional[
        "capo_mediatailor.types.__integer_min1_max100.__integerMin1Max100"
    ] = None,
    next_token: Optional["capo_mediatailor.types.__string.__string"] = None,
    schedule_type: Optional[
        "capo_mediatailor.types.list_prefetch_schedule_type.ListPrefetchScheduleType"
    ] = None,
    stream_id: Optional["capo_mediatailor.types.__string.__string"] = None,
) -> "capo_mediatailor.types.list_prefetch_schedules_response.ListPrefetchSchedulesResponse":
    """<p>Lists the prefetch schedules for a playback configuration.</p>

    Args:
        max_results: <p>The maximum number of prefetch schedules that you want MediaTailor to return in response to the current request. If there are more than <code>MaxResults</code> prefetch schedules, use the value of <code>NextToken</code> in the response to get the next page of results.</p> <p>The default value is 100. MediaTailor uses DynamoDB-based pagination, which means that a response might contain fewer than <code>MaxResults</code> items, including 0 items, even when more results are available. To retrieve all results, you must continue making requests using the <code>NextToken</code> value from each response until the response no longer includes a <code>NextToken</code> value.</p>
        next_token: <p>Pagination token returned by the list request when results exceed the maximum allowed. Use the token to fetch the next page of results.</p> <p>For the first <code>ListPrefetchSchedules</code> request, omit this value. For subsequent requests, get the value of <code>NextToken</code> from the previous response and specify that value for <code>NextToken</code> in the request. Continue making requests until the response no longer includes a <code>NextToken</code> value, which indicates that all results have been retrieved.</p>
        playback_configuration_name: <p>Retrieves the prefetch schedule(s) for a specific playback configuration.</p>
        schedule_type: <p>The type of prefetch schedules that you want to list. <code>SINGLE</code> indicates that you want to list the configured single prefetch schedules. <code>RECURRING</code> indicates that you want to list the configured recurring prefetch schedules. <code>ALL</code> indicates that you want to list all configured prefetch schedules.</p>
        stream_id: <p>An optional filtering parameter whereby MediaTailor filters the prefetch schedules to include only specific streams.</p>

    Raises:
        capo_mediatailor.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_mediatailor.types.list_prefetch_schedules_request.ListPrefetchSchedulesRequest]",
    ) -> OperationResponse[
        "capo_mediatailor.types.list_prefetch_schedules_response.ListPrefetchSchedulesResponse"
    ]:
        import capo_mediatailor._operations.media_tailor.list_prefetch_schedules

        output, http_response = (
            capo_mediatailor._operations.media_tailor.list_prefetch_schedules.list_prefetch_schedules(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ListPrefetchSchedules")
    )
    input_: capo_mediatailor.types.list_prefetch_schedules_request.ListPrefetchSchedulesRequest = {
        "playback_configuration_name": playback_configuration_name
    }
    if max_results is not None:
        input_["max_results"] = max_results
    if next_token is not None:
        input_["next_token"] = next_token
    if schedule_type is not None:
        input_["schedule_type"] = schedule_type
    if stream_id is not None:
        input_["stream_id"] = stream_id

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