update_stream_warm_throughput async ¶

async def update_stream_warm_throughput(
    self,
    warm_throughput_mi_bps: "capo_kinesis.types.natural_integer_object.NaturalIntegerObject",
    *,
    config_overrides: Optional[AsyncKinesisClientConfig] = None,
    stream_arn: Optional["capo_kinesis.types.stream_arn.StreamARN"] = None,
    stream_name: Optional["capo_kinesis.types.stream_name.StreamName"] = None,
    stream_id: Optional["capo_kinesis.types.stream_id.StreamId"] = None,
) -> "capo_kinesis.types.update_stream_warm_throughput_output.UpdateStreamWarmThroughputOutput":
    """<p>Updates the warm throughput configuration for the specified Amazon Kinesis Data Streams on-demand data stream. Updates the warm throughput configuration for the specified on-demand data stream. Use this operation to scale your stream to a specified throughput level before anticipated traffic spikes, or to release excess capacity after traffic has decreased. </p> <note> <p>When invoking this API, you must use either the <code>StreamARN</code> or the <code>StreamName</code> parameter, or both. It is recommended that you use the <code>StreamARN</code> input parameter when you invoke this API.</p> </note> <p>Updating the warm throughput is an asynchronous operation. Upon receiving the request, Kinesis Data Streams returns immediately and sets the status of the stream to <code>UPDATING</code>. After the update is complete, Kinesis Data Streams sets the status of the stream back to <code>ACTIVE</code>. Depending on the size of the stream, the scaling action could take a few minutes to complete. You can continue to read and write data to your stream while its status is <code>UPDATING</code>.</p> <p>This operation is only supported for data streams with the on-demand capacity mode in accounts that have <code>MinimumThroughputBillingCommitment</code> enabled. Provisioned capacity mode streams do not support warm throughput configuration.</p> <p>To release excess capacity, call the API again and set the warm throughput to the same or a lower value.</p> <p>This operation has the following default limits. By default, you cannot do the following:</p> <ul> <li> <p>Scale to more than 10 GiBps for an on-demand stream.</p> </li> <li> <p>This API has a call limit of 5 transactions per second (TPS) for each Amazon Web Services account. TPS over 5 will initiate the <code>LimitExceededException</code>.</p> </li> </ul> <p>For the default limits for an Amazon Web Services account, see <a href="https://docs.aws.amazon.com/kinesis/latest/dev/service-sizes-and-limits.html">Streams Limits</a> in the <i>Amazon Kinesis Data Streams Developer Guide</i>. To request an increase in the call rate limit, the shard limit for this API, or your overall shard limit, use the <a href="https://console.aws.amazon.com/support/v1#/case/create?issueType=service-limit-increase&limitType=service-code-kinesis">limits form</a>.</p>

    Args:
        stream_arn: <p>The ARN of the stream to be updated.</p>
        stream_name: <p>The name of the stream to be updated.</p>
        stream_id: <p>Not Implemented. Reserved for future use.</p>
        warm_throughput_mi_bps: <p>The target warm throughput in MB/s that the stream should be scaled to handle. This represents the throughput capacity that will be immediately available for write operations.</p>

    Raises:
        capo_kinesis.errors.access_denied_exception.AccessDeniedException: <p>Specifies that you do not have the permissions required to perform this operation.</p>
        capo_kinesis.errors.invalid_argument_exception.InvalidArgumentException: <p>A specified parameter exceeds its restrictions, is not supported, or can't be used. For more information, see the returned message.</p>
        capo_kinesis.errors.limit_exceeded_exception.LimitExceededException: <p>The requested resource exceeds the maximum number allowed, or the number of concurrent stream requests exceeds the maximum number allowed. </p>
        capo_kinesis.errors.resource_in_use_exception.ResourceInUseException: <p>The resource is not available for this operation. For successful operation, the resource must be in the <code>ACTIVE</code> state.</p>
        capo_kinesis.errors.resource_not_found_exception.ResourceNotFoundException: <p>The requested resource could not be found. The stream might not be specified correctly.</p>
        capo_kinesis.errors.validation_exception.ValidationException: <p>Specifies that you tried to invoke this API for a data stream with the on-demand capacity mode. This API is only supported for data streams with the provisioned capacity mode. </p>
        capo_kinesis.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_kinesis.types.update_stream_warm_throughput_input.UpdateStreamWarmThroughputInput]",
    ) -> AsyncOperationResponse[
        "capo_kinesis.types.update_stream_warm_throughput_output.UpdateStreamWarmThroughputOutput"
    ]:
        import capo_kinesis._operations.kinesis_20131202.update_stream_warm_throughput

        (
            output,
            http_response,
        ) = await capo_kinesis._operations.kinesis_20131202.update_stream_warm_throughput.async_update_stream_warm_throughput(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("UpdateStreamWarmThroughput")
    )
    input_: capo_kinesis.types.update_stream_warm_throughput_input.UpdateStreamWarmThroughputInput = {
        "warm_throughput_mi_bps": warm_throughput_mi_bps
    }
    if stream_arn is not None:
        input_["stream_arn"] = stream_arn
    if stream_name is not None:
        input_["stream_name"] = stream_name
    if stream_id is not None:
        input_["stream_id"] = stream_id

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output