Input ¶

class Input(TypedDict, closed=True):
    name_prefix: "capo_kinesis_analytics_v2.types.in_app_stream_name.InAppStreamName"
    """<p>The name prefix to use when creating an in-application stream. Suppose that you specify a prefix "<code>MyInApplicationStream</code>." Kinesis Data Analytics then creates one or more (as per the <code>InputParallelism</code> count you specified) in-application streams with the names "<code>MyInApplicationStream_001</code>," "<code>MyInApplicationStream_002</code>," and so on. </p>"""
    input_processing_configuration: NotRequired[
        "capo_kinesis_analytics_v2.types.input_processing_configuration.InputProcessingConfiguration"
    ]
    """<p>The <a>InputProcessingConfiguration</a> for the input. An input processor transforms records as they are received from the stream, before the application's SQL code executes. Currently, the only input processing configuration available is <a>InputLambdaProcessor</a>. </p>"""
    kinesis_streams_input: NotRequired[
        "capo_kinesis_analytics_v2.types.kinesis_streams_input.KinesisStreamsInput"
    ]
    """<p>If the streaming source is an Amazon Kinesis data stream, identifies the stream's Amazon Resource Name (ARN). </p>"""
    kinesis_firehose_input: NotRequired[
        "capo_kinesis_analytics_v2.types.kinesis_firehose_input.KinesisFirehoseInput"
    ]
    """<p>If the streaming source is an Amazon Kinesis Data Firehose delivery stream, identifies the delivery stream's ARN.</p>"""
    input_parallelism: NotRequired[
        "capo_kinesis_analytics_v2.types.input_parallelism.InputParallelism"
    ]
    """<p>Describes the number of in-application streams to create. </p>"""
    input_schema: "capo_kinesis_analytics_v2.types.source_schema.SourceSchema"
    """<p>Describes the format of the data in the streaming source, and how each data element maps to corresponding columns in the in-application stream that is being created.</p> <p>Also used to describe the format of the reference data source.</p>"""