CfnApplicationProps

class aws_cdk.aws_kinesisanalytics.CfnApplicationProps(*, inputs, application_code=None, application_description=None, application_name=None)

Bases: object

Properties for defining a CfnApplication.

Parameters:

inputs (Union[IResolvable, Sequence[Union[InputProperty, Dict[str, Any], IResolvable]]]) – Use this parameter to configure the application input. You can configure your application to receive input from a single streaming source. In this configuration, you map this streaming source to an in-application stream that is created. Your application code can then query the in-application stream like a table (you can think of it as a constantly updating table). For the streaming source, you provide its Amazon Resource Name (ARN) and format of data on the stream (for example, JSON, CSV, etc.). You also must provide an IAM role that Amazon Kinesis Analytics can assume to read this stream on your behalf. To create the in-application stream, you need to specify a schema to transform your data into a schematized version used in SQL. In the schema, you provide the necessary mapping of the data elements in the streaming source to record columns in the in-app stream.
application_code (Optional[str]) – One or more SQL statements that read input data, transform it, and generate output. For example, you can write a SQL statement that reads data from one in-application stream, generates a running average of the number of advertisement clicks by vendor, and insert resulting rows in another in-application stream using pumps. For more information about the typical pattern, see Application Code . You can provide such series of SQL statements, where output of one statement can be used as the input for the next statement. You store intermediate results by creating in-application streams and pumps. Note that the application code must create the streams with names specified in the Outputs . For example, if your Outputs defines output streams named ExampleOutputStream1 and ExampleOutputStream2 , then your application code must create these streams.
application_description (Optional[str]) – Summary description of the application.
application_name (Optional[str]) – Name of your Amazon Kinesis Analytics application (for example, sample-app ).

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-kinesisanalytics-application.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
import aws_cdk.aws_kinesisanalytics as kinesisanalytics

cfn_application_props = kinesisanalytics.CfnApplicationProps(
    inputs=[kinesisanalytics.CfnApplication.InputProperty(
        input_schema=kinesisanalytics.CfnApplication.InputSchemaProperty(
            record_columns=[kinesisanalytics.CfnApplication.RecordColumnProperty(
                name="name",
                sql_type="sqlType",

                # the properties below are optional
                mapping="mapping"
            )],
            record_format=kinesisanalytics.CfnApplication.RecordFormatProperty(
                record_format_type="recordFormatType",

                # the properties below are optional
                mapping_parameters=kinesisanalytics.CfnApplication.MappingParametersProperty(
                    csv_mapping_parameters=kinesisanalytics.CfnApplication.CSVMappingParametersProperty(
                        record_column_delimiter="recordColumnDelimiter",
                        record_row_delimiter="recordRowDelimiter"
                    ),
                    json_mapping_parameters=kinesisanalytics.CfnApplication.JSONMappingParametersProperty(
                        record_row_path="recordRowPath"
                    )
                )
            ),

            # the properties below are optional
            record_encoding="recordEncoding"
        ),
        name_prefix="namePrefix",

        # the properties below are optional
        input_parallelism=kinesisanalytics.CfnApplication.InputParallelismProperty(
            count=123
        ),
        input_processing_configuration=kinesisanalytics.CfnApplication.InputProcessingConfigurationProperty(
            input_lambda_processor=kinesisanalytics.CfnApplication.InputLambdaProcessorProperty(
                resource_arn="resourceArn",
                role_arn="roleArn"
            )
        ),
        kinesis_firehose_input=kinesisanalytics.CfnApplication.KinesisFirehoseInputProperty(
            resource_arn="resourceArn",
            role_arn="roleArn"
        ),
        kinesis_streams_input=kinesisanalytics.CfnApplication.KinesisStreamsInputProperty(
            resource_arn="resourceArn",
            role_arn="roleArn"
        )
    )],

    # the properties below are optional
    application_code="applicationCode",
    application_description="applicationDescription",
    application_name="applicationName"
)

Attributes

application_code

One or more SQL statements that read input data, transform it, and generate output.

For example, you can write a SQL statement that reads data from one in-application stream, generates a running average of the number of advertisement clicks by vendor, and insert resulting rows in another in-application stream using pumps. For more information about the typical pattern, see Application Code .

You can provide such series of SQL statements, where output of one statement can be used as the input for the next statement. You store intermediate results by creating in-application streams and pumps.

Note that the application code must create the streams with names specified in the Outputs . For example, if your Outputs defines output streams named ExampleOutputStream1 and ExampleOutputStream2 , then your application code must create these streams.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-kinesisanalytics-application.html#cfn-kinesisanalytics-application-applicationcode

application_description

Summary description of the application.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-kinesisanalytics-application.html#cfn-kinesisanalytics-application-applicationdescription

application_name

Name of your Amazon Kinesis Analytics application (for example, sample-app ).

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-kinesisanalytics-application.html#cfn-kinesisanalytics-application-applicationname

inputs

Use this parameter to configure the application input.

You can configure your application to receive input from a single streaming source. In this configuration, you map this streaming source to an in-application stream that is created. Your application code can then query the in-application stream like a table (you can think of it as a constantly updating table).

For the streaming source, you provide its Amazon Resource Name (ARN) and format of data on the stream (for example, JSON, CSV, etc.). You also must provide an IAM role that Amazon Kinesis Analytics can assume to read this stream on your behalf.

To create the in-application stream, you need to specify a schema to transform your data into a schematized version used in SQL. In the schema, you provide the necessary mapping of the data elements in the streaming source to record columns in the in-app stream.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-kinesisanalytics-application.html#cfn-kinesisanalytics-application-inputs