Interface ICfnApplicationProps
Properties for defining a CfnApplication
.
Namespace: Amazon.CDK.AWS.KinesisAnalytics
Assembly: Amazon.CDK.Lib.dll
Syntax (csharp)
public interface ICfnApplicationProps
Syntax (vb)
Public Interface ICfnApplicationProps
Remarks
ExampleMetadata: fixture=_generated
Examples
// The code below shows an example of how to instantiate this type.
// The values are placeholders you should change.
using Amazon.CDK.AWS.KinesisAnalytics;
var cfnApplicationProps = new CfnApplicationProps {
Inputs = new [] { new InputProperty {
InputSchema = new InputSchemaProperty {
RecordColumns = new [] { new RecordColumnProperty {
Name = "name",
SqlType = "sqlType",
// the properties below are optional
Mapping = "mapping"
} },
RecordFormat = new RecordFormatProperty {
RecordFormatType = "recordFormatType",
// the properties below are optional
MappingParameters = new MappingParametersProperty {
CsvMappingParameters = new CSVMappingParametersProperty {
RecordColumnDelimiter = "recordColumnDelimiter",
RecordRowDelimiter = "recordRowDelimiter"
},
JsonMappingParameters = new JSONMappingParametersProperty {
RecordRowPath = "recordRowPath"
}
}
},
// the properties below are optional
RecordEncoding = "recordEncoding"
},
NamePrefix = "namePrefix",
// the properties below are optional
InputParallelism = new InputParallelismProperty {
Count = 123
},
InputProcessingConfiguration = new InputProcessingConfigurationProperty {
InputLambdaProcessor = new InputLambdaProcessorProperty {
ResourceArn = "resourceArn",
RoleArn = "roleArn"
}
},
KinesisFirehoseInput = new KinesisFirehoseInputProperty {
ResourceArn = "resourceArn",
RoleArn = "roleArn"
},
KinesisStreamsInput = new KinesisStreamsInputProperty {
ResourceArn = "resourceArn",
RoleArn = "roleArn"
}
} },
// the properties below are optional
ApplicationCode = "applicationCode",
ApplicationDescription = "applicationDescription",
ApplicationName = "applicationName"
};
Synopsis
Properties
ApplicationCode | One or more SQL statements that read input data, transform it, and generate output. |
ApplicationDescription | Summary description of the application. |
ApplicationName | Name of your Amazon Kinesis Analytics application (for example, |
Inputs | Use this parameter to configure the application input. |
Properties
ApplicationCode
One or more SQL statements that read input data, transform it, and generate output.
virtual string ApplicationCode { get; }
Property Value
System.String
Remarks
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.
ApplicationDescription
Summary description of the application.
virtual string ApplicationDescription { get; }
Property Value
System.String
Remarks
ApplicationName
Name of your Amazon Kinesis Analytics application (for example, sample-app
).
virtual string ApplicationName { get; }
Property Value
System.String
Remarks
Inputs
Use this parameter to configure the application input.
object Inputs { get; }
Property Value
System.Object
Remarks
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.