io.cloudshiftdev.awscdk.services.kinesisanalytics.CfnApplicationProps.kt Maven / Gradle / Ivy
@file:Suppress("RedundantVisibilityModifier","RedundantUnitReturnType","RemoveRedundantQualifierName","unused","UnusedImport","ClassName","REDUNDANT_PROJECTION","DEPRECATION")
package io.cloudshiftdev.awscdk.services.kinesisanalytics
import io.cloudshiftdev.awscdk.IResolvable
import io.cloudshiftdev.awscdk.common.CdkDslMarker
import io.cloudshiftdev.awscdk.common.CdkObject
import io.cloudshiftdev.awscdk.common.CdkObjectWrappers
import kotlin.Any
import kotlin.String
import kotlin.Unit
import kotlin.collections.List
/**
* Properties for defining a `CfnApplication`.
*
* Example:
*
* ```
* // The code below shows an example of how to instantiate this type.
* // The values are placeholders you should change.
* import io.cloudshiftdev.awscdk.services.kinesisanalytics.*;
* CfnApplicationProps cfnApplicationProps = CfnApplicationProps.builder()
* .inputs(List.of(InputProperty.builder()
* .inputSchema(InputSchemaProperty.builder()
* .recordColumns(List.of(RecordColumnProperty.builder()
* .name("name")
* .sqlType("sqlType")
* // the properties below are optional
* .mapping("mapping")
* .build()))
* .recordFormat(RecordFormatProperty.builder()
* .recordFormatType("recordFormatType")
* // the properties below are optional
* .mappingParameters(MappingParametersProperty.builder()
* .csvMappingParameters(CSVMappingParametersProperty.builder()
* .recordColumnDelimiter("recordColumnDelimiter")
* .recordRowDelimiter("recordRowDelimiter")
* .build())
* .jsonMappingParameters(JSONMappingParametersProperty.builder()
* .recordRowPath("recordRowPath")
* .build())
* .build())
* .build())
* // the properties below are optional
* .recordEncoding("recordEncoding")
* .build())
* .namePrefix("namePrefix")
* // the properties below are optional
* .inputParallelism(InputParallelismProperty.builder()
* .count(123)
* .build())
* .inputProcessingConfiguration(InputProcessingConfigurationProperty.builder()
* .inputLambdaProcessor(InputLambdaProcessorProperty.builder()
* .resourceArn("resourceArn")
* .roleArn("roleArn")
* .build())
* .build())
* .kinesisFirehoseInput(KinesisFirehoseInputProperty.builder()
* .resourceArn("resourceArn")
* .roleArn("roleArn")
* .build())
* .kinesisStreamsInput(KinesisStreamsInputProperty.builder()
* .resourceArn("resourceArn")
* .roleArn("roleArn")
* .build())
* .build()))
* // the properties below are optional
* .applicationCode("applicationCode")
* .applicationDescription("applicationDescription")
* .applicationName("applicationName")
* .build();
* ```
*
* [Documentation](http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-kinesisanalytics-application.html)
*/
public interface CfnApplicationProps {
/**
* 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](https://docs.aws.amazon.com/kinesisanalytics/latest/dev/how-it-works-app-code.html) .
*
* 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.
*
* [Documentation](http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-kinesisanalytics-application.html#cfn-kinesisanalytics-application-applicationcode)
*/
public fun applicationCode(): String? = unwrap(this).getApplicationCode()
/**
* Summary description of the application.
*
* [Documentation](http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-kinesisanalytics-application.html#cfn-kinesisanalytics-application-applicationdescription)
*/
public fun applicationDescription(): String? = unwrap(this).getApplicationDescription()
/**
* Name of your Amazon Kinesis Analytics application (for example, `sample-app` ).
*
* [Documentation](http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-kinesisanalytics-application.html#cfn-kinesisanalytics-application-applicationname)
*/
public fun applicationName(): String? = unwrap(this).getApplicationName()
/**
* 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.
*
* [Documentation](http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-kinesisanalytics-application.html#cfn-kinesisanalytics-application-inputs)
*/
public fun inputs(): Any
/**
* A builder for [CfnApplicationProps]
*/
@CdkDslMarker
public interface Builder {
/**
* @param applicationCode 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](https://docs.aws.amazon.com/kinesisanalytics/latest/dev/how-it-works-app-code.html) .
*
* 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.
*/
public fun applicationCode(applicationCode: String)
/**
* @param applicationDescription Summary description of the application.
*/
public fun applicationDescription(applicationDescription: String)
/**
* @param applicationName Name of your Amazon Kinesis Analytics application (for example,
* `sample-app` ).
*/
public fun applicationName(applicationName: String)
/**
* @param 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.
*/
public fun inputs(inputs: IResolvable)
/**
* @param 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.
*/
public fun inputs(inputs: List)
/**
* @param 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.
*/
public fun inputs(vararg inputs: Any)
}
private class BuilderImpl : Builder {
private val cdkBuilder:
software.amazon.awscdk.services.kinesisanalytics.CfnApplicationProps.Builder =
software.amazon.awscdk.services.kinesisanalytics.CfnApplicationProps.builder()
/**
* @param applicationCode 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](https://docs.aws.amazon.com/kinesisanalytics/latest/dev/how-it-works-app-code.html) .
*
* 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.
*/
override fun applicationCode(applicationCode: String) {
cdkBuilder.applicationCode(applicationCode)
}
/**
* @param applicationDescription Summary description of the application.
*/
override fun applicationDescription(applicationDescription: String) {
cdkBuilder.applicationDescription(applicationDescription)
}
/**
* @param applicationName Name of your Amazon Kinesis Analytics application (for example,
* `sample-app` ).
*/
override fun applicationName(applicationName: String) {
cdkBuilder.applicationName(applicationName)
}
/**
* @param 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.
*/
override fun inputs(inputs: IResolvable) {
cdkBuilder.inputs(inputs.let(IResolvable.Companion::unwrap))
}
/**
* @param 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.
*/
override fun inputs(inputs: List) {
cdkBuilder.inputs(inputs.map{CdkObjectWrappers.unwrap(it)})
}
/**
* @param 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.
*/
override fun inputs(vararg inputs: Any): Unit = inputs(inputs.toList())
public fun build(): software.amazon.awscdk.services.kinesisanalytics.CfnApplicationProps =
cdkBuilder.build()
}
private class Wrapper(
cdkObject: software.amazon.awscdk.services.kinesisanalytics.CfnApplicationProps,
) : CdkObject(cdkObject),
CfnApplicationProps {
/**
* 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](https://docs.aws.amazon.com/kinesisanalytics/latest/dev/how-it-works-app-code.html) .
*
* 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.
*
* [Documentation](http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-kinesisanalytics-application.html#cfn-kinesisanalytics-application-applicationcode)
*/
override fun applicationCode(): String? = unwrap(this).getApplicationCode()
/**
* Summary description of the application.
*
* [Documentation](http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-kinesisanalytics-application.html#cfn-kinesisanalytics-application-applicationdescription)
*/
override fun applicationDescription(): String? = unwrap(this).getApplicationDescription()
/**
* Name of your Amazon Kinesis Analytics application (for example, `sample-app` ).
*
* [Documentation](http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-kinesisanalytics-application.html#cfn-kinesisanalytics-application-applicationname)
*/
override fun applicationName(): String? = unwrap(this).getApplicationName()
/**
* 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.
*
* [Documentation](http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-kinesisanalytics-application.html#cfn-kinesisanalytics-application-inputs)
*/
override fun inputs(): Any = unwrap(this).getInputs()
}
public companion object {
public operator fun invoke(block: Builder.() -> Unit = {}): CfnApplicationProps {
val builderImpl = BuilderImpl()
return Wrapper(builderImpl.apply(block).build())
}
internal
fun wrap(cdkObject: software.amazon.awscdk.services.kinesisanalytics.CfnApplicationProps):
CfnApplicationProps = CdkObjectWrappers.wrap(cdkObject) as? CfnApplicationProps ?:
Wrapper(cdkObject)
internal fun unwrap(wrapped: CfnApplicationProps):
software.amazon.awscdk.services.kinesisanalytics.CfnApplicationProps = (wrapped as
CdkObject).cdkObject as software.amazon.awscdk.services.kinesisanalytics.CfnApplicationProps
}
}
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