com.pulumi.awsnative.kinesisanalyticsv2.kotlin.outputs.ApplicationInput.kt Maven / Gradle / Ivy

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@file:Suppress("NAME_SHADOWING", "DEPRECATION")

package com.pulumi.awsnative.kinesisanalyticsv2.kotlin.outputs

import kotlin.String
import kotlin.Suppress

/**
 * When you configure the application input for a SQL-based Kinesis Data Analytics application, you specify the streaming source, the in-application stream name that is created, and the mapping between the two.
 * @property inputParallelism Describes the number of in-application streams to create.
 * @property inputProcessingConfiguration The InputProcessingConfiguration 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 InputLambdaProcessor.
 * @property inputSchema 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.
 * @property kinesisFirehoseInput If the streaming source is an Amazon Kinesis Data Firehose delivery stream, identifies the delivery stream's ARN.
 * @property kinesisStreamsInput If the streaming source is an Amazon Kinesis data stream, identifies the stream's Amazon Resource Name (ARN).
 * @property namePrefix The name prefix to use when creating an in-application stream. Suppose that you specify a prefix `"MyInApplicationStream"`. Kinesis Data Analytics then creates one or more (as per the InputParallelism count you specified) in-application streams with the names `"MyInApplicationStream_001"`, `"MyInApplicationStream_002"`, and so on.
 */
public data class ApplicationInput(
    public val inputParallelism: ApplicationInputParallelism? = null,
    public val inputProcessingConfiguration: ApplicationInputProcessingConfiguration? = null,
    public val inputSchema: ApplicationInputSchema,
    public val kinesisFirehoseInput: ApplicationKinesisFirehoseInput? = null,
    public val kinesisStreamsInput: ApplicationKinesisStreamsInput? = null,
    public val namePrefix: String,
) {
    public companion object {
        public fun toKotlin(javaType: com.pulumi.awsnative.kinesisanalyticsv2.outputs.ApplicationInput): ApplicationInput = ApplicationInput(
            inputParallelism = javaType.inputParallelism().map({ args0 ->
                args0.let({ args0 ->
                    com.pulumi.awsnative.kinesisanalyticsv2.kotlin.outputs.ApplicationInputParallelism.Companion.toKotlin(args0)
                })
            }).orElse(null),
            inputProcessingConfiguration = javaType.inputProcessingConfiguration().map({ args0 ->
                args0.let({ args0 ->
                    com.pulumi.awsnative.kinesisanalyticsv2.kotlin.outputs.ApplicationInputProcessingConfiguration.Companion.toKotlin(args0)
                })
            }).orElse(null),
            inputSchema = javaType.inputSchema().let({ args0 ->
                com.pulumi.awsnative.kinesisanalyticsv2.kotlin.outputs.ApplicationInputSchema.Companion.toKotlin(args0)
            }),
            kinesisFirehoseInput = javaType.kinesisFirehoseInput().map({ args0 ->
                args0.let({ args0 ->
                    com.pulumi.awsnative.kinesisanalyticsv2.kotlin.outputs.ApplicationKinesisFirehoseInput.Companion.toKotlin(args0)
                })
            }).orElse(null),
            kinesisStreamsInput = javaType.kinesisStreamsInput().map({ args0 ->
                args0.let({ args0 ->
                    com.pulumi.awsnative.kinesisanalyticsv2.kotlin.outputs.ApplicationKinesisStreamsInput.Companion.toKotlin(args0)
                })
            }).orElse(null),
            namePrefix = javaType.namePrefix(),
        )
    }
}