commonMain.aws.sdk.kotlin.services.glue.model.SparkSql.kt Maven / Gradle / Ivy
// Code generated by smithy-kotlin-codegen. DO NOT EDIT!
package aws.sdk.kotlin.services.glue.model
import aws.smithy.kotlin.runtime.SdkDsl
/**
* Specifies a transform where you enter a SQL query using Spark SQL syntax to transform the data. The output is a single `DynamicFrame`.
*/
public class SparkSql private constructor(builder: Builder) {
/**
* The data inputs identified by their node names. You can associate a table name with each input node to use in the SQL query. The name you choose must meet the Spark SQL naming restrictions.
*/
public val inputs: List = requireNotNull(builder.inputs) { "A non-null value must be provided for inputs" }
/**
* The name of the transform node.
*/
public val name: kotlin.String = requireNotNull(builder.name) { "A non-null value must be provided for name" }
/**
* Specifies the data schema for the SparkSQL transform.
*/
public val outputSchemas: List? = builder.outputSchemas
/**
* A list of aliases. An alias allows you to specify what name to use in the SQL for a given input. For example, you have a datasource named "MyDataSource". If you specify `From` as MyDataSource, and `Alias` as SqlName, then in your SQL you can do:
*
* `select * from SqlName`
*
* and that gets data from MyDataSource.
*/
public val sqlAliases: List = requireNotNull(builder.sqlAliases) { "A non-null value must be provided for sqlAliases" }
/**
* A SQL query that must use Spark SQL syntax and return a single data set.
*/
public val sqlQuery: kotlin.String = requireNotNull(builder.sqlQuery) { "A non-null value must be provided for sqlQuery" }
public companion object {
public operator fun invoke(block: Builder.() -> kotlin.Unit): aws.sdk.kotlin.services.glue.model.SparkSql = Builder().apply(block).build()
}
override fun toString(): kotlin.String = buildString {
append("SparkSql(")
append("inputs=$inputs,")
append("name=$name,")
append("outputSchemas=$outputSchemas,")
append("sqlAliases=$sqlAliases,")
append("sqlQuery=$sqlQuery")
append(")")
}
override fun hashCode(): kotlin.Int {
var result = inputs.hashCode()
result = 31 * result + (name.hashCode())
result = 31 * result + (outputSchemas?.hashCode() ?: 0)
result = 31 * result + (sqlAliases.hashCode())
result = 31 * result + (sqlQuery.hashCode())
return result
}
override fun equals(other: kotlin.Any?): kotlin.Boolean {
if (this === other) return true
if (other == null || this::class != other::class) return false
other as SparkSql
if (inputs != other.inputs) return false
if (name != other.name) return false
if (outputSchemas != other.outputSchemas) return false
if (sqlAliases != other.sqlAliases) return false
if (sqlQuery != other.sqlQuery) return false
return true
}
public inline fun copy(block: Builder.() -> kotlin.Unit = {}): aws.sdk.kotlin.services.glue.model.SparkSql = Builder(this).apply(block).build()
@SdkDsl
public class Builder {
/**
* The data inputs identified by their node names. You can associate a table name with each input node to use in the SQL query. The name you choose must meet the Spark SQL naming restrictions.
*/
public var inputs: List? = null
/**
* The name of the transform node.
*/
public var name: kotlin.String? = null
/**
* Specifies the data schema for the SparkSQL transform.
*/
public var outputSchemas: List? = null
/**
* A list of aliases. An alias allows you to specify what name to use in the SQL for a given input. For example, you have a datasource named "MyDataSource". If you specify `From` as MyDataSource, and `Alias` as SqlName, then in your SQL you can do:
*
* `select * from SqlName`
*
* and that gets data from MyDataSource.
*/
public var sqlAliases: List? = null
/**
* A SQL query that must use Spark SQL syntax and return a single data set.
*/
public var sqlQuery: kotlin.String? = null
@PublishedApi
internal constructor()
@PublishedApi
internal constructor(x: aws.sdk.kotlin.services.glue.model.SparkSql) : this() {
this.inputs = x.inputs
this.name = x.name
this.outputSchemas = x.outputSchemas
this.sqlAliases = x.sqlAliases
this.sqlQuery = x.sqlQuery
}
@PublishedApi
internal fun build(): aws.sdk.kotlin.services.glue.model.SparkSql = SparkSql(this)
internal fun correctErrors(): Builder {
if (inputs == null) inputs = emptyList()
if (name == null) name = ""
if (sqlAliases == null) sqlAliases = emptyList()
if (sqlQuery == null) sqlQuery = ""
return this
}
}
}