case class GpuDataSource(sparkSession: SparkSession, className: String, paths: Seq[String] = Nil, userSpecifiedSchema: Option[StructType] = None, partitionColumns: Seq[String] = Seq.empty, bucketSpec: Option[BucketSpec] = None, options: Map[String, String] = Map.empty, catalogTable: Option[CatalogTable] = None, origProvider: Class[_], gpuFileFormat: ColumnarFileFormat) extends GpuDataSourceBase with Product with Serializable
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Instance Constructors
- new GpuDataSource(sparkSession: SparkSession, className: String, paths: Seq[String] = Nil, userSpecifiedSchema: Option[StructType] = None, partitionColumns: Seq[String] = Seq.empty, bucketSpec: Option[BucketSpec] = None, options: Map[String, String] = Map.empty, catalogTable: Option[CatalogTable] = None, origProvider: Class[_], gpuFileFormat: ColumnarFileFormat)
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asInstanceOf[T0]: T0
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- val bucketSpec: Option[BucketSpec]
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val
caseInsensitiveOptions: CaseInsensitiveMap[String]
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- GpuDataSourceBase
- val catalogTable: Option[CatalogTable]
- val className: String
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equality: Resolver
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finalize(): Unit
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def
globPaths: Boolean
Whether or not paths should be globbed before being used to access files.
Whether or not paths should be globbed before being used to access files.
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- GpuDataSourceBase
- val gpuFileFormat: ColumnarFileFormat
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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ne(arg0: AnyRef): Boolean
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def
newHadoopConfiguration(): Configuration
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notify(): Unit
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- val options: Map[String, String]
- val origProvider: Class[_]
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def
originalProvidingInstance(): Any
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- GpuDataSourceBase
- val partitionColumns: Seq[String]
- val paths: Seq[String]
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def
resolveRelation(checkFilesExist: Boolean = true): BaseRelation
Create a resolved
BaseRelationthat can be used to read data from or write data into thisDataSourceCreate a resolved
BaseRelationthat can be used to read data from or write data into thisDataSource- checkFilesExist
Whether to confirm that the files exist when generating the non-streaming file based datasource. StructuredStreaming jobs already list file existence, and when generating incremental jobs, the batch is considered as a non-streaming file based data source. Since we know that files already exist, we don't need to check them again.
- Definition Classes
- GpuDataSourceBase
- val sparkSession: SparkSession
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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- val userSpecifiedSchema: Option[StructType]
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def
writeAndRead(mode: SaveMode, data: LogicalPlan, outputColumnNames: Seq[String], physicalPlan: SparkPlan, useStableSort: Boolean, concurrentWriterPartitionFlushSize: Long): BaseRelation
Writes the given
LogicalPlanout to thisDataSourceand returns aBaseRelationfor the following reading.Writes the given
LogicalPlanout to thisDataSourceand returns aBaseRelationfor the following reading.- mode
The save mode for this writing.
- data
The input query plan that produces the data to be written. Note that this plan is analyzed and optimized.
- outputColumnNames
The original output column names of the input query plan. The optimizer may not preserve the output column's names' case, so we need this parameter instead of
data.output.- physicalPlan
The physical plan of the input query plan. We should run the writing command with this physical plan instead of creating a new physical plan, so that the metrics can be correctly linked to the given physical plan and shown in the web UI.