CSVFileFormat

Value Members

1. final def !=(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

   AnyRef → Any

3. final def ==(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

4. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

5. def buildReader(sparkSession: SparkSession, dataSchema: StructType, partitionSchema: StructType, requiredSchema: StructType, filters: Seq[Filter], options: Map[String, String], hadoopConf: Configuration): (PartitionedFile) ⇒ Iterator[InternalRow]

   

   Returns a function that can be used to read a single file in as an Iterator of InternalRow.

   Returns a function that can be used to read a single file in as an Iterator of InternalRow.

   dataSchema

   The global data schema. It can be either specified by the user, or reconciled/merged from all underlying data files. If any partition columns are contained in the files, they are preserved in this schema.

   partitionSchema

   The schema of the partition column row that will be present in each PartitionedFile. These columns should be appended to the rows that are produced by the iterator.

   requiredSchema

   The schema of the data that should be output for each row. This may be a subset of the columns that are present in the file if column pruning has occurred.

   filters

   A set of filters than can optionally be used to reduce the number of rows output

   options

   A set of string -> string configuration options.

   Definition Classes

   CSVFileFormat → FileFormat

6. def buildReaderWithPartitionValues(sparkSession: SparkSession, dataSchema: StructType, partitionSchema: StructType, requiredSchema: StructType, filters: Seq[Filter], options: Map[String, String], hadoopConf: Configuration): (PartitionedFile) ⇒ Iterator[InternalRow]

   

   Exactly the same as buildReader except that the reader function returned by this method appends partition values to InternalRows produced by the reader function buildReader returns.

   Exactly the same as buildReader except that the reader function returned by this method appends partition values to InternalRows produced by the reader function buildReader returns.

   Definition Classes

   FileFormat

7. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

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   Annotations

   @throws( ... )

8. final def eq(arg0: AnyRef): Boolean

   

   Definition Classes

   AnyRef

9. def equals(other: Any): Boolean

   

   Definition Classes

   CSVFileFormat → AnyRef → Any

10. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

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    Annotations

    @throws( classOf[java.lang.Throwable] )

11. final def getClass(): Class[_]

    

    Definition Classes

    AnyRef → Any

12. def hashCode(): Int

    

    Definition Classes

    CSVFileFormat → AnyRef → Any

13. def inferSchema(sparkSession: SparkSession, options: Map[String, String], files: Seq[FileStatus]): Option[StructType]

    

    When possible, this method should return the schema of the given files.

    When possible, this method should return the schema of the given files. When the format does not support inference, or no valid files are given should return None. In these cases Spark will require that user specify the schema manually.

    Definition Classes

    CSVFileFormat → FileFormat

14. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

15. def isSplitable(sparkSession: SparkSession, options: Map[String, String], path: Path): Boolean

    

    Returns whether a file with path could be splitted or not.

    Returns whether a file with path could be splitted or not.

    Definition Classes

    TextBasedFileFormat → FileFormat

16. final def ne(arg0: AnyRef): Boolean

    

    Definition Classes

    AnyRef

17. final def notify(): Unit

    

    Definition Classes

    AnyRef

18. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

19. def prepareWrite(sparkSession: SparkSession, job: Job, options: Map[String, String], dataSchema: StructType): OutputWriterFactory

    

    Prepares a write job and returns an OutputWriterFactory.

    Prepares a write job and returns an OutputWriterFactory. Client side job preparation can be put here. For example, user defined output committer can be configured here by setting the output committer class in the conf of spark.sql.sources.outputCommitterClass.

    Definition Classes

    CSVFileFormat → FileFormat

20. def shortName(): String

    

    The string that represents the format that this data source provider uses.

    The string that represents the format that this data source provider uses. This is overridden by children to provide a nice alias for the data source. For example:

    override def shortName(): String = "parquet"

    Definition Classes

    CSVFileFormat → DataSourceRegister

    Since

    1.5.0

21. def supportBatch(sparkSession: SparkSession, dataSchema: StructType): Boolean

    

    Returns whether this format support returning columnar batch or not.

    Returns whether this format support returning columnar batch or not.

    TODO: we should just have different traits for the different formats.

    Definition Classes

    FileFormat

22. final def synchronized[T0](arg0: ⇒ T0): T0

    

    Definition Classes

    AnyRef

23. def toString(): String

    

    Definition Classes

    CSVFileFormat → AnyRef → Any

24. final def wait(): Unit

    

    Definition Classes

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    Annotations

    @throws( ... )

25. final def wait(arg0: Long, arg1: Int): Unit

    

    Definition Classes

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    @throws( ... )

26. final def wait(arg0: Long): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )