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

   TextFileFormat → 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[lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... ) @native()

8.  def createFileMetadataCol(): AttributeReference

   Create a file metadata struct column containing fields supported by the given file format.

   Create a file metadata struct column containing fields supported by the given file format.

   Definition Classes

   FileFormat

9.  final def eq(arg0: AnyRef): Boolean

   Definition Classes

   AnyRef

10.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

11.  def fileConstantMetadataExtractors: Map[String, (PartitionedFile) ⇒ Any]

    The extractors to use when deriving file-constant metadata columns for this file format.

    The extractors to use when deriving file-constant metadata columns for this file format.

    Implementations that define custom constant metadata columns can override this method to associate a custom extractor with a given metadata column name, when a simple name-based lookup in PartitionedFile.extraConstantMetadataColumnValues is not expressive enough; extractors have access to the entire PartitionedFile and can perform arbitrary computations.

    NOTE: Extractors are lazy, invoked only if the query actually selects their column at runtime.

    See also FileFormat.getFileConstantMetadataColumnValue.

    Definition Classes

    FileFormat

12.  def finalize(): Unit

    Attributes

    protected[lang]

    Definition Classes

    AnyRef

    Annotations

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

13.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

14.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

15.  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

    TextFileFormat → FileFormat

16.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

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

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

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

    Definition Classes

    TextFileFormat → TextBasedFileFormat → FileFormat

18.  def metadataSchemaFields: Seq[StructField]

    All fields the file format's _metadata struct defines.

    All fields the file format's _metadata struct defines.

    Each metadata struct field is either "constant" or "generated" (respectively defined/matched by FileSourceConstantMetadataStructField or FileSourceGeneratedMetadataAttribute).

    Constant metadata columns are derived from the PartitionedFile instances a scan's FileIndex provides. Thus, a custom FileFormat that defines constant metadata columns will generally pair with a a custom FileIndex that populates PartitionedFile with appropriate metadata values. By default, constant attribute values are obtained by a simple name-based lookup in PartitionedFile.extraConstantMetadataColumnValues, but implementations can override fileConstantMetadataExtractors to define custom extractors that have access to the entire PartitionedFile when deriving the column's value.

    Generated metadata columns map to a hidden/internal column the underlying reader provides, and so will often pair with a custom reader that can populate those columns. For example, ParquetFileFormat defines a "_metadata.row_index" column that relies on VectorizedParquetRecordReader to extract the actual row index values from the parquet scan.

    Definition Classes

    FileFormat

19.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

20.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

21.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

22.  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

    TextFileFormat → FileFormat

23.  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

    TextFileFormat → DataSourceRegister

    Since

    1.5.0

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

    Returns whether this format supports returning columnar batch or not.

    Returns whether this format supports returning columnar batch or not. If columnar batch output is requested, users shall supply FileFormat.OPTION_RETURNING_BATCH -> true in relation options when calling buildReaderWithPartitionValues. This should only be passed as true if it can actually be supported. For ParquetFileFormat and OrcFileFormat, passing this option is required.

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

    Definition Classes

    FileFormat

25.  def supportDataType(dataType: DataType): Boolean

    Returns whether this format supports the given DataType in read/write path.

    Returns whether this format supports the given DataType in read/write path. By default all data types are supported.

    Definition Classes

    TextFileFormat → FileFormat

26.  def supportFieldName(name: String): Boolean

    Returns whether this format supports the given filed name in read/write path.

    Returns whether this format supports the given filed name in read/write path. By default all field name is supported.

    Definition Classes

    FileFormat

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

    Definition Classes

    AnyRef

28.  def toString(): String

    Definition Classes

    TextFileFormat → AnyRef → Any

29.  def vectorTypes(requiredSchema: StructType, partitionSchema: StructType, sqlConf: SQLConf): Option[Seq[String]]

    Returns concrete column vector class names for each column to be used in a columnar batch if this format supports returning columnar batch.

    Returns concrete column vector class names for each column to be used in a columnar batch if this format supports returning columnar batch.

    Definition Classes

    FileFormat

30.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

32.  final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... ) @native()