org.apache.spark.sql.execution.datasources

TextBasedFileFormat

abstract class TextBasedFileFormat extends FileFormat

The base class file format that is based on text file.

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FileFormat, AnyRef, Any
Known Subclasses
CSVFileFormat, JsonFileFormat, TextFileFormat
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Instance Constructors

  1. new TextBasedFileFormat()

Abstract Value Members

  1. abstract 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
    FileFormat

  2. abstract 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
    FileFormat

Concrete Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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

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  6. final def asInstanceOf[T0]: T0

    Definition Classes
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  7. 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.

    returns

    Attributes
    protected
    Definition Classes
    FileFormat

  8. 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

  9. def clone(): AnyRef

    Attributes
    protected[java.lang]
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    @throws( ... )

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

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  16. 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

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

    Definition Classes
    AnyRef

  18. final def notify(): Unit

    Definition Classes
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  19. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  20. 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

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

    Definition Classes
    AnyRef

  22. def toString(): String

    Definition Classes
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  23. final def wait(): Unit

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

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

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Inherited from FileFormat

Inherited from AnyRef

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