Class

org.apache.spark.sql.execution.datasources

TextBasedFileFormat

Related Doc: package datasources

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abstract class TextBasedFileFormat extends FileFormat

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

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Instance Constructors

  1. new TextBasedFileFormat()

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Abstract Value Members

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

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    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

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    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: Any): Boolean

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  2. final def ##(): Int

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

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  4. final def asInstanceOf[T0]: T0

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

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

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    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

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    Attributes
    protected[java.lang]
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  8. final def eq(arg0: AnyRef): Boolean

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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

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  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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

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

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

    Definition Classes
    TextBasedFileFormatFileFormat
  15. final def ne(arg0: AnyRef): Boolean

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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

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

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  20. def toString(): String

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  21. final def wait(): Unit

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

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

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Inherited from FileFormat

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