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com.holdenkarau.spark.testing

DataframeGenerator

object DataframeGenerator

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  4. def arbitraryDataFrame(sqlContext: SQLContext, schema: StructType, minPartitions: Int = 1): Arbitrary[DataFrame]

    Creates a DataFrame Generator for the given Schema.

    Creates a DataFrame Generator for the given Schema.

    sqlContext

    SQL Context.

    schema

    The required Schema.

    minPartitions

    minimum number of partitions, defaults to 1.

    returns

    Arbitrary DataFrames generator of the required schema.

  5. def arbitraryDataFrameWithCustomFields(sqlContext: SQLContext, schema: StructType, minPartitions: Int = 1)(userGenerators: ColumnGenerator*): Arbitrary[DataFrame]

    Creates a DataFrame Generator for the given Schema, and the given custom generators.

    Creates a DataFrame Generator for the given Schema, and the given custom generators. Custom generators should be specified as a list of: (column index, generator function) tuples.

    Note: The given custom generators should match the required schema, for ex. you can't use Int generator for StringType.

    Note 2: The ColumnGenerator* accepted as userGenerators has changed. ColumnGenerator is now the base class of the accepted generators, users upgrading to 0.6 need to change their calls to use Column. Further explanation can be found in the release notes, and in the class descriptions at the bottom of this file.

    sqlContext

    SQL Context.

    schema

    The required Schema.

    minPartitions

    minimum number of partitions, defaults to 1.

    userGenerators

    custom user generators in the form of: (column index, generator function). where column index starts from 0 to length - 1

    returns

    Arbitrary DataFrames generator of the required schema.

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  11. def getRowGenerator(schema: StructType, customGenerators: Seq[ColumnGenerator]): Gen[Row]

    Creates row generator for the required schema and with user's custom generators.

    Creates row generator for the required schema and with user's custom generators.

    Note: Custom generators should match the required schema, for example you can't use Int generator for StringType.

    schema

    the required Row's schema.

    customGenerators

    user custom generator, this is useful if the you want to control specific columns generation.

    returns

    Gen[Row]

  12. def getRowGenerator(schema: StructType): Gen[Row]

    Creates row generator for the required schema.

    Creates row generator for the required schema.

    schema

    the required Row's schema.

    returns

    Gen[Row]

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