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com.holdenkarau.spark.testing

DatasetGenerator

object DatasetGenerator

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  4. def arbitraryDataset[T](sqlCtx: SQLContext, minPartitions: Int = 1)(generator: ⇒ Gen[T])(implicit arg0: ClassTag[T], arg1: Encoder[T]): Arbitrary[Dataset[T]]

    Generate an Dataset Generator of the desired type.

    Generate an Dataset Generator of the desired type. Attempt to try different number of partitions so as to catch problems with empty partitions, etc. minPartitions defaults to 1, but when generating data too large for a single machine, choose a larger value.

    sqlCtx

    Spark sql Context

    minPartitions

    defaults to 1

    generator

    used to create the generator. This function will be used to create the generator as many times as required.

  5. def arbitrarySizedDataset[T](sqlCtx: SQLContext, minPartitions: Int = 1)(generator: (Int) ⇒ Gen[T])(implicit arg0: ClassTag[T], arg1: Encoder[T]): Arbitrary[Dataset[T]]

    Generate an Dataset Generator of the desired type.

    Generate an Dataset Generator of the desired type. Attempt to try different number of partitions so as to catch problems with empty partitions, etc. minPartitions defaults to 1, but when generating data too large for a single machine, choose a larger value.

    sqlCtx

    Spark sql Context

    minPartitions

    defaults to 1

    generator

    used to create the generator. This function will be used to create the generator as many times as required.

  6. final def asInstanceOf[T0]: T0
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  7. def clone(): AnyRef
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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 genDataset[T](sqlCtx: SQLContext, minPartitions: Int = 1)(generator: ⇒ Gen[T])(implicit arg0: ClassTag[T], arg1: Encoder[T]): Gen[Dataset[T]]

    Generate an Dataset Generator of the desired type.

    Generate an Dataset Generator of the desired type. Attempt to try different number of partitions so as to catch problems with empty partitions, etc. minPartitions defaults to 1, but when generating data too large for a single machine, choose a larger value.

    sqlCtx

    Spark sql Context

    minPartitions

    defaults to 1

    generator

    used to create the generator. This function will be used to create the generator as many times as required.

  11. def genSizedDataset[T](sqlCtx: SQLContext, minPartitions: Int = 1)(generator: (Int) ⇒ Gen[T])(implicit arg0: ClassTag[T], arg1: Encoder[T]): Gen[Dataset[T]]

    Generate an Dataset Generator of the desired type with its size accessible.

    Generate an Dataset Generator of the desired type with its size accessible. Attempt to try different number of partitions so as to catch problems with empty partitions, etc. minPartitions defaults to 1, but when generating data too large for a single machine, choose a larger value.

    sqlCtx

    Spark sql Context

    minPartitions

    defaults to 1

    generator

    used to create the generator. This function will be used to create the generator as many times as required.

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