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org.apache.spark.h2o.utils

H2OSchemaUtils

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object H2OSchemaUtils

Utilities for working with Spark SQL component.

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  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. val ARRAY_TYPE: Byte

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  5. val NORMAL_TYPE: Byte

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  6. val VEC_TYPE: Byte

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  7. final def asInstanceOf[T0]: T0

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  9. def collectArrayLikeTypes(fields: Seq[StructField], path: Seq[Int] = Seq()): Seq[Seq[Int]]

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  10. def collectStringTypesIndx(fields: Seq[StructField], path: Seq[Int] = Seq()): Seq[Seq[Int]]

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    Collect all StringType indexes in give list representing schema

  11. def collectTypeIndx(fields: Seq[StructField], path: Seq[Int] = Seq()): Seq[Seq[Int]]

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  12. def collectVectorLikeTypes(fields: Seq[StructField], path: Seq[Int] = Seq()): Seq[Seq[Int]]

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  13. def createSchema[T <: Frame](f: T, copyMetadata: Boolean): StructType

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

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  15. def equals(arg0: Any): Boolean

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  16. def expandedSchema(sc: SparkContext, srdd: DataFrame): Seq[(Seq[Int], StructField, Byte)]

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    Returns expanded schema

    Returns expanded schema

    • schema is represented as list of types and its position inside row
    • all arrays are expanded into columns based on the longest one
    • all vectors are expanded into columns
    sc

    actual Spark context

    srdd

    schema-based RDD

    returns

    list of types with their positions

  17. def finalize(): Unit

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  18. def flatSchema(s: StructType, typeName: Option[String] = None, nullable: Boolean = false): Seq[StructField]

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    Return flattenized type - recursively transforms StrucType into Seq of encapsulated types.

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

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

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