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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. case class FieldWithOrder(field: StructField, order: Iterable[Any]) extends Product with Serializable

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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. def appendFlattenedStructsToDataFrame(df: DataFrame, prefixForNewColumns: String): DataFrame

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  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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  7. def collectElemStartPositions(maxElemSizes: Array[Int]): Array[Int]

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    Mapping from row in Spark frame to position where to start filling in H2OFrame

  8. def collectMaxElementSizes(flatDataFrame: DataFrame): Array[Int]

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    Collect max size of each element in DataFrame.

    Collect max size of each element in DataFrame. For array -> max array size For vectors -> max vector size For simple types -> 1

    returns

    array containing size of each element

  9. def collectSparseInfo(flatDataFrame: DataFrame, elemMaxSizes: Array[Int]): Array[Boolean]

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    Returns array for all expanded elements with values true/false based on the fact whether the value is sparse vector or not

    Returns array for all expanded elements with values true/false based on the fact whether the value is sparse vector or not

    • schema is represented as list of types
    • all arrays are expanded into columns based on the longest one
    • all vectors are expanded into columns based on the longest one
    flatDataFrame

    flat data frame

    elemMaxSizes

    max sizes of each element in the dataframe

    returns

    list of types with their positions

  10. def collectVectorLikeTypes(flatSchema: StructType): Seq[Int]

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

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

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  13. def equals(arg0: Any): Boolean

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  14. def expandedSchema(flatSchema: StructType, elemMaxSizes: Array[Int]): Seq[StructField]

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    Returns expanded schema

    Returns expanded schema

    • schema is represented as list of types
    • all arrays are expanded into columns based on the longest one
    • all vectors are expanded into columns based on the longest one
    flatSchema

    flat schema of spark data frame

    returns

    list of types with their positions

  15. def finalize(): Unit

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  16. def flattenDataFrame(df: DataFrame, flatSchema: StructType): DataFrame

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  17. def flattenDataFrame(df: DataFrame): DataFrame

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  18. def flattenSchema(df: DataFrame): StructType

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  19. def flattenStructsInDataFrame(df: DataFrame): DataFrame

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  20. def flattenStructsInSchema(schema: StructType, sourceColPrefix: Option[String] = None, targetColPrefix: Option[String] = None, nullable: Boolean = false): Seq[(StructField, String)]

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

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  22. def getDatasetShape(schema: StructType): DatasetShape.DatasetShape

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  23. def hashCode(): Int

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  24. final def isInstanceOf[T0]: Boolean

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

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  26. final def notify(): Unit

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  27. final def notifyAll(): Unit

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  28. def rowsToRowSchemas(df: DataFrame): Dataset[ArrayBuffer[FieldWithOrder]]

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

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  30. def toString(): String

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  31. final def wait(): Unit

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

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

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