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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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  6. def collectArrayLikeTypes(flatSchema: StructType): Seq[Int]

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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(sc: SparkContext, 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(sc: SparkContext, 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
    sc

    Spark context

    flatDataFrame

    flat data frame

    elemMaxSizes

    max sizes of each element in the dataframe

    returns

    list of types with their positions

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

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

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

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

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  14. def equals(arg0: Any): Boolean

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  15. def expandWithoutVectors(sc: SparkContext, flatSchema: StructType, elemMaxSizes: Array[Int]): Seq[StructField]

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  16. def expandedSchema(sc: SparkContext, 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
    sc

    Spark context

    flatSchema

    flat schema of spark data frame

    returns

    list of types with their positions

  17. def finalize(): Unit

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  18. def flattenDataFrame(df: DataFrame): DataFrame

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  19. def flattenSchema(schema: StructType, prefix: String = null, nullable: Boolean = false): StructType

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