H2OSchemaUtils

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. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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

   

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

   

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

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

   

   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]

   

   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]

    

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

    

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

    

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]

    

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

    

    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

    

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

    

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

    

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21. def hashCode(): Int

    

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22. final def isInstanceOf[T0]: Boolean

    

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

    

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24. final def notify(): Unit

    

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25. final def notifyAll(): Unit

    

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

    

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27. def toString(): String

    

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28. final def wait(): Unit

    

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

    

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

    

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