org.apache.spark.sql.catalyst.plans.logical.statsEstimation

EstimationUtils

object EstimationUtils

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  1. final def !=(arg0: AnyRef): Boolean

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. final def asInstanceOf[T0]: T0

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  7. def ceil(bigDecimal: BigDecimal): BigInt

  8. def clone(): AnyRef

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    @throws( ... )
  9. def columnStatsExist(statsAndAttr: (Statistics, Attribute)*): Boolean

    Check if each attribute has column stat in the corresponding statistics.

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

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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  13. def fromDecimal(dec: Decimal, dataType: DataType): Any

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

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  15. def getOutputMap(inputMap: AttributeMap[ColumnStat], output: Seq[Attribute]): AttributeMap[ColumnStat]

    Get column stats for output attributes.

  16. def getOutputSize(attributes: Seq[Attribute], outputRowCount: BigInt, attrStats: AttributeMap[ColumnStat] = AttributeMap(Nil)): BigInt

  17. def hashCode(): Int

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  18. final def isInstanceOf[T0]: Boolean

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

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  20. final def notify(): Unit

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  21. final def notifyAll(): Unit

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  22. def nullColumnStat(dataType: DataType, rowCount: BigInt): ColumnStat

  23. def rowCountsExist(conf: SQLConf, plans: LogicalPlan*): Boolean

    Check if each plan has rowCount in its statistics.

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

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  25. def toDecimal(value: Any, dataType: DataType): Decimal

    For simplicity we use Decimal to unify operations for data types whose min/max values can be represented as numbers, e.

    For simplicity we use Decimal to unify operations for data types whose min/max values can be represented as numbers, e.g. Boolean can be represented as 0 (false) or 1 (true). The two methods below are the contract of conversion.

  26. def toString(): String

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  27. def updateNdv(oldNumRows: BigInt, newNumRows: BigInt, oldNdv: BigInt): BigInt

    Updates (scales down) the number of distinct values if the number of rows decreases after some operation (such as filter, join).

    Updates (scales down) the number of distinct values if the number of rows decreases after some operation (such as filter, join). Otherwise keep it unchanged.

  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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