Object

com.actian.spark_vector.vector

PredicatePushdown

Related Doc: package vector

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object PredicatePushdown extends Logging

Object that brings together a set of functions that are used for implementing "Predicate Pushdown": A Vector query such as "SELECT * from external_table where col = 10" should not result in the provider sending the whole table to Vector, but only those tuples that pass the "col = 10" predicate.

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Type Members

  1. type ValueRange = Seq[String]

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    Textual representation of single value ranges for a particular column, consisting of a list of Strings.

    Textual representation of single value ranges for a particular column, consisting of a list of Strings.

    First element denotes the type of range, which can currently be of two types:

    • Bounded ranges: intervals with inclusive/exclusive lower and upper bounds (e.g. "()", "(]", "[)", "[]")
      • For this type of intervals, the second and third strings in the list represent the lower and upper bounds.
    • Unbounded ranges: intervals for which either the lower or the upper bound is (-)infinity (e.g. ">", ">=", "<=", "<")
      • For this types of intervals, the list is of size two and the second string represents the only bound
      • Note: We also include here ("==", x) as a shorter notation for ("[]", x, x)

  2. type ValueRanges = Seq[ValueRange]

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    A ValueRanges object is a sequence of ValueRange elements and it is associated with a certain column.

    A ValueRanges object is a sequence of ValueRange elements and it is associated with a certain column. As such, all string bounds of all the ValueRanges are expected to be literals of that column's VectorDataType.

    The interpretation is that the individual ranges are union-ed (or-ed) together.

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 applyFilters(df: DataFrame, columns: Seq[ColumnMetadata], sparkContext: SparkContext): DataFrame

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    Filters the given df according to any ValueRanges that its columns might have.

  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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

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  8. def equals(arg0: Any): Boolean

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  9. def filterForColumn(columnMetadata: ColumnMetadata, sparkContext: SparkContext): Option[Column]

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    Creates Some(Column) that represents the condition ("predicate"), in Spark's terms, corresponding to the given columnMetadata, if the latter has some ValueRanges specified and is of a type for which Predicate Pushdown is safe.

    Creates Some(Column) that represents the condition ("predicate"), in Spark's terms, corresponding to the given columnMetadata, if the latter has some ValueRanges specified and is of a type for which Predicate Pushdown is safe. Otherwise returns None.

    See also

    typeIsSafeForPredPD

  10. def filterForColumn(column: Column, litToConstCol: (String) ⇒ Column, range: ValueRange): Column

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    Creates a Column that represents the condition ("predicate"), in Spark's terms, that is expressed by the given range

  11. def finalize(): Unit

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

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  13. def hashCode(): Int

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  14. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Logging

  15. final def isInstanceOf[T0]: Boolean

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  16. def isTraceEnabled(): Boolean

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  17. def log: Logger

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  18. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  19. def logDebug(msg: ⇒ String): Unit

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  20. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  21. def logError(msg: ⇒ String): Unit

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  22. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  23. def logInfo(msg: ⇒ String): Unit

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  24. def logName: String

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  25. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  26. def logTrace(msg: ⇒ String): Unit

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  27. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  28. def logWarning(msg: ⇒ String): Unit

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

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  30. final def notify(): Unit

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  31. final def notifyAll(): Unit

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

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  33. def toString(): String

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  34. def typeIsSafeForPredPD(vectorTypeName: String): Boolean

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    Says whether or not Predicate Pushdown is supported on columns of the given vectorTypeName.

  35. def typeIsSafeForPredPD(vectorDataType: VectorTypeEnum): Boolean

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    Says whether or not Predicate Pushdown is supported on columns of the given vectorDataType.

  36. def vectorLiteralToSparkLiteral(vectorLiteral: String, vectorDataType: VectorTypeEnum, sparkContext: SparkContext): String

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    Converts the given vectorLiteral to the corresponding Spark literal, according to the specified VectorDataType.

  37. final def wait(): Unit

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

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

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