Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

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2.  final def ##: Int

   Definition Classes

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3.  final def ==(arg0: Any): Boolean

   Definition Classes

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4.  final def asInstanceOf[T0]: T0

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5.  def clone(): AnyRef

   Attributes

   protected[lang]

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   @throws(classOf[java.lang.CloneNotSupportedException]) @native()

6.  final def eq(arg0: AnyRef): Boolean

   Definition Classes

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7.  def equals(arg0: AnyRef): Boolean

   Definition Classes

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8.  def finalize(): Unit

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   protected[lang]

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   @throws(classOf[java.lang.Throwable])

9.  final def getClass(): Class[_ <: AnyRef]

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   @native()

10.  def hash(value: Any, dataType: DataType, seed: Long): Long

    Computes hash of a given value of type dataType.

    Computes hash of a given value of type dataType. The caller needs to check the validity of input value.

    Definition Classes

    HiveHashFunction → InterpretedHashFunction

11.  def hashCalendarInterval(calendarInterval: CalendarInterval): Long

    Hive allows input intervals to be defined using units below but the intervals have to be from the same category: - year, month (stored as HiveIntervalYearMonth) - day, hour, minute, second, nanosecond (stored as HiveIntervalDayTime)

    Hive allows input intervals to be defined using units below but the intervals have to be from the same category: - year, month (stored as HiveIntervalYearMonth) - day, hour, minute, second, nanosecond (stored as HiveIntervalDayTime)

    e.g. (INTERVAL '30' YEAR + INTERVAL '-23' DAY) fails in Hive

    This method mimics HiveIntervalDayTime.hashCode() in Hive.

    Two differences wrt Hive due to how intervals are stored in Spark vs Hive:

    - If the INTERVAL is backed as HiveIntervalYearMonth in Hive, then this method will not produce Hive compatible result. The reason being Spark's representation of calendar does not have such categories based on the interval and is unified.

    - Spark's CalendarInterval has precision upto microseconds but Hive's HiveIntervalDayTime can store data with precision upto nanoseconds. So, any input intervals with nanosecond values will lead to wrong output hashes (i.e. non adherent with Hive output)

12.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

13.  def hashInt(i: Int, seed: Long): Long

    Attributes

    protected

    Definition Classes

    HiveHashFunction → InterpretedHashFunction

14.  def hashLong(l: Long, seed: Long): Long

    Attributes

    protected

    Definition Classes

    HiveHashFunction → InterpretedHashFunction

15.  def hashTimestamp(timestamp: Long): Long

    Mimics TimestampWritable.hashCode() in Hive

16.  def hashUnsafeBytes(base: AnyRef, offset: Long, len: Int, seed: Long): Long

    Attributes

    protected

    Definition Classes

    HiveHashFunction → InterpretedHashFunction

17.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

18.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

19.  def normalizeDecimal(input: BigDecimal): BigDecimal
20.  final def notify(): Unit

    Definition Classes

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    @native()

21.  final def notifyAll(): Unit

    Definition Classes

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    @native()

22.  final def synchronized[T0](arg0: => T0): T0

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23.  def toString(): String

    Definition Classes

    AnyRef → Any

24.  final def wait(): Unit

    Definition Classes

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    Annotations

    @throws(classOf[java.lang.InterruptedException])

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

    Definition Classes

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    Annotations

    @throws(classOf[java.lang.InterruptedException])

26.  final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws(classOf[java.lang.InterruptedException]) @native()