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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. val aggregations: Seq[Aggregation]

   

5. final def asInstanceOf[T0]: T0

   

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6. def clone(): AnyRef

   

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   protected[java.lang]

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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 finalize(): Unit

   

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   protected[java.lang]

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

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

    

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11. def hashCode(): Int

    

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12. def hopsAggregate(minQueryTs: Long, resolution: Resolution): RDD[(KeyWithHash, OutputArrayType)]

    

13. val inputDf: DataFrame

    

14. final def isInstanceOf[T0]: Boolean

    

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15. val keyColumns: Seq[String]

    

16. val keySchema: StructType

    

17. val mutationDf: DataFrame

    

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

    

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19. final def notify(): Unit

    

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20. final def notifyAll(): Unit

    

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21. lazy val postAggSchema: StructType

    

22. val preAggSchema: StructType

    

23. val selectedSchema: Array[(String, DataType)]

    

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    protected

24. def snapshotEntities: DataFrame

    

25. def snapshotEntitiesBase: RDD[(Array[Any], Array[Any])]

    

26. def snapshotEvents(partitionRange: PartitionRange): DataFrame

    

27. def snapshotEventsBase(partitionRange: PartitionRange, resolution: Resolution = DailyResolution): RDD[(Array[Any], Array[Any])]

    

28. implicit val sparkSession: SparkSession

    

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

    

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30. def temporalEntities(queriesUnfilteredDf: DataFrame, resolution: Resolution = FiveMinuteResolution): DataFrame

    

    Support for entities with mutations.

    Support for entities with mutations. Three way join between: Queries: grouped by key and dsOf[ts] Snapshot[InputDf]: Grouped by key and ds providing a FinalBatchIR to be extended. Mutations[MutationDf]: Grouped by key and dsOf[MutationTs] providing an array of updates/deletes to be done With this process the components (end of day batchIr + day's mutations + day's queries -> output)

31. def temporalEvents(queriesUnfilteredDf: DataFrame, queryTimeRange: Option[TimeRange] = None, resolution: Resolution = FiveMinuteResolution): DataFrame

    

32. def toDf(aggregateRdd: RDD[(Array[Any], Array[Any])], additionalFields: Seq[(String, DataType)]): DataFrame

    

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    protected[ai.chronon.spark]

33. def toString(): String

    

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34. val tsIndex: Int

    

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    protected[ai.chronon.spark]

35. final def wait(): Unit

    

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

    

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

    

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38. lazy val windowAggregator: RowAggregator

    

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    protected[ai.chronon.spark]