IntervalTreeJoinImpl

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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. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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

   

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7. def equals(arg0: Any): Boolean

   

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8. def finalize(): Unit

   

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

   

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10. def hashCode(): Int

    

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11. final def isInstanceOf[T0]: Boolean

    

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

    

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13. final def notify(): Unit

    

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14. final def notifyAll(): Unit

    

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15. def overlapJoin(sc: SparkContext, rdd1: RDD[(Interval[Int], InternalRow)], rdd2: RDD[(Interval[Int], InternalRow)]): RDD[(InternalRow, Iterable[InternalRow])]

    

    Multi-joins together two RDDs that contain objects that map to reference regions.

    Multi-joins together two RDDs that contain objects that map to reference regions. The elements from the first RDD become the key of the output RDD, and the value contains all elements from the second RDD which overlap the region of the key. This is a multi-join, so it preserves n-to-m relationships between regions.

    sc

    A spark context from the cluster that will perform the join

    rdd1

    RDD of values on which we build an interval tree. Assume |rdd1| < |rdd2|

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

    

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17. def toString(): String

    

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18. final def wait(): Unit

    

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

    

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

    

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