Class

com.tresata.spark.skewjoin

BlockJoinOperations

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class BlockJoinOperations[K, V] extends Serializable

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Instance Constructors

  1. new BlockJoinOperations(rdd: RDD[(K, V)])(implicit arg0: ClassTag[K], arg1: ClassTag[V])

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

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  2. final def ##(): Int

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  4. final def asInstanceOf[T0]: T0

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  5. def blockJoin[W](other: RDD[(K, W)], leftReplication: Int, rightReplication: Int): RDD[(K, (V, W))]

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    Same as join, but uses a block join, otherwise known as a replicate fragment join.

    Same as join, but uses a block join, otherwise known as a replicate fragment join. This is useful in cases where the data has extreme skew. The input params leftReplication and rightReplication control the replication of the left (this rdd) and right (other rdd) respectively.

  6. def blockJoin[W](other: RDD[(K, W)], leftReplication: Int, rightReplication: Int, partitioner: Partitioner): RDD[(K, (V, W))]

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    Same as join, but uses a block join, otherwise known as a replicate fragment join.

    Same as join, but uses a block join, otherwise known as a replicate fragment join. This is useful in cases where the data has extreme skew. The input params leftReplication and rightReplication control the replication of the left (this rdd) and right (other rdd) respectively.

  7. def blockLeftOuterJoin[W](other: RDD[(K, W)], rightReplication: Int): RDD[(K, (V, Option[W]))]

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    Same as leftOuterJoin, but uses a block join, otherwise known as a replicate fragment join.

    Same as leftOuterJoin, but uses a block join, otherwise known as a replicate fragment join. This is useful in cases where the data has extreme skew. The input param rightReplication controls the replication of the right (other rdd).

  8. def blockLeftOuterJoin[W](other: RDD[(K, W)], rightReplication: Int, partitioner: Partitioner): RDD[(K, (V, Option[W]))]

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    Same as leftOuterJoin, but uses a block join, otherwise known as a replicate fragment join.

    Same as leftOuterJoin, but uses a block join, otherwise known as a replicate fragment join. This is useful in cases where the data has extreme skew. The input param rightReplication controls the replication of the right (other rdd).

  9. def blockRightOuterJoin[W](other: RDD[(K, W)], leftReplication: Int): RDD[(K, (Option[V], W))]

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    Same as rightOuterJoin, but uses a block join, otherwise known as a replicate fragment join.

    Same as rightOuterJoin, but uses a block join, otherwise known as a replicate fragment join. This is useful in cases where the data has extreme skew. The input param leftReplication controls the replication of the left (this rdd).

  10. def blockRightOuterJoin[W](other: RDD[(K, W)], leftReplication: Int, partitioner: Partitioner): RDD[(K, (Option[V], W))]

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    Same as rightOuterJoin, but uses a block join, otherwise known as a replicate fragment join.

    Same as rightOuterJoin, but uses a block join, otherwise known as a replicate fragment join. This is useful in cases where the data has extreme skew. The input param leftReplication controls the replication of the left (this rdd).

  11. def clone(): AnyRef

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

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  14. def finalize(): Unit

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

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  16. def hashCode(): Int

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  17. final def isInstanceOf[T0]: Boolean

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

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