com.hindog.spark.rdd

PairRDDFunctions

class PairRDDFunctions[K, V] extends Serializable

Merge-join operators that provide scalable equivalents to the existing Spark RDD join, leftOuterJoin, rightOuterJoin, fullOuterJoin operators.

Refer to the documentation for MergeJoin for implementation details.

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Instance Constructors

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

Value Members

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

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

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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  11. def fullOuterMergeJoin[W](other: RDD[(K, W)])(implicit arg0: ClassTag[W], ord: Ordering[K]): RDD[(K, (Option[V], Option[W]))]

    Perform a full outer join of this and other.

    Perform a full outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements in this have key k. Uses the default Partitioner to partition the output RDD.

    During the join, values for any given (k) that is present in the right side will accumulate into memory and spilled to disk, if necessary, so they may be iterated across for every value of (k) in this.

    For performance reasons, the side of the join that has the largest number of values per unique key grouping, on average, should be this and joined against other so that the likelihood of a spill occurring with other will be reduced.

  12. def fullOuterMergeJoin[W](other: RDD[(K, W)], partitions: Int)(implicit arg0: ClassTag[W], ord: Ordering[K]): RDD[(K, (Option[V], Option[W]))]

    Perform a full outer join of this and other.

    Perform a full outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements in this have key k. Uses the org.apache.spark.HashPartitioner to partition the results.

    During the join, values for any given (k) that is present in the right side will accumulate into memory and spilled to disk, if necessary, so they may be iterated across for every value of (k) in this.

    For performance reasons, the side of the join that has the largest number of values per unique key grouping, on average, should be this and joined against other so that the likelihood of a spill occurring with other will be reduced.

  13. def fullOuterMergeJoin[W](other: RDD[(K, W)], partitioner: Partitioner)(implicit arg0: ClassTag[W], ord: Ordering[K]): RDD[(K, (Option[V], Option[W]))]

    Perform a full outer join of this and other.

    Perform a full outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements in this have key k. Uses the given Partitioner to partition the output RDD.

    During the join, values for any given (k) that is present in the right side will accumulate into memory and spilled to disk, if necessary, so they may be iterated across for every value of (k) in this.

    For performance reasons, the side of the join that has the largest number of values per unique key grouping, on average, should be this and joined against other so that the likelihood of a spill occurring with other will be reduced.

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

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  15. def hashCode(): Int

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  16. final def isInstanceOf[T0]: Boolean

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  17. def leftOuterMergeJoin[W](other: RDD[(K, W)])(implicit arg0: ClassTag[W], ord: Ordering[K]): RDD[(K, (V, Option[W]))]

    Perform a left outer join of this and other.

    Perform a left outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (v, Some(w))) for w in other, or the pair (k, (v, None)) if no elements in other have key k. Uses the default Partitioner to partition the results.

    During the join, values for any given (k) that is present in the right side will accumulate into memory and (if necessary) spilled to disk so they may be iterated across for every value of (k) in this. There is no accumulation of values for this, only other.

    For performance reasons, consider using other.rightOuterMergeJoin(this) if other is the larger of the two RDDs being joined.

  18. def leftOuterMergeJoin[W](other: RDD[(K, W)], partitions: Int)(implicit arg0: ClassTag[W], ord: Ordering[K]): RDD[(K, (V, Option[W]))]

    Perform a left outer join of this and other.

    Perform a left outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (v, Some(w))) for w in other, or the pair (k, (v, None)) if no elements in other have key k. Uses org.apache.spark.HashPartitioner to partition the results into {partitions} partitions.

    During the join, values for any given (k) that is present in the right side will accumulate into memory and (if necessary) spilled to disk so they may be iterated across for every value of (k) in this. There is no accumulation of values for this, only other.

    For performance reasons, consider using other.rightOuterMergeJoin(this) if other is the larger of the two RDDs being joined.

  19. def leftOuterMergeJoin[W](other: RDD[(K, W)], partitioner: Partitioner)(implicit arg0: ClassTag[W], ord: Ordering[K]): RDD[(K, (V, Option[W]))]

    Perform a left outer join of this and other.

    Perform a left outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (v, Some(w))) for w in other, or the pair (k, (v, None)) if no elements in other have key k. Uses the given Partitioner to partition the output RDD.

    During the join, values for any given (k) that is present in the right side will accumulate into memory and (if necessary) spilled to disk so they may be iterated across for every value of (k) in this. There is no accumulation of values for this, only other.

    For performance reasons, consider using other.rightOuterMergeJoin(this) if other is the larger of the two RDDs being joined.

  20. def mergeJoin[W](other: RDD[(K, W)])(implicit arg0: ClassTag[W], ord: Ordering[K]): RDD[(K, (V, W))]

    Return an RDD containing all pairs of elements with matching keys in this and other.

    Return an RDD containing all pairs of elements with matching keys in this and other. Each pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in this and (k, v2) is in other. Uses the default Partitioner to partition the results.

    During the join, values for any given (k) that is present in the right side will accumulate into memory and (if necessary) spilled to disk so they may be iterated across for every value of (k) in this. There is no accumulation of values for this, only other.

    For performance reasons, the side of the join that has the largest number of values per unique key grouping, on average, should be this and joined against other so that the likelihood of a spill occurring with other will be reduced.

  21. def mergeJoin[W](other: RDD[(K, W)], partitions: Int)(implicit arg0: ClassTag[W], ord: Ordering[K]): RDD[(K, (V, W))]

    Return an RDD containing all pairs of elements with matching keys in this and other.

    Return an RDD containing all pairs of elements with matching keys in this and other. Each pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in this and (k, v2) is in other. Uses org.apache.spark.HashPartitioner to partition the results into {partitions} partitions.

    During the join, values for any given (k) that is present in the right side will accumulate into memory and (if necessary) spilled to disk so they may be iterated across for every value of (k) in this. There is no accumulation of values for this, only other.

    For performance reasons, the side of the join that has the largest number of values per unique key grouping, on average, should be this and joined against other so that the likelihood of a spill occurring with other will be reduced.

  22. def mergeJoin[W](other: RDD[(K, W)], partitioner: Partitioner)(implicit arg0: ClassTag[W], ord: Ordering[K]): RDD[(K, (V, W))]

    Return an RDD containing all pairs of elements with matching keys in this and other.

    Return an RDD containing all pairs of elements with matching keys in this and other. Each pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in this and (k, v2) is in other. Uses the given Partitioner to partition the output RDD.

    During the join, values for any given (k) that is present in the right side will accumulate into memory and spilled to disk, if necessary, so they may be iterated across for every value of (k) in this.

    For performance reasons, the side of the join that has the largest number of values per unique key grouping, on average, should be this and joined against other so that the likelihood of a spill occurring with other will be reduced.

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

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  24. final def notify(): Unit

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  25. final def notifyAll(): Unit

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  26. def rightOuterMergeJoin[W](other: RDD[(K, W)])(implicit arg0: ClassTag[W], ord: Ordering[K]): RDD[(K, (Option[V], W))]

    Perform a right outer join of this and other.

    Perform a right outer join of this and other. For each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), w)) for v in this, or the pair (k, (None, w)) if no elements in this have key k. Uses the default Partitioner to partition the results.

    During the join, values for any given (k) that is present in the right side will accumulate into memory and (if necessary) spilled to disk so they may be iterated across for every value of (k) in this. There is no accumulation of values for this, only other.

    For performance reasons, consider using other.leftOuterMergeJoin(this) if other is the larger of the two RDDs being joined.

  27. def rightOuterMergeJoin[W](other: RDD[(K, W)], partitions: Int)(implicit arg0: ClassTag[W], ord: Ordering[K]): RDD[(K, (Option[V], W))]

    Perform a right outer join of this and other.

    Perform a right outer join of this and other. For each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), w)) for v in this, or the pair (k, (None, w)) if no elements in this have key k. Uses the org.apache.spark.HashPartitioner to partition the results.

    During the join, values for any given (k) that is present in the right side will accumulate into memory and (if necessary) spilled to disk so they may be iterated across for every value of (k) in this. There is no accumulation of values for this, only other.

    For performance reasons, consider using other.leftOuterMergeJoin(this) if other is the larger of the two RDDs being joined.

  28. def rightOuterMergeJoin[W](other: RDD[(K, W)], partitioner: Partitioner)(implicit arg0: ClassTag[W], ord: Ordering[K]): RDD[(K, (Option[V], W))]

    Perform a right outer join of this and other.

    Perform a right outer join of this and other. For each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), w)) for v in this, or the pair (k, (None, w)) if no elements in this have key k. Uses the given Partitioner to partition the output RDD.

    During the join, values for any given (k) that is present in the right side will accumulate into memory and (if necessary) spilled to disk so they may be iterated across for every value of (k) in this. There is no accumulation of values for this, only other.

    For performance reasons, consider using other.leftOuterMergeJoin(this) if other is the larger of the two RDDs being joined.

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

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  30. def toString(): String

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  31. final def wait(): Unit

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

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

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