shark.execution

RDDUtils

object RDDUtils

A set of RDD-related functions that provide some handy features in addition to Spark's built-in abstractions.

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  17. def partitionTopK[K <: Comparable[K], V](rdd: RDD[(K, V)], k: Int)(implicit arg0: ClassTag[K], arg1: ClassTag[V]): RDD[(K, V)]

    Take top K on each partition and return a new RDD.

  18. def repartition[K, V](rdd: RDD[(K, V)], part: Partitioner)(implicit arg0: ClassTag[K], arg1: ClassTag[V]): RDD[(K, V)]

    Repartition an RDD using the given partitioner.

    Repartition an RDD using the given partitioner. This is similar to Spark's partitionBy, except we use the Shark shuffle serializer.

  19. def sortByKey[K <: Comparable[K], V](rdd: RDD[(K, V)])(implicit arg0: ClassTag[K], arg1: ClassTag[V]): RDD[(K, V)]

    Sort the RDD by key.

    Sort the RDD by key. This is similar to Spark's sortByKey, except that we use the Shark shuffle serializer.

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  22. def topK[K <: Comparable[K], V](rdd: RDD[(K, V)], k: Int)(implicit arg0: ClassTag[K], arg1: ClassTag[V]): RDD[(K, V)]

    Return an RDD containing the top K (K smallest key) from the given RDD.

  23. def unionAndFlatten[T](rdd: RDD[T], otherRdd: RDD[T])(implicit arg0: ClassTag[T]): UnionRDD[T]

    Returns a UnionRDD using both RDD arguments.

    Returns a UnionRDD using both RDD arguments. Any UnionRDD argument is "flattened", in that its parent sequence of RDDs is directly passed to the UnionRDD returned.

  24. def unpersistRDD(rdd: RDD[_]): RDD[_]

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