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Spark RDDs with shared pointers within each partition (and the magic number 200??)


For anybody stumbling upon this in the future, I eventually came up with a super hacky solution (though I'd still be happy to hear a better one). Instead of using rdd.cache(), I define:

def cached[T: ClassTag](rdd:RDD[T]) = {
    rdd.mapPartitions(p => 
        Iterator(p.toSeq)
    ).cache().mapPartitions(p =>
        p.next().toIterator
    )
}

so that cached(rdd) returns an RDD that is generated from a 'cached' List


Categories : Scala

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