Hi,
I have a Spark job and a dataset of 0.5 Million items. Each item performs
some sort of computation (joining a shared external dataset, if that does
matter) and produces an RDD containing 20-500 result items. Now I would like
to combine all these RDDs and perform a next job. What I have found out is
that the computation itself is quite fast, but combining these RDDs takes
much longer time.
val result = data // 0.5M data items
.map(compute(_)) // Produces an RDD - fast
.reduce(_ ++ _) // Combining RDDs - slow
I have also tried to collect results from compute(_) and use a flatMap, but
that is also slow.
Is there a way to efficiently do this? I'm thinking about writing this
result to HDFS and reading from disk for the next job, but am not sure if
that's a preferred way in Spark.
Thank you.
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