Have you tried simply making a list with your tables in it, then using
SparkContext.makeRDD(Seq)? ie
val tablenames = List("table1", "table2", "table3", ...)
val tablesRDD = sc.makeRDD(tablenames, nParallelTasks)
tablesRDD.foreach(....)
> Am 17.07.2017 um 14:12 schrieb FN <nuson.fr...@gmail.com>:
>
> Hi
> I am currently trying to parallelize reading multiple tables from Hive . As
> part of an archival framework, i need to convert few hundred tables which
> are in txt format to Parquet. For now i am calling a Spark SQL inside a for
> loop for conversion. But this execute sequential and entire process takes
> longer time to finish.
>
> I tired submitting 4 different Spark jobs ( each having set of tables to be
> converted), it did give me some parallelism , but i would like to do this in
> single Spark job due to few limitation of our cluster and process
>
> Any help will be greatly appreciated
>
>
>
>
>
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