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 > > > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/Reading-Hive-tables-Parallel-in-Spark-tp28869.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe e-mail: user-unsubscr...@spark.apache.org > --------------------------------------------------------------------- To unsubscribe e-mail: user-unsubscr...@spark.apache.org